Transcriptomics technologies

126: 1278: 2375: 1673: 1012:(UMIs) are short random sequences that are used to individually tag sequence fragments during library preparation so that every tagged fragment is unique. UMIs provide an absolute scale for quantification, the opportunity to correct for subsequent amplification bias introduced during library construction, and accurately estimate the initial sample size. UMIs are particularly well-suited to single-cell RNA-Seq transcriptomics, where the amount of input RNA is restricted and extended amplification of the sample is required. 727: 1284:. Microarrays and RNA-seq rely on image analysis in different ways. In a microarray chip, each spot on a chip is a defined oligonucleotide probe, and fluorescence intensity directly detects the abundance of a specific sequence (Affymetrix). In a high-throughput sequencing flow cell, spots are sequenced one nucleotide at a time, with the colour at each round indicating the next nucleotide in the sequence (Illumina Hiseq). Other variations of these techniques use more or fewer colour channels. 907:, or identify which genes are active at a particular point in time, and read counts can be used to accurately model the relative gene expression level. RNA-Seq methodology has constantly improved, primarily through the development of DNA sequencing technologies to increase throughput, accuracy, and read length. Since the first descriptions in 2006 and 2008, RNA-Seq has been rapidly adopted and overtook microarrays as the dominant transcriptomics technique in 2015. 843: 900:)—a key advantage over microarray transcriptomes. In addition, input RNA amounts are much lower for RNA-Seq (nanogram quantity) compared to microarrays (microgram quantity), which allow examination of the transcriptome even at a single-cell resolution when combined with amplification of cDNA. Theoretically, there is no upper limit of quantification in RNA-Seq, and background noise is very low for 100 bp reads in non-repetitive regions. 599: 1410:, informed by canonical splice site sequences and known intron splice site information. Identification of intron splice junctions prevents reads from being misaligned across splice junctions or erroneously discarded, allowing more reads to be aligned to the reference genome and improving the accuracy of gene expression estimates. Since 2281:

Contains manual curations of public transcriptome datasets, focusing on medical and plant biology data. Individual experiments are normalised across the full database to allow comparison of gene expression across diverse experiments. Full functionality requires licence purchase, with free access to a

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include sufficient speed to permit billions of short sequences to be aligned in a meaningful timeframe, flexibility to recognise and deal with intron splicing of eukaryotic mRNA, and correct assignment of reads that map to multiple locations. Software advances have greatly addressed these issues, and

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and therefore requires a tailored sequence yield for an effective transcriptome. Early studies determined suitable thresholds empirically, but as the technology matured suitable coverage was predicted computationally by transcriptome saturation. Somewhat counter-intuitively, the most effective way to

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Within the organisms, genes are transcribed and spliced (in eukaryotes) to produce mature mRNA transcripts (red). The mRNA is extracted from the organism and reverse transcriptase is used to copy the mRNA into stable ds-cDNA (blue). In microarrays, the ds-cDNA is fragmented and fluorescently labelled

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file. Gene and exon read counts may be calculated quite easily using HTSeq, for example. Quantitation at the transcript level is more complicated and requires probabilistic methods to estimate transcript isoform abundance from short read information; for example, using cufflinks software. Reads that

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Each column contains the measurements for gene expression change for a single sample. Relative gene expression is indicated by colour: high-expression (red), median-expression (white) and low-expression (blue). Genes and samples with similar expression profiles can be automatically grouped (left and

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The first steps of RNA-seq also include similar image processing; however, conversion of images to sequence data is typically handled automatically by the instrument software. The Illumina sequencing-by-synthesis method results in an array of clusters distributed over the surface of a flow cell. The

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Some quantification methods can circumvent the need for an exact alignment of a read to a reference sequence altogether. The kallisto software method combines pseudoalignment and quantification into a single step that runs 2 orders of magnitude faster than contemporary methods such as those used by

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to record all transcripts. As the technology improved, the volume of data produced by each transcriptome experiment increased. As a result, data analysis methods have steadily been adapted to more accurately and efficiently analyse increasingly large volumes of data. Transcriptome databases getting

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Currently RNA-Seq relies on copying RNA molecules into cDNA molecules prior to sequencing; therefore, the subsequent platforms are the same for transcriptomic and genomic data. Consequently, the development of DNA sequencing technologies has been a defining feature of RNA-Seq. Direct sequencing of

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Once the transcript molecules have been prepared they can be sequenced in just one direction (single-end) or both directions (paired-end). A single-end sequence is usually quicker to produce, cheaper than paired-end sequencing and sufficient for quantification of gene expression levels. Paired-end

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Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O'Keeffe S, Haas S, Vingron M, Lehrach H, Yaspo ML (August 2008). "A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome".

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obtained from each sample. A large number of reads are needed to ensure sufficient coverage of the transcriptome, enabling detection of low abundance transcripts. Experimental design is further complicated by sequencing technologies with a limited output range, the variable efficiency of sequence

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ENCODE Project Consortium; Aldred, Shelley F.; Collins, Patrick J.; Davis, Carrie A.; Doyle, Francis; Epstein, Charles B.; Frietze, Seth; Harrow, Jennifer; Kaul, Rajinder; Khatun, Jainab; Lajoie, Bryan R.; Landt, Stephen G.; Lee, Bum-Kyu; Pauli, Florencia; Rosenbloom, Kate R.; Sabo, Peter; Safi,

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Garalde DR, Snell EA, Jachimowicz D, Sipos B, Lloyd JH, Bruce M, Pantic N, Admassu T, James P, Warland A, Jordan M, Ciccone J, Serra S, Keenan J, Martin S, McNeill L, Wallace EJ, Jayasinghe L, Wright C, Blasco J, Young S, Brocklebank D, Juul S, Clarke J, Heron AJ, Turner DJ (March 2018). "Highly

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The first attempts to study whole transcriptomes began in the early 1990s. Subsequent technological advances since the late 1990s have repeatedly transformed the field and made transcriptomics a widespread discipline in biological sciences. There are two key contemporary techniques in the field:

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Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (December 2001).

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of concatenated random transcript fragments. Transcripts were quantified by matching the fragments to known genes. A variant of SAGE using high-throughput sequencing techniques, called digital gene expression analysis, was also briefly used. However, these methods were largely overtaken by high

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Transcriptomic analysis has predominantly focused on either the host or the pathogen. Dual RNA-Seq has been applied to simultaneously profile RNA expression in both the pathogen and host throughout the infection process. This technique enables the study of the dynamic response and interspecies

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Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, Luo S, McCurdy S, Foy M, Ewan M, Roth R, George D, Eletr S, Albrecht G, Vermaas E, Williams SR, Moon K, Burcham T, Pallas M, DuBridge RB, Kirchner J, Fearon K, Mao J, Corcoran K (June 2000). "Gene expression analysis by massively

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Legend: NCBI – National Center for Biotechnology Information; EBI – European Bioinformatics Institute; DDBJ – DNA Data Bank of Japan; ENA – European Nucleotide Archive; MIAME – Minimum Information About a Microarray Experiment; MINSEQE – Minimum Information about a high-throughput nucleotide

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RNA-Seq was established in concert with the rapid development of a range of high-throughput DNA sequencing technologies. However, before the extracted RNA transcripts are sequenced, several key processing steps are performed. Methods differ in the use of transcript enrichment, fragmentation,

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might be placed for maximum discrimination. The measurement of multiple control genes along with the genes of interest produces a stable reference within a biological context. qPCR validation of RNA-Seq data has generally shown that different RNA-Seq methods are highly correlated.

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Microarrays for transcriptomics typically fall into one of two broad categories: low-density spotted arrays or high-density short probe arrays. Transcript abundance is inferred from the intensity of fluorescence derived from fluorophore-tagged transcripts that bind to the array.

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methodology with computational methods to capture and quantify transcripts present in an RNA extract. The nucleotide sequences generated are typically around 100 bp in length, but can range from 30 bp to over 10,000 bp depending on the sequencing method used. RNA-Seq leverages

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Transcriptomics has been characterised by the development of new techniques which have redefined what is possible every decade or so and rendered previous technologies obsolete. The first attempt at capturing a partial human transcriptome was published in 1991 and reported 609

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to label the test and control samples, and the ratio of fluorescence is used to calculate a relative measure of abundance. High-density arrays use a single fluorescent label, and each sample is hybridised and detected individually. High-density arrays were popularised by the

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Petryszak R, Keays M, Tang YA, Fonseca NA, Barrera E, Burdett T, Füllgrabe A, Fuentes AM, Jupp S, Koskinen S, Mannion O, Huerta L, Megy K, Snow C, Williams E, Barzine M, Hastings E, Weisser H, Wright J, Jaiswal P, Huber W, Choudhary J, Parkinson HE, Brazma A (January 2016).

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method, which permitted flexible manufacture of arrays in small or large numbers. These arrays had 100,000s of 45 to 85-mer probes and were hybridised with a one-colour labelled sample for expression analysis. Some designs incorporated up to 12 independent arrays per slide.

65:. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated. 1428:

assembly include larger computational requirements compared to a reference-based transcriptome, additional validation of gene variants or fragments, and additional annotation of assembled transcripts. The first metrics used to describe transcriptome assemblies, such as

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Sequence reads are not perfect, so the accuracy of each base in the sequence needs to be estimated for downstream analyses. Raw data is examined to ensure: quality scores for base calls are high, the GC content matches the expected distribution, short sequence motifs

237:(RT-qPCR) methods, but these methods are laborious and can only capture a tiny subsection of a transcriptome. Consequently, the manner in which a transcriptome as a whole is expressed and regulated remained unknown until higher-throughput techniques were developed. 1363:) are not over-represented, and the read duplication rate is acceptably low. Several software options exist for sequence quality analysis, including FastQC and FaQCs. Abnormalities may be removed (trimming) or tagged for special treatment during later processes. 581:

enzyme before the resultant cDNA is sequenced. Because ESTs can be collected without prior knowledge of the organism from which they come, they can be made from mixtures of organisms or environmental samples. Although higher-throughput methods are now used,

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at each probe location on the array indicates the transcript abundance for that probe sequence. Groups of probes designed to measure the same transcript (i.e., hybridizing a specific transcript in different positions) are usually referred to as "probesets".

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Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, Couger MB, Eccles D, Li B, Lieber M, MacManes MD, Ott M, Orvis J, Pochet N, Strozzi F, Weeks N, Westerman R, William T, Dewey CN, Henschel R, LeDuc RD, Friedman N, Regev A (August 2013).

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flow cell is imaged up to four times during each sequencing cycle, with tens to hundreds of cycles in total. Flow cell clusters are analogous to microarray spots and must be correctly identified during the early stages of the sequencing process. In

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Transcriptomics is most commonly applied to the mRNA content of the cell. However, the same techniques are equally applicable to non-coding RNAs (ncRNAs) that are not translated into a protein, but instead have direct functions (e.g. roles in

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Imports datasets from the Gene Expression Omnibus and accepts direct submissions. Processed data and experiment metadata is stored at ArrayExpress, while the raw sequence reads are held at the ENA. Complies with MIAME and MINSEQE standards.

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Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A (May 2011).

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assembly of reads. A human transcriptome could be accurately captured using RNA-Seq with 30 million 100 bp sequences per sample. This example would require approximately 1.8 gigabytes of disk space per sample when stored in a compressed

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Within the organisms, genes are transcribed and spliced (in eukaryotes) to produce mature mRNA transcripts (red). The mRNA is extracted from the organism, fragmented, and copied into stable ds-cDNA (blue). The ds-cDNA is sequenced using

149:. In 2008, two human transcriptomes, composed of millions of transcript-derived sequences covering 16,000 genes, were published, and by 2015 transcriptomes had been published for hundreds of individuals. Transcriptomes of different 5183:

Selzer RR, Richmond TA, Pofahl NJ, Green RD, Eis PS, Nair P, Brothman AR, Stallings RL (November 2005). "Analysis of chromosome breakpoints in neuroblastoma at sub-kilobase resolution using fine-tiling oligonucleotide array CGH".

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Alexias; Sanyal, Amartya; Shoresh, Noam; Simon, Jeremy M.; Song, Lingyun; Altshuler, Robert C.; Birney, Ewan; Brown, James B.; Cheng, Chao; Djebali, Sarah; Dong, Xianjun; Dunham, Ian; Ernst, Jason; et al. (September 2012).

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bigger and more useful as transcriptomes continue to be collected and shared by researchers. It would be almost impossible to interpret the information contained in a transcriptome without the knowledge of previous experiments.

1334:) that vary according to the experimental design and goals. The process can be broken down into four stages: quality control, alignment, quantification, and differential expression. Most popular RNA-Seq programs are run from a 1024:

information of a sequenced transcript. Without strand information, reads can be aligned to a gene locus but do not inform in which direction the gene is transcribed. Stranded-RNA-Seq is useful for deciphering transcription for

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Since mRNAs are longer than the read-lengths of typical high-throughput sequencing methods, transcripts are usually fragmented prior to sequencing. The fragmentation method is a key aspect of sequencing library construction.

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The sensitivity of an RNA-Seq experiment can be increased by enriching classes of RNA that are of interest and depleting known abundant RNAs. The mRNA molecules can be separated using oligonucleotides probes which bind their

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Kolesnikov N, Hastings E, Keays M, Melnichuk O, Tang YA, Williams E, Dylag M, Kurbatova N, Brandizi M, Burdett T, Megy K, Pilicheva E, Rustici G, Tikhonov A, Parkinson H, Petryszak R, Sarkans U, Brazma A (January 2015).

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and spectral analysis. Microarray raw image files are each about 750 MB in size, while the processed intensities are around 60 MB in size. Multiple short probes matching a single transcript can reveal details about the

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format read alignments as input. The final outputs of these analyses are gene lists with associated pair-wise tests for differential expression between treatments and the probability estimates of those differences.

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method, the intensity of emitted light determines the number of consecutive nucleotides in a homopolymer repeat. There are many variants on these methods, each with a different error profile for the resulting data.

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Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Yang JL, Ferrante TC, Terry R, Jeanty SS, Li C, Amamoto R, Peters DT, Turczyk BM, Marblestone AH, Inverso SA, Bernard A, Mali P, Rios X, Aach J, Church GM (March 2014).

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Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL (December 1996). "Expression monitoring by hybridization to high-density oligonucleotide arrays".

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Quantification of sequence alignments may be performed at the gene, exon, or transcript level. Typical outputs include a table of read counts for each feature supplied to the software; for example, for genes in a

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to a reference genome sequence to reconstruct which genome regions were being transcribed. This data can be used to annotate where expressed genes are, their relative expression levels, and any alternative splice

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Meyers BC, Vu TH, Tej SS, Ghazal H, Matvienko M, Agrawal V, Ning J, Haudenschild CD (August 2004). "Analysis of the transcriptional complexity of Arabidopsis thaliana by massively parallel signature sequencing".

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must be additionally identified and removed from the overall analysis. Fluorescence intensities directly indicate the abundance of each sequence, since the sequence of each probe on the array is already known.

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Xie Y, Wu G, Tang J, Luo R, Patterson J, Liu S, Huang W, He G, Gu S, Li S, Zhou X, Lam TW, Li Y, Xu X, Wong GK, Wang J (June 2014). "SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads".

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The NCBI Sequence Read Archive (SRA) was searched using “RNA-Seq” and one of "LS454”, “Illumina”, "ABI Solid”, "Ion Torrent”, "PacBio SMRT"” to report the number of RNA-Seq runs deposited for each platform.

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affinity methods or by depletion of ribosomal RNA using sequence-specific probes. Degraded RNA may affect downstream results; for example, mRNA enrichment from degraded samples will result in the depletion of

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Shanker S, Paulson A, Edenberg HJ, Peak A, Perera A, Alekseyev YO, Beckloff N, Bivens NJ, Donnelly R, Gillaspy AF, Grove D, Gu W, Jafari N, Kerley-Hamilton JS, Lyons RH, Tepper C, Nicolet CM (April 2015).

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Shiraki T, Kondo S, Katayama S, Waki K, Kasukawa T, Kawaji H, Kodzius R, Watahiki A, Nakamura M, Arakawa T, Fukuda S, Sasaki D, Podhajska A, Harbers M, Kawai J, Carninci P, Hayashizaki Y (December 2003).

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Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B (Nov 2021). "Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology".

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top trees). Samples may be different individuals, tissues, environments or health conditions. In this example, expression of gene set 1 is high and expression of gene set 2 is low in samples 1, 2, and 3.

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Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, Penkett CJ, Rogers J, Bähler J (June 2008). "Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution".

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Gehlenborg N, O'Donoghue SI, Baliga NS, Goesmann A, Hibbs MA, Kitano H, Kohlbacher O, Neuweger H, Schneider R, Tenenbaum D, Gavin AC (March 2010). "Visualization of omics data for systems biology".

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Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, et al. (August 2008). "A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome".

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Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA (May 2012).

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Piétu G, Mariage-Samson R, Fayein NA, Matingou C, Eveno E, Houlgatte R, Decraene C, Vandenbrouck Y, Tahi F, Devignes MD, Wirkner U, Ansorge W, Cox D, Nagase T, Nomura N, Auffray C (February 1999).

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genes. The measurement by qPCR is similar to that obtained by RNA-Seq wherein a value can be calculated for the concentration of a target region in a given sample. qPCR is, however, restricted to

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is measured by normalising, modelling, and statistically analysing the data. Most tools will read a table of genes and read counts as their input, but some programs, such as cuffdiff, will accept

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Govind G, Harshavardhan VT, ThammeGowda HV, Patricia JK, Kalaiarasi PJ, Dhanalakshmi R, Iyer DR, Senthil Kumar M, Muthappa SK, Sreenivasulu N, Nese S, Udayakumar M, Makarla UK (June 2009).

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across the array indicates the abundance of a predetermined set of sequences. These sequences are typically specifically chosen to report on genes of interest within the organism's genome.

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is available, these tags may be matched to their corresponding gene in the genome. If a reference genome is unavailable, the tags can be directly used as diagnostic markers if found to be

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Black MB, Parks BB, Pluta L, Chu TM, Allen BC, Wolfinger RD, Thomas RS (February 2014). "Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments".

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Su Z, Fang H, Hong H, Shi L, Zhang W, Zhang W, Zhang Y, Dong Z, Lancashire LJ, Bessarabova M, Yang X, Ning B, Gong B, Meehan J, Xu J, Ge W, Perkins R, Fischer M, Tong W (December 2014).

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Transcriptomics studies generate large amounts of data that have potential applications far beyond the original aims of an experiment. As such, raw or processed data may be deposited in

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to enrich for fragments that contain the expected 5’ and 3’ adapter sequences. Amplification is also used to allow sequencing of very low input amounts of RNA, down to as little as 50

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or economically important organisms. Advances in design and manufacture of arrays improved the specificity of probes and allowed more genes to be tested on a single array. Advances in

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Transcriptomics methods are highly parallel and require significant computation to produce meaningful data for both microarray and RNA-Seq experiments. Microarray data is recorded as

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specific rRNA sequences (e.g. mammal rRNA, plant rRNA). However, ribo-depletion can also introduce some bias via non-specific depletion of off-target transcripts. Small RNAs, such as

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Bainbridge MN, Warren RL, Hirst M, Romanuik T, Zeng T, Go A, Delaney A, Griffith M, Hickenbotham M, Magrini V, Mardis ER, Sadar MD, Siddiqui AS, Marra MA, Jones SJ (September 2006).

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SAGE and CAGE methods produce information on more genes than was possible when sequencing single ESTs, but sample preparation and data analysis are typically more labour-intensive.

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Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA (May 2009). "mRNA-Seq whole-transcriptome analysis of a single cell".

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All transcriptomic methods require RNA to first be isolated from the experimental organism before transcripts can be recorded. Although biological systems are incredibly diverse,

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Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH, Xiao H, et al. (June 1991). "Complementary DNA sequencing: expressed sequence tags and human genome project".

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structure, requiring statistical models to determine the authenticity of the resulting signal. RNA-Seq studies produce billions of short DNA sequences, which must be aligned to

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high-density arrays were the method of choice for transcriptional profiling until the late 2000s. Over this period, a range of microarrays were produced to cover known genes in

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were first published in 1995. Microarray technology allowed the assay of thousands of transcripts simultaneously and at a greatly reduced cost per gene and labour saving. Both

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Hobbs M, Pavasovic A, King AG, Prentis PJ, Eldridge MD, Chen Z, Colgan DJ, Polkinghorne A, Wilkins MR, Flanagan C, Gillett A, Hanger J, Johnson RN, Timms P (September 2014).

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Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, Lönnerberg P, Linnarsson S (February 2014). "Quantitative single-cell RNA-seq with unique molecular identifiers".

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with the phenotype. Integration of RNA-Seq datasets across different tissues has been used to improve annotation of gene functions in commercially important organisms (e.g.

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The quest for transcriptome data at the level of individual cells has driven advances in RNA-Seq library preparation methods, resulting in dramatic advances in sensitivity.

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Kivioja T, Vähärautio A, Karlsson K, Bonke M, Enge M, Linnarsson S, Taipale J (November 2011). "Counting absolute numbers of molecules using unique molecular identifiers".

1265:. Processed count data for each gene would be much smaller, equivalent to processed microarray intensities. Sequence data may be stored in public repositories, such as the 2553:

Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ (December 2008). "Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing".

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The non-targeted nature of transcriptomics allows the identification of novel transcriptional networks in complex systems. For example, comparative analysis of a range of

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RNA-Seq experiments generate a large volume of raw sequence reads which have to be processed to yield useful information. Data analysis usually requires a combination of

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increases in sequencing read length reduce the chance of ambiguous read alignments. A list of currently available high-throughput sequence aligners is maintained by the

1433:, have been shown to be misleading and improved evaluation methods are now available. Annotation-based metrics are better assessments of assembly completeness, such as 6208:

Loman NJ, Misra RV, Dallman TJ, Constantinidou C, Gharbia SE, Wain J, Pallen MJ (May 2012). "Performance comparison of benchtop high-throughput sequencing platforms".

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Rich SM, Leendertz FH, Xu G, LeBreton M, Djoko CF, Aminake MN, Takang EE, Diffo JL, Pike BL, Rosenthal BM, Formenty P, Boesch C, Ayala FJ, Wolfe ND (September 2009).

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and so is ideal for gene expression studies of non-model organisms with non-existing or poorly developed genomic resources. For example, a database of SNPs used in

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Functional validation of key genes is an important consideration for post transcriptome planning. Observed gene expression patterns may be functionally linked to a

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align equally well to multiple locations must be identified and either removed, aligned to one of the possible locations, or aligned to the most probable location.

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Chomczynski P, Sacchi N (2006). "The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on".

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are now routinely generated. This explosion in transcriptomics has been driven by the rapid development of new technologies with improved sensitivity and economy.

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genes. Transcriptome analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human

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Nakamura K, Oshima T, Morimoto T, Ikeda S, Yoshikawa H, Shiwa Y, Ishikawa S, Linak MC, Hirai A, Takahashi H, Altaf-Ul-Amin M, Ogasawara N, Kanaya S (July 2011).

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Has a graphical user interface, can combine diverse sequencing technologies, has no transcriptome-specific features, and a licence must be purchased before use.

1901:. RNA-Seq can provide information about the transcription of endogenous retrotransposons that may influence the transcription of neighboring genes by various 1424:

assembly can be used to align reads to one another to construct full-length transcript sequences without use of a reference genome. Challenges particular to

454:. Transcript abundance is derived from the number of counts from each transcript. The technique has therefore been heavily influenced by the development of 9061:"Identification and functional validation of a unique set of drought induced genes preferentially expressed in response to gradual water stress in peanut" 6395:

Ozsolak F, Platt AR, Jones DR, Reifenberger JG, Sass LE, McInerney P, Thompson JF, Bowers J, Jarosz M, Milos PM (October 2009). "Direct RNA sequencing".

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Romanov V, Davidoff SN, Miles AR, Grainger DW, Gale BK, Brooks BD (March 2014). "A critical comparison of protein microarray fabrication technologies".

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in both interaction partners from initial contact through to invasion and the final persistence of the pathogen or clearance by the host immune system.

10075:"De novo transcriptome assembly for the lobster Homarus americanus and characterization of differential gene expression across nervous system tissues" 2174:

to ensure their utility for the broader scientific community. For example, as of 2018, the Gene Expression Omnibus contained millions of experiments.

1806:(qPCR), which is recognisable and statistically assessable. Gene expression is measured against defined standards both for the gene of interest and 6503:

Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcześniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A (January 2016).

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Schena M, Shalon D, Davis RW, Brown PO (October 1995). "Quantitative monitoring of gene expression patterns with a complementary DNA microarray".

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Putney SD, Herlihy WC, Schimmel P (April 1983). "A new troponin T and cDNA clones for 13 different muscle proteins, found by shotgun sequencing".

660:(SAGE) was a development of EST methodology to increase the throughput of the tags generated and allow some quantitation of transcript abundance. 3370:"The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics" 2257:

Tissue-specific gene expression database for animals and plants. Displays secondary analyses and visualisation, such as functional enrichment of

234: 1212:(ENCODE) Project are for 70-fold exome coverage for standard RNA-Seq and up to 500-fold exome coverage to detect rare transcripts and isoforms. 3728:

Stears RL, Getts RC, Gullans SR (August 2000). "A novel, sensitive detection system for high-density microarrays using dendrimer technology".

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sequences, which are absent from mature mRNA. Short read aligners perform an additional round of alignments specifically designed to identify

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Robertson G, Schein J, Chiu R, Corbett R, Field M, Jackman SD, et al. (November 2010). "De novo assembly and analysis of RNA-seq data".

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Grillo M, Margolis FL (September 1990). "Use of reverse transcriptase polymerase chain reaction to monitor expression of intronless genes".

3321:"Absolute mRNA quantification using the polymerase chain reaction (PCR). A novel approach by a PCR aided transcript titration assay (PATTY)" 884:

of the transcriptome with many short fragments from a transcriptome to allow computational reconstruction of the original RNA transcript by

9600:"Transcriptome analyses reveal genotype- and developmental stage-specific molecular responses to drought and salinity stresses in chickpea" 5014:

Auburn RP, Kreil DP, Meadows LA, Fischer B, Matilla SS, Russell S (July 2005). "Robotic spotting of cDNA and oligonucleotide microarrays".

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Chomczynski P, Sacchi N (April 1987). "Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction".

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The word "transcriptome" was first used in the 1990s. In 1995, one of the earliest sequencing-based transcriptomic methods was developed,

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Lahens NF, Kavakli IH, Zhang R, Hayer K, Black MB, Dueck H, Pizarro A, Kim J, Irizarry R, Thomas RS, Grant GR, Hogenesch JB (June 2014).

1998: 1327: 537: 113:. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted 10875: 9973:"A transcriptome resource for the koala (Phascolarctos cinereus): insights into koala retrovirus transcription and sequence diversity" 9208:

Khurana E, Fu Y, Chakravarty D, Demichelis F, Rubin MA, Gerstein M (February 2016). "Role of non-coding sequence variants in cancer".

3881:"A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium" 2386: 480:. This was sufficient coverage to quantify relative transcript abundance. RNA-Seq began to increase in popularity after 2008 when new 7765:"Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation" 1418:

level, splice-aware alignments also permit detection of isoform abundance changes that would otherwise be lost in a bulked analysis.

6084:"A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers" 5973:

Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, Mildner A, Cohen N, Jung S, Tanay A, Amit I (February 2014).

5641:"ClickSeq: Fragmentation-Free Next-Generation Sequencing via Click Ligation of Adaptors to Stochastically Terminated 3'-Azido cDNAs" 4961:

Barbulovic-Nad I, Lucente M, Sun Y, Zhang M, Wheeler AR, Bussmann M (2006-10-01). "Bio-microarray fabrication techniques—a review".

3979:

Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J (May 2005). "Independence and reproducibility across microarray platforms".

4376:

Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (July 2008). "Mapping and quantifying mammalian transcriptomes by RNA-Seq".

3262:"Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes" 6352:

Loman NJ, Quick J, Simpson JT (August 2015). "A complete bacterial genome assembled de novo using only nanopore sequencing data".

4858:"Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage" 4735:

Close TJ, Wanamaker SI, Caldo RA, Turner SM, Ashlock DA, Dickerson JA, Wing RA, Muehlbauer GJ, Kleinhofs A, Wise RP (March 2004).

1819: 641:(at location ‘X’ and ‘X’+11) to produce 11-nucleotide "tag" fragments. These tags are concatenated and sequenced using long-read 459: 1256:

but modern algorithms mean consumer computing hardware is sufficient for simple transcriptomics experiments that do not require

2099: 1441:, the assembly can be used as a reference for subsequent sequence alignment methods and quantitative gene expression analysis. 561:

of tissue prior to RNA isolation is typical, and care is taken to reduce exposure to RNase enzymes once isolation is complete.

416:, were developed in the mid-1990s and 2000s. Microarrays that measure the abundances of a defined set of transcripts via their 7609:"Transcriptome Sequence Reveals Candidate Genes Involving in the Post-Harvest Hardening of Trifoliate Yam Dioscorea dumetorum" 9495: 7297: 4651: 4218: 1567:

Can process repetitive sequences, combine different sequencing formats, and a wide range of sequence platforms are accepted.

496:

Generating data on RNA transcripts can be achieved via either of two main principles: sequencing of individual transcripts (

9108:

Tavassoly, Iman; Goldfarb, Joseph; Iyengar, Ravi (2018-10-04). "Systems biology primer: the basic methods and approaches".

1331: 7010:"De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis" 5748:"Evaluation of commercially available RNA amplification kits for RNA sequencing using very low input amounts of total RNA" 676:

head-to-tail into long strands (>500 bp) and sequenced using low-throughput, but long read-length methods such as

10749: 3775: 1803: 1383: 1225: 421: 9765:"Drug resistance. Population transcriptomics of human malaria parasites reveals the mechanism of artemisinin resistance" 8899:"Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes" 3417:

Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE, Hieter P, Vogelstein B, Kinzler KW (January 1997).

1818:. If validation of transcript isoforms is required, an inspection of RNA-Seq read alignments should indicate where qPCR 61:

perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a

5227:

Svensson V, Vento-Tormo R, Teichmann SA (April 2018). "Exponential scaling of single-cell RNA-seq in the past decade".

1021: 805:

arrayed on the surface of a glass slide. These probes are longer than those of high-density arrays and cannot identify

707:

of genes can be identified when the tags are aligned to a reference genome. Identifying gene start sites is of use for

657: 604: 246: 428: 8218:"Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs" 6980: 6955: 2249: 2047:

The use of transcriptomics is also important to investigate responses in the marine environment. In marine ecology, "

1851:

Transcriptomic strategies have seen broad application across diverse areas of biomedical research, including disease

1388: 5582:"Systematic comparison of three methods for fragmentation of long-range PCR products for next generation sequencing" 10745: 8473:

Kovaka, Sam; Zimin, Aleksey V.; Pertea, Geo M.; Razaghi, Roham; Salzberg, Steven L.; Pertea, Mihaela (2019-07-08).

1430: 254:

throughput sequencing of entire transcripts, which provided additional information on transcript structure such as

7763:

Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (May 2010).

3225:

Marra MA, Hillier L, Waterston RH (January 1998). "Expressed sequence tags—ESTablishing bridges between genomes".

1004:

of known RNAs can be used for quality control assessment to check library preparation and sequencing, in terms of

9598:

Garg R, Shankar R, Thakkar B, Kudapa H, Krishnamurthy L, Mantri N, Varshney RK, Bhatia S, Jain M (January 2016).

4677:"Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling" 680:. The sequences are then divided back into their original 11 bp tags using computer software in a process called 17: 3032:

Morozova O, Hirst M, Marra MA (2009). "Applications of new sequencing technologies for transcriptome analysis".

997: 9657:"Candida albicans biofilms: a developmental state associated with specific and stable gene expression patterns" 3069:"Use of a cDNA library for studies on evolution and developmental expression of the chorion multigene families" 1880: 1376: 1242: 889: 672:

that cut DNA at a specific sequence, and 11 base pairs along from that sequence. These cDNA tags are then

357: 1016:

sequencing produces more robust alignments/assemblies, which is beneficial for gene annotation and transcript

704: 10527:"Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants" 9251:

Slotkin RK, Martienssen R (April 2007). "Transposable elements and the epigenetic regulation of the genome".

1938: 1876: 1411: 1199:

creation, and variable sequence quality. Added to those considerations is that every species has a different

544:

to digest any traces of DNA. It is necessary to enrich messenger RNA as total RNA extracts are typically 98%

98: 2162:). Many of these ncRNAs affect disease states, including cancer, cardiovascular, and neurological diseases. 5051:"A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization" 2075:, especially for non-model species, and can lead to vague conclusions on the effects of responses studied. 1209: 1128: 1009: 696: 673: 10574:

Hruz T, Laule O, Szabo G, Wessendorp F, Bleuler S, Oertle L, Widmayer P, Gruissem W, Zimmermann P (2008).

1875:

are important in human disease and, therefore, defining such variants is crucial to the interpretation of

1371:

In order to link sequence read abundance to the expression of a particular gene, transcript sequences are

6033:

Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA, Friedman N, Gnirke A, Regev A (September 2010).

5373:"An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era" 2650:

Lappalainen T, Sammeth M, Friedländer MR, 't Hoen PA, Monlong J, Rivas MA, et al. (September 2013).

2230: 1860: 982: 911: 774:

Microarrays require some genomic knowledge from the organism of interest, for example, in the form of an

368: 7607:

Siadjeu, Christian; Mayland-Quellhorst, Eike; Pande, Shruti; Laubinger, Sascha; Albach, Dirk C. (2021).

7370:

Van Verk MC, Hickman R, Pieterse CM, Van Wees SC (April 2013). "RNA-Seq: revelation of the messengers".

6082:

Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, Bertoni A, Swerdlow HP, Gu Y (July 2012).

4327:"Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach" 2071:

have been underrepresented. One issue still is a deficiency in functional genetic studies, which hamper

2033: 476:. The earliest RNA-Seq work was published in 2006 with one hundred thousand transcripts sequenced using 10890: 8648:

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (August 2009).

3473:

Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (October 1995). "Serial analysis of gene expression".

2401: 2159: 2055:" have been among the most common research topics, especially related to anthropogenic stress, such as 876: 855: 501: 467: 455: 417: 303: 202: 78: 10830: 2218:

community standards that define necessary experiment metadata to ensure effective interpretation and

2029: 1981:

lines at different developmental stages identified distinct transcriptional profiles associated with

1706: 1530: 993: 689: 630: 440: 363:

Specialised arrays can detect mRNA splice variants (limited by probe design and cross-hybridisation)

297: 94: 6938: 6829:"edgeR: a Bioconductor package for differential expression analysis of digital gene expression data" 4975: 916: 823: 573:(EST) is a short nucleotide sequence generated from a single RNA transcript. RNA is first copied as 132:

Published papers referring to RNA-Seq (black), RNA microarray (red), expressed sequence tag (blue),

2390: 1934: 1343: 485: 339: 125: 50: 9879: 5795:

Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, Gingeras TR, Oliver B (September 2011).

3930:"Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data" 1663:

Legend: RAM – random access memory; MPI – message passing interface; EST – expressed sequence tag.

10024:"A SNP resource for Douglas-fir: de novo transcriptome assembly and SNP detection and validation" 8602: 5975:"Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types" 2197: 1954: 1084: 738:(orange). The labelled fragments bind to an ordered array of complementary oligonucleotides, and 650: 610: 481: 218: 206: 10373:"Minimum information about a microarray experiment (MIAME)-toward standards for microarray data" 4115: 3832:"RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays" 477: 10674:

Zhao Y, Li H, Fang S, Kang Y, Wu W, Hao Y, Li Z, Bu D, Sun N, Zhang MQ, Chen R (January 2016).

9396: 8950:"Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters" 8897:

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (June 2002).

8424:"RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome" 8265:

Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, et al. (September 2005).

6933: 6879:

Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. (February 2015).

4970: 2290: 2147: 1689: 1584:

Specialised to accommodate the homo-polymer sequencing errors typical of Roche 454 sequencers.

1335: 1183:

represents a current state-of-the-art RNA-Seq technique. Nanopore sequencing of RNA can detect

762:", typically arranged on a glass slide. Transcript abundance is determined by hybridisation of 570: 497: 210: 194: 6666:

Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS, Hitz BC, et al. (January 2016).

941:. Alternatively, ribo-depletion can be used to specifically remove abundant but uninformative 798: 8840:"An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data" 6557:

Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D (2013).

2298:

Human, mouse, and rat transcriptomes from 40 different organs. Gene expression visualised as

2134: 2122: 2028:, a virulent parasite responsible for malaria in humans, identified that upregulation of the 2024: 1946: 1898: 1266: 978: 626: 578: 533: 230: 186: 10576:"Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes" 10425:"Minimum Information About a Microarray Experiment (MIAME)--successes, failures, challenges" 7424: 6559:"Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data" 3576:

Mantione KJ, Kream RM, Kuzelova H, Ptacek R, Raboch J, Samuel JM, Stefano GB (August 2014).

1929:

has become an established method for quantifying gene expression changes, identifying novel

1512:

An early example of a short read assembler. It has been updated for transcriptome assembly.

933:

amplification, single or paired-end sequencing, and whether to preserve strand information.

558: 10787: 10221: 9776: 9717: 9611: 9450: 8961: 8851: 8278: 8216:

Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Müller WE, Wetter T, Suhai S (June 2004).

6622: 6404: 5986: 5701: 5593: 5436: 5327: 5283: 4924: 4869: 4806: 4481: 4429: 4183: 4069: 3638: 3482: 3273: 3176: 3121: 2723: 2663: 2608: 2511: 2303: 1990: 1909:

is expanding rapidly due to the ability to dissect immune cell populations and to sequence

1894: 1868: 1710: 1415: 1106: 921: 830: 806: 472: 255: 178: 173:

were being performed several decades before any transcriptomics approaches were available.

133: 4142: 2710:

Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, et al. (May 2015).

892:). Both low-abundance and high-abundance RNAs can be quantified in an RNA-Seq experiment ( 528:, disruption of macromolecules and nucleotide complexes, separation of RNA from undesired 8: 7258: 7223: 6777:"limma powers differential expression analyses for RNA-sequencing and microarray studies" 2015:

revealed a co-regulated set of genes critical for biofilm establishment and maintenance.

1872: 1864: 1205: 1180: 1150: 1029:

in different directions and to make more robust gene predictions in non-model organisms.

954: 881: 708: 463: 10791: 10225: 10137: 10022:

Howe GT, Yu J, Knaus B, Cronn R, Kolpak S, Dolan P, Lorenz WW, Dean JF (February 2013).

9780: 9721: 9615: 9454: 8965: 8855: 8375:"SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing" 8282: 6626: 6408: 6277: 6252: 5990: 5705: 5597: 5440: 5331: 5287: 4928: 4873: 4810: 4485: 4433: 4073: 3741: 3642: 3486: 3277: 3180: 3125: 3045: 2727: 2667: 2612: 2515: 10810: 10775: 10700: 10675: 10651: 10626: 10602: 10575: 10551: 10526: 10501: 10476: 10451: 10424: 10402: 10325: 10247: 10185: 10160: 10101: 10074: 10050: 10023: 9999: 9972: 9948: 9921: 9797: 9764: 9740: 9705: 9632: 9599: 9572: 9545: 9526: 9471: 9438: 9372: 9347: 9320: 9295: 9276: 9233: 9185: 9160: 9141: 9085: 9060: 9033: 9006: 8982: 8949: 8874: 8839: 8774: 8749: 8725: 8698: 8674: 8649: 8630: 8578: 8553: 8534: 8450: 8423: 8399: 8374: 8350: 8323: 8299: 8266: 8193: 8168: 8145: 8087: 8062: 8038: 8013: 7989: 7962: 7938: 7913: 7889: 7862: 7838: 7813: 7789: 7764: 7737: 7712: 7635: 7608: 7589: 7563: 7527: 7502: 7473: 7446: 7405: 7347: 7322: 7303: 7262: 7227: 7163:"Gene Expression Omnibus: NCBI gene expression and hybridization array data repository" 7135: 7110: 7086: 7061: 7034: 7009: 6905: 6880: 6853: 6828: 6801: 6776: 6692: 6667: 6643: 6610: 6585: 6558: 6531: 6504: 6475: 6450: 6428: 6377: 6334: 6290: 6233: 6164: 6137: 6110: 6083: 6059: 6034: 6007: 5974: 5955: 5912: 5869: 5821: 5796: 5772: 5747: 5722: 5689: 5665: 5640: 5616: 5581: 5554: 5527: 5508: 5457: 5424: 5399: 5372: 5348: 5315: 5252: 5209: 5116: 4996: 4829: 4794: 4703: 4676: 4589: 4505: 4453: 4401: 4353: 4326: 4304: 4260: 4093: 4004: 3956: 3929: 3905: 3880: 3856: 3831: 3661: 3626: 3602: 3577: 3506: 3448: 3200: 3004: 2979: 2950: 2925: 2796: 2744: 2711: 2684: 2651: 2632: 2578: 2535: 2446: 2433: 2396: 1966: 1807: 1395: 1372: 1297: 1253: 1174:

Legend: NCBI SRA – National center for biotechnology information sequence read archive.

897: 885: 859: 669: 638: 484:

allowed one billion transcript sequences to be recorded. This yield now allows for the

170: 9829:"The Next Generation Is Here: A Review of Transcriptomic Approaches in Marine Ecology" 9681: 9656: 8925: 8898: 8242: 8217: 8063:"Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels" 5160: 5135: 4892: 4857: 4761: 4736: 3627:"Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells" 3435: 3418: 3394: 3369: 3345: 3320: 3296: 3261: 3238: 3144: 3109: 3067:

Sim GK, Kafatos FC, Jones CW, Koehler MD, Efstratiadis A, Maniatis T (December 1979).

2395:

Rohan Lowe; Neil Shirley; Mark Bleackley; Stephen Dolan; Thomas Shafee (18 May 2017).

1802:

Transcriptomic analyses may be validated using an independent technique, for example,

1277: 10826: 10815: 10705: 10656: 10607: 10556: 10506: 10456: 10394: 10317: 10282: 10239: 10190: 10141: 10106: 10055: 10004: 9953: 9902: 9898: 9860: 9802: 9745: 9686: 9637: 9577: 9530: 9518: 9476: 9419: 9377: 9325: 9268: 9225: 9190: 9133: 9125: 9090: 9038: 8987: 8930: 8915: 8879: 8820: 8779: 8730: 8679: 8634: 8622: 8583: 8538: 8526: 8455: 8404: 8355: 8304: 8247: 8198: 8137: 8092: 8043: 7994: 7961:

Li B, Fillmore N, Bai Y, Collins M, Thomson JA, Stewart R, Dewey CN (December 2014).

7943: 7894: 7843: 7794: 7742: 7693: 7640: 7581: 7532: 7478: 7409: 7397: 7352: 7293: 7247:

The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology

7192: 7187: 7162: 7140: 7091: 7039: 6986: 6976: 6951: 6910: 6858: 6806: 6754: 6697: 6648: 6590: 6536: 6480: 6420: 6369: 6326: 6282: 6225: 6169: 6115: 6064: 6012: 5947: 5904: 5861: 5826: 5777: 5727: 5670: 5621: 5559: 5500: 5462: 5404: 5353: 5244: 5201: 5165: 5108: 5072: 5031: 4988: 4940: 4897: 4834: 4766: 4708: 4657: 4647: 4616: 4581: 4543: 4539: 4497: 4457: 4445: 4405: 4393: 4358: 4296: 4252: 4214: 4187: 4147: 4085: 4042: 3996: 3961: 3910: 3861: 3812: 3745: 3710: 3666: 3607: 3547: 3498: 3440: 3399: 3350: 3301: 3242: 3192: 3149: 3090: 3085: 3068: 3049: 3009: 2955: 2883: 2837: 2788: 2749: 2689: 2624: 2570: 2527: 2438: 2420: 2130: 1942: 1930: 1550:

Suited to short reads. It can handle complex transcriptomes but is memory intensive.

1310: 1062: 1008:, fragment length, as well as the bias due to fragment position within a transcript. 962: 802: 782: 712: 677: 661: 642: 634: 574: 451: 250: 226: 198: 190: 182: 10329: 10265:

Hüttenhofer A, Schattner P, Polacek N (May 2005). "Non-coding RNAs: hope or hype?".

10251: 9348:"Translating RNA sequencing into clinical diagnostics: opportunities and challenges" 9237: 9161:"RNA-Seq and human complex diseases: recent accomplishments and future perspectives" 9145: 8665: 8569: 8078: 7728: 7688: 7671: 7577: 7501:

Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L (January 2013).

7447:"Rapid evaluation and quality control of next generation sequencing data with FaQCs" 7266: 7231: 6844: 6381: 5916: 5873: 5213: 5120: 5000: 4593: 4509: 4308: 4264: 4008: 3510: 3452: 2800: 2636: 2539: 2374: 2037: 1902: 1204:

improve detection of differential expression in low expression genes is to add more

985:. Alternatively, fragmentation and cDNA tagging may be done simultaneously by using 586:

commonly provided sequence information for early microarray designs; for example, a

10805: 10795: 10695: 10687: 10646: 10638: 10597: 10587: 10546: 10538: 10496: 10488: 10446: 10436: 10406: 10384: 10309: 10274: 10229: 10180: 10172: 10133: 10096: 10086: 10045: 10035: 9994: 9984: 9943: 9933: 9894: 9850: 9840: 9792: 9784: 9735: 9725: 9676: 9668: 9655:

García-Sánchez S, Aubert S, Iraqui I, Janbon G, Ghigo JM, d'Enfert C (April 2004).

9627: 9619: 9567: 9557: 9510: 9466: 9458: 9411: 9367: 9359: 9315: 9307: 9280: 9260: 9217: 9180: 9172: 9117: 9080: 9072: 9028: 9018: 8977: 8969: 8920: 8910: 8869: 8859: 8810: 8769: 8761: 8720: 8710: 8669: 8661: 8614: 8573: 8565: 8518: 8482: 8474: 8445: 8435: 8394: 8386: 8345: 8335: 8294: 8286: 8237: 8229: 8188: 8180: 8149: 8127: 8119: 8082: 8074: 8033: 8025: 7984: 7974: 7933: 7925: 7884: 7874: 7833: 7825: 7784: 7776: 7732: 7724: 7683: 7630: 7620: 7593: 7573: 7522: 7514: 7468: 7458: 7387: 7379: 7342: 7334: 7307: 7285: 7254: 7219: 7210:

Petrov A, Shams S (2004-11-01). "Microarray Image Processing and Quality Control".

7182: 7174: 7130: 7122: 7081: 7073: 7060:

Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL (March 2015).

7029: 7021: 6943: 6900: 6892: 6848: 6840: 6796: 6788: 6746: 6687: 6679: 6638: 6630: 6580: 6570: 6526: 6516: 6470: 6462: 6432: 6412: 6361: 6338: 6318: 6294: 6272: 6264: 6237: 6217: 6159: 6149: 6105: 6095: 6054: 6046: 6002: 5994: 5959: 5939: 5896: 5853: 5816: 5808: 5767: 5759: 5717: 5709: 5660: 5652: 5611: 5601: 5549: 5539: 5512: 5492: 5452: 5444: 5394: 5384: 5343: 5335: 5291: 5256: 5236: 5193: 5155: 5147: 5100: 5062: 5023: 4980: 4932: 4887: 4877: 4824: 4814: 4756: 4748: 4698: 4688: 4639: 4573: 4535: 4489: 4437: 4385: 4348: 4338: 4288: 4244: 4179: 4170:

Heller MJ (2002). "DNA microarray technology: devices, systems, and applications".

4137: 4127: 4097: 4077: 4034: 3988: 3951: 3941: 3900: 3892: 3851: 3843: 3804: 3737: 3700: 3656: 3646: 3597: 3589: 3578:"Comparing bioinformatic gene expression profiling methods: microarray and RNA-Seq" 3537: 3490: 3430: 3389: 3381: 3340: 3332: 3291: 3281: 3234: 3204: 3184: 3139: 3129: 3080: 3041: 2999: 2991: 2945: 2937: 2875: 2827: 2780: 2739: 2731: 2679: 2671: 2616: 2582: 2562: 2519: 2454: 2450: 2428: 2410: 2244: 2118: 2095: 2048: 2011: 1835: 1289: 1238: 1221: 1195: 1026: 685: 525: 154: 90: 10161:"Functional requirement of noncoding Y RNAs for human chromosomal DNA replication" 7914:"TransRate: reference-free quality assessment of de novo transcriptome assemblies" 7383: 5314:

Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M (June 2008).

5296: 5271: 5027: 4025:

Nelson NJ (April 2001). "Microarrays have arrived: gene expression tool matures".

1814:

smaller than 300 bp, usually toward the 3’ end of the coding region, avoiding the

992:

During preparation for sequencing, cDNA copies of transcripts may be amplified by

443:

increased the sensitivity and measurement accuracy for low abundance transcripts.

10848: 10838: 10800: 10625:

Mitsuhashi N, Fujieda K, Tamura T, Kawamoto S, Takagi T, Okubo K (January 2009).

9346:

Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW (May 2016).

9023: 9007:"Molecular mechanisms of ethanol-induced pathogenesis revealed by RNA-sequencing" 8864: 8750:"Ballgown bridges the gap between transcriptome assembly and expression analysis" 8699:"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" 8169:"Full-length transcriptome assembly from RNA-Seq data without a reference genome" 7062:"StringTie enables improved reconstruction of a transcriptome from RNA-seq reads" 5606: 4819: 4081: 3705: 3688: 3651: 3494: 2832: 2815: 2415: 2171: 2151: 2107: 2084: 2072: 2032:

and slower progression through the early stages of the asexual intraerythrocytic

2019: 1978: 1914: 1910: 1906: 1890: 1672: 1407: 1301: 1200: 1188: 1017: 970: 938: 759: 755: 645:(different shades of blue indicate tags from different genes). The sequences are 587: 549: 517: 424: 394: 332: 214: 46: 10210:"The snoRNA HBII-52 regulates alternative splicing of the serotonin receptor 2C" 9763:

Mok S, Ashley EA, Ferreira PE, Zhu L, Lin Z, Yeo T, et al. (January 2015).

9672: 7829: 7503:"Differential analysis of gene regulation at transcript resolution with RNA-seq" 7212:

Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology

2087:

and identifying those responsible for particular phenotypes. Transcriptomics of

10885: 10676:"NONCODE 2016: an informative and valuable data source of long non-coding RNAs" 9710:

Proceedings of the National Academy of Sciences of the United States of America

9415: 6932:. Statistics for Biology and Health. Springer, New York, NY. pp. 397–420. 5134:

Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (February 2003).

4862:

Proceedings of the National Academy of Sciences of the United States of America

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Proceedings of the National Academy of Sciences of the United States of America

3114:

Proceedings of the National Academy of Sciences of the United States of America

2879: 2381: 2270: 2041: 1974: 1905:

that lead to disease. Similarly, the potential for using RNA-Seq to understand

1831: 1314: 1249: 775: 732: 513: 436: 409: 158: 106: 101:

and reveals details of an organism's biology. It can also be used to infer the

62: 58: 57:

serves as a transient intermediary molecule in the information network, whilst

10843: 10278: 10091: 9845: 9828: 9076: 8748:

Frazee AC, Pertea G, Jaffe AE, Langmead B, Salzberg SL, Leek JT (March 2015).

8715: 7979: 7863:"Assessing De Novo transcriptome assembly metrics for consistency and utility" 7463: 7289: 6521: 6035:"Comprehensive comparative analysis of strand-specific RNA sequencing methods" 5656: 5389: 4984: 920:

RNA-Seq where transcriptomes of individual cells are directly interrogated in

649:

to find the frequency of each tag. The tag frequency can be used to report on

10859: 10040: 9989: 9938: 9864: 9562: 9129: 8440: 8340: 7879: 7245:

Petrov A, Shams S (2004). "Microarray Image Processing and Quality Control".

6990: 6947: 6575: 6100: 5544: 5316:"The transcriptional landscape of the yeast genome defined by RNA sequencing" 4693: 3336: 2652:"Transcriptome and genome sequencing uncovers functional variation in humans" 2424: 2258: 2219: 2056: 1970: 942: 893: 681: 646: 622: 545: 381: 142: 34: 10234: 10209: 9922:"RNA-Seq improves annotation of protein-coding genes in the cucumber genome" 9788: 9730: 8973: 5998: 5763: 5690:"The impact of amplification on differential expression analyses by RNA-seq" 5448: 5339: 4882: 4493: 4343: 4038: 3946: 3808: 3286: 3134: 2735: 2620: 2523: 2476:"Medline trend: automated yearly statistics of PubMed results for any query" 2018:

Transcriptomic profiling also provides crucial information on mechanisms of

1635:

Can estimate frequency of alternatively spliced transcripts. User friendly.

726: 328:

None required, although a reference genome/transcriptome sequence is useful

213:

came to prominence during the 1990s as an efficient method to determine the

10819: 10709: 10660: 10611: 10560: 10510: 10460: 10398: 10321: 10286: 10243: 10194: 10110: 10059: 10008: 9957: 9906: 9806: 9749: 9690: 9641: 9581: 9522: 9480: 9423: 9381: 9329: 9272: 9229: 9194: 9137: 9094: 9042: 8991: 8934: 8883: 8824: 8783: 8734: 8683: 8626: 8587: 8530: 8459: 8408: 8359: 8308: 8251: 8202: 8141: 8096: 8047: 8014:"Velvet: algorithms for de novo short read assembly using de Bruijn graphs" 7998: 7947: 7898: 7847: 7798: 7746: 7697: 7644: 7625: 7585: 7536: 7482: 7401: 7356: 7196: 7178: 7144: 7095: 7043: 7025: 6930:

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

6914: 6862: 6810: 6758: 6701: 6652: 6607: 6594: 6540: 6484: 6424: 6373: 6330: 6286: 6229: 6173: 6119: 6068: 6016: 5951: 5908: 5865: 5830: 5781: 5731: 5674: 5625: 5563: 5504: 5466: 5408: 5357: 5248: 5240: 5205: 5169: 5035: 4992: 4944: 4901: 4838: 4770: 4712: 4585: 4501: 4449: 4397: 4362: 4300: 4256: 4191: 4151: 4046: 4000: 3965: 3914: 3865: 3816: 3749: 3714: 3670: 3611: 3403: 3053: 3013: 2959: 2887: 2841: 2814:

Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA (May 2015).

2792: 2753: 2693: 2628: 2574: 2475: 2442: 2355: 2155: 2068: 1347: 1293: 1262: 1194:

The sensitivity and accuracy of an RNA-Seq experiment are dependent on the

1001: 763: 614: 583: 470:, and was used in 2004 to validate the expression of ten thousand genes in 376:

1 transcript per thousand (approximate, limited by fluorescence detection)

174: 10691: 10592: 10542: 10492: 10145: 9176: 8475:"Transcriptome assembly from long-read RNA-seq alignments with StringTie2" 8390: 8029: 7929: 6750: 6683: 6466: 6154: 5812: 5112: 5076: 4752: 4661: 4620: 4577: 4547: 4132: 4089: 3847: 3593: 3551: 3502: 3444: 3354: 3246: 3196: 3153: 2531: 2458: 2210:

First transcriptomics database to accept data from any source. Introduced

10642: 10176: 9546:"A review on computational systems biology of pathogen-host interactions" 8815: 8798: 7338: 7126: 6792: 6775:

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (April 2015).

5151: 5104: 3305: 3094: 2769:"Entering the era of single-cell transcriptomics in biology and medicine" 2103: 1884: 1269:(SRA). RNA-Seq datasets can be uploaded via the Gene Expression Omnibus. 1208:

rather than adding more reads. The current benchmarks recommended by the

986: 810: 767: 739: 593: 529: 146: 10441: 9855: 9514: 9363: 9345: 9221: 9121: 8290: 7658: 6634: 6416: 6268: 4725:

Some examples of environmental samples include: sea water, soil, or air.

4441: 3542: 3525: 2712:"Human genomics. The human transcriptome across tissues and individuals" 2675: 2649: 1897:

which proliferate within eukaryotic genomes through a process involving

1859:. RNA-Seq approaches have allowed for the large-scale identification of 1296:

of features within an image and independently quantify the fluorescence

1187:

that would be otherwise masked when sequencing cDNA and also eliminates

136:(green), and serial/cap analysis of gene expression (yellow) since 1990. 10389: 10372: 9462: 8554:"HTSeq—a Python framework to work with high-throughput sequencing data" 8522: 8233: 8123: 7912:

Smith-Unna R, Boursnell C, Patro R, Hibberd JM, Kelly S (August 2016).

7606: 7392: 6896: 6365: 6322: 6050: 5943: 5900: 5857: 5067: 5050: 4936: 4389: 4206: 3385: 2784: 2350: 2265:

domains, or pathways. Links to protein abundance data where available.

2052: 1680:

identification of gene co-expression patterns across different samples.

1005: 974: 966: 815: 751: 700: 504:

of transcripts to an ordered array of nucleotide probes (microarrays).

432: 284: 70: 41:. The information content of an organism is recorded in the DNA of its 9623: 9311: 9058: 8508: 8267:"Genome sequencing in microfabricated high-density picolitre reactors" 8132: 5713: 5197: 4737:"A new resource for cereal genomics: 22K barley GeneChip comes of age" 10761:

One picolitre is about 30 million times smaller than a drop of water.

8765: 8618: 8184: 7780: 7518: 7077: 6449:

Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher JP (December 2013).

6253:"Coming of age: ten years of next-generation sequencing technologies" 6221: 4675:

Zhao W, He X, Hoadley KA, Parker JS, Hayes DN, Perou CM (June 2014).

3992: 3896: 3830:

Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (September 2008).

3188: 2866:

McGettigan PA (February 2013). "Transcriptomics in the RNA-seq era".

2060: 1994: 1887:, which contributes to the understanding of disease causal variants. 1852: 1827: 1815: 1399: 950: 842: 618: 554: 373:

1 transcript per million (approximate, limited by sequence coverage)

189:

in the late 1970s. In the 1980s, low-throughput sequencing using the

102: 10343: 10313: 9264: 9005:

Camarena L, Bruno V, Euskirchen G, Poggio S, Snyder M (April 2010).

5496: 2995: 2941: 1382:

to one another if no reference is available. The key challenges for

8487: 6502: 6187: 5483:

Shendure J, Ji H (October 2008). "Next-generation DNA sequencing".

4292: 3928:

Chen JJ, Hsueh HM, Delongchamp RR, Lin CJ, Tsai CA (October 2007).

2768: 2566: 2360: 2345: 2262: 1986: 1926: 1811: 1184: 819: 7963:"Evaluation of de novo transcriptome assemblies from RNA-Seq data" 7568: 7425:"FastQC: A Quality Control tool for High Throughput Sequence Data" 6881:"Orchestrating high-throughput genomic analysis with Bioconductor" 6136:

Liu L, Li Y, Li S, Hu N, He Y, Pong R, Lin D, Lu L, Law M (2012).

5972: 4248: 3689:"CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification" 598: 9439:"Prediction of antibiotic resistance by gene expression profiles" 6928:

Smyth, G. K. (2005). "Limma: Linear Models for Microarray Data".

5688:

Parekh S, Ziegenhain C, Vieth B, Enard W, Hellmann I (May 2016).

3367: 2311: 2299: 2215: 2091: 2002: 1982: 1856: 1677: 1529:

Suited to short reads, can handle complex transcriptomes, and an

1495:

The original short read assembler. It is now largely superseded.

872: 848: 413: 150: 110: 74: 10736:

is a phenomenon in which single-stranded deoxyribonucleic acid (

10473: 10369: 10300:

Esteller M (November 2011). "Non-coding RNAs in human disease".

9654: 8896: 8215: 7500: 7111:"The Sequence Read Archive: explosive growth of sequencing data" 6611:"An integrated encyclopedia of DNA elements in the human genome" 3472: 699:(CAGE) method is a variant of SAGE that sequences tags from the 625:

transcripts (red). The mRNA is extracted from the organism, and

590:

microarray was designed from 350,000 previously sequenced ESTs.

10624: 7911: 7762: 7369: 7006: 6715: 6207: 4375: 4116:"Oligonucleotide microarrays: widely applied—poorly understood" 2813: 2083:

All transcriptomic techniques have been particularly useful in

2064: 2006: 1434: 1403: 1360: 1230: 904: 785:

of ESTs that can be used to generate the probes for the array.

778: 222: 42: 10881:

Knowledge articles published in peer-reviewed literature (J2W)

10774:

Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T (May 2017).

9207: 9159:

Costa V, Aprile M, Esposito R, Ciccodicola A (February 2013).

9158: 5843: 4793:

Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T (May 2017).

818:

array, where each transcript is quantified by several short 25

10159:

Christov CP, Gardiner TJ, Szüts D, Krude T (September 2006).

10073:

McGrath LL, Vollmer SV, Kaluziak ST, Ayers J (January 2016).

9920:

Li Z, Zhang Z, Yan P, Huang S, Fei Z, Lin K (November 2011).

9004: 7284:. Methods in Molecular Biology. Vol. 733. SpringerLink. 6394: 5580:

Knierim E, Lucke B, Schwarz JM, Schuelke M, Seelow D (2011).

4960: 4638:. Methods in Molecular Biology. Vol. 86. pp. 61–4. 4324: 3416: 2340: 2211: 2111: 1742:

Transcript analysis that tracks alternative splicing of mRNA

1252:. RNA-Seq operations are highly repetitious and benefit from 1248:

within a dataset requires the construction of highly complex

946: 858:, short-read sequencing methods. These sequences can then be 541: 521: 360:

and splice variants (limited by sequencing accuracy of ~99%)

114: 10627:"BodyParts3D: 3D structure database for anatomical concepts" 8372: 6307: 5639:

Routh A, Head SR, Ordoukhanian P, Johnson JE (August 2015).

5638: 5226: 4914: 4418: 3108:

Sutcliffe JG, Milner RJ, Bloom FE, Lerner RA (August 1982).

97:, or at different times, gives information on how genes are 10264: 10072: 9880:"Molecular mechanisms of metal hyperaccumulation in plants" 8838:

Ramsköld D, Wang ET, Burge CB, Sandberg R (December 2009).

8264: 7320: 6451:"Calculating sample size estimates for RNA sequencing data" 6032: 5579: 5421: 4634:

Bryant S, Manning DL (1998). "Isolation of messenger RNA".

4235:

parallel signature sequencing (MPSS) on microbead arrays".

4233: 3687:

Hashimshony T, Wagner F, Sher N, Yanai I (September 2012).

3575: 2379:

This article was adapted from the following source under a

1941:. A primary aim of this technology is to develop optimised 1705:

Once quantitative counts of each transcript are available,

1697:

tophat/cufflinks software, with less computational burden.

1339: 1234: 668:

but is then digested into 11 bp "tag" fragments using

629:

is used to copy the mRNA into stable double-stranded–cDNA (

462:(MPSS) was an early example based on generating 16–20 86: 54: 10871:

Knowledge articles published in PLOS Computational Biology

10158: 8600: 8109: 8060: 7670:

Fonseca NA, Rung J, Brazma A, Marioni JC (December 2012).

7059: 6309:

parallel direct RNA sequencing on an array of nanopores".

5794: 5744: 5687: 5089: 3776:"RNA-Seq Data Comparison with Gene Expression Microarrays" 3686: 3107: 2816:"The technology and biology of single-cell RNA sequencing" 2709: 225:. Amounts of individual transcripts were quantified using 193:

method was used to sequence random transcripts, producing

10773: 10741: 10737: 10523: 9970: 8837: 8747: 8647: 8324:"Comparing de novo assemblers for 454 transcriptome data" 8061:

Schulz MH, Zerbino DR, Vingron M, Birney E (April 2012).

7814:"Assembly algorithms for next-generation sequencing data" 6878: 5886: 5182: 5133: 5013: 4792: 4734: 2501: 1034:

Sequencing technology platforms commonly used for RNA-Seq

829:

NimbleGen arrays were a high-density array produced by a

665: 331:

Reference genome/transcriptome is required for design of

261: 38: 10866:

Knowledge articles published in peer-reviewed literature

10573: 9597: 9436: 9107: 8472: 8165: 7669: 7323:"Sequence-specific error profile of Illumina sequencers" 6665: 6556: 6250: 5929: 5313: 4854: 4470: 3978: 3878: 3829: 3624: 2980:"RNA sequencing: advances, challenges and opportunities" 2598: 1786:

Efficient and sensitive transcript discovery, flexible.

1020:

discovery. Strand-specific RNA-Seq methods preserve the

557:

and an uneven signal across the length of a transcript.

9703: 9397:"Discovery of virulence factors of pathogenic bacteria" 7710: 6448: 5525: 5425:"Highly multiplexed subcellular RNA sequencing in situ" 5272:"Transcriptomics today: Microarrays, RNA-seq, and more" 3927: 3625:

Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X (2014).

1775:

Microarray or RNA-Seq data, flexible experiment design

1402:

to a reference genome requires specialised handling of

73:, which quantify a set of predetermined sequences, and 9877: 9543: 8601:

Bray NL, Pimentel H, Melsted P, Pachter L (May 2016).

6735: 6505:"A survey of best practices for RNA-seq data analysis" 6081: 5797:"Synthetic spike-in standards for RNA-seq experiments" 4059: 3526:"The significance of digital gene expression profiles" 3066: 1618:

Used for transcriptomics experiments on single cells.

914:

are now well described and have even been extended to

594:

Serial and cap analysis of gene expression (SAGE/CAGE)

516:

techniques are broadly similar and involve mechanical

9493: 9296:"Single-cell technologies to study the immune system" 8799:"Design and validation issues in RNA-seq experiments" 7108: 6826: 3318: 3224: 3166: 2063:. Most of the studies in this area have been done in 348:>90% (limited by fluorescence detection accuracy) 9762: 9494:

Westermann AJ, Gorski SA, Vogel J (September 2012).

9437:

Suzuki S, Horinouchi T, Furusawa C (December 2014).

7960: 1920: 1846: 446:

RNA-Seq is accomplished by reverse transcribing RNA

10124:Noller HF (1991). "Ribosomal RNA and translation". 8603:"Near-optimal probabilistic RNA-seq quantification" 7713:"TopHat: discovering splice junctions with RNA-Seq" 7672:"Tools for mapping high-throughput sequencing data" 6827:Robinson MD, McCarthy DJ, Smyth GK (January 2010). 5136:"Summaries of Affymetrix GeneChip probe level data" 4674: 4204: 4113: 3110:"Common 82-nucleotide sequence unique to brain RNA" 2926:"RNA-Seq: a revolutionary tool for transcriptomics" 1965:Transcriptomics allows identification of genes and 945:(rRNAs) by hybridisation to probes tailored to the 10477:"ArrayExpress update—simplifying data submissions" 9250: 7160: 6774: 6138:"Comparison of next-generation sequencing systems" 3794: 3727: 3031: 2923: 2322:Non-coding RNAs (ncRNAs) excluding tRNA and rRNA. 7811: 6351: 6251:Goodwin S, McPherson JD, McCombie WR (May 2016). 4563: 4525: 4277: 4120:Briefings in Functional Genomics & Proteomics 1056:RNA-Seq runs deposited in the NCBI SRA (Oct 2016) 548:. Enrichment for transcripts can be performed by 181:mRNA transcripts were collected and converted to 10857: 10021: 8947: 8650:"The Sequence Alignment/Map format and SAMtools" 7860: 7238: 7109:Kodama Y, Shumway M, Leinonen R (January 2012). 5528:"IVT-seq reveals extreme bias in RNA sequencing" 5048: 2117:Assembly of RNA-Seq reads is not dependent on a 1241:composed of millions to billions of base pairs. 540:. Isolated RNA may additionally be treated with 532:including DNA, and concentration of the RNA via 9919: 9878:Verbruggen N, Hermans C, Schat H (March 2009). 9293: 9054: 9052: 8948:Core LJ, Waterfall JJ, Lis JT (December 2008). 8696: 7711:Trapnell C, Pachter L, Salzberg SL (May 2009). 7055: 7053: 4165: 4163: 4161: 3259: 1879:. RNA-Seq can also identify disease-associated 1564:Moderate, multi-thread, medium RAM requirement 1547:Moderate, multi-thread, medium RAM requirement 1526:Moderate, multi-thread, medium RAM requirement 1509:Moderate, multi-thread, medium RAM requirement 903:RNA-Seq may be used to identify genes within a 33:are the techniques used to study an organism's 10673: 9394: 8551: 7161:Edgar R, Domrachev M, Lash AE (January 2002). 4606: 3260:Alwine JC, Kemp DJ, Stark GR (December 1977). 2552: 1652:Can use a combination of reference-guided and 888:reads to a reference genome or to each other ( 754:usually consist of a grid of short nucleotide 10418: 10416: 10365: 10363: 9593: 9591: 9544:Durmuş S, Çakır T, Özgür A, Guthke R (2015). 9395:Wu HJ, Wang AH, Jennings MP (February 2008). 9341: 9339: 8161: 8159: 8011: 7758: 7756: 7548: 7546: 7496: 7494: 7492: 7156: 7154: 5370: 4633: 4114:Pozhitkov AE, Tautz D, Noble PA (June 2007). 3523: 3419:"Characterization of the yeast transcriptome" 3319:Becker-André M, Hahlbrock K (November 1989). 2924:Wang Z, Gerstein M, Snyder M (January 2009). 2919: 2917: 1719:RNA-Seq differential gene expression software 1533:version is available for computing clusters. 797:Spotted low-density arrays typically feature 637:; blue). In SAGE, the ds-cDNA is digested by 389:1,000:1 (limited by fluorescence saturation) 10207: 9827:Page, Tessa M.; Lawley, Jonathan W. (2022). 9049: 8321: 7552: 7050: 7002: 7000: 6874: 6872: 6822: 6820: 6770: 6768: 6601: 6552: 6550: 6498: 6496: 6494: 6444: 6442: 6131: 6129: 6028: 6026: 5575: 5573: 5478: 5476: 5309: 5307: 4956: 4954: 4908: 4850: 4848: 4788: 4786: 4784: 4782: 4780: 4636:RNA Isolation and Characterization Protocols 4559: 4557: 4320: 4318: 4158: 4109: 4107: 4020: 4018: 3790: 3788: 3682: 3680: 3571: 3569: 3567: 3565: 3563: 3561: 3034:Annual Review of Genomics and Human Genetics 3027: 3025: 3023: 2977: 2973: 2971: 2969: 2915: 2913: 2911: 2909: 2907: 2905: 2903: 2901: 2899: 2897: 2497: 2495: 1989:stresses, including identifying the role of 867: 317:High (sample preparation and data analysis) 9697: 7244: 7209: 4521: 4519: 3879:SEQC/MAQC-III Consortium (September 2014). 3468: 3466: 3464: 3462: 3220: 3218: 3216: 3214: 2861: 2859: 2857: 2855: 2853: 2851: 2705: 2703: 2594: 2592: 2129:transcriptome analysis in the absence of a 2078: 1960: 1492:Low, single-threaded, high RAM requirement 703:of an mRNA transcript only. Therefore, the 10667: 10618: 10567: 10517: 10467: 10413: 10360: 10293: 10258: 10201: 10152: 10066: 10015: 9964: 9913: 9871: 9826: 9756: 9648: 9588: 9537: 9487: 9430: 9388: 9336: 9287: 9244: 9201: 9152: 8998: 8941: 8890: 8831: 8790: 8690: 8594: 8552:Anders S, Pyl PT, Huber W (January 2015). 8545: 8315: 8258: 8209: 8156: 8103: 8054: 8005: 7954: 7905: 7854: 7812:Miller JR, Koren S, Sutton G (June 2010). 7805: 7753: 7663: 7543: 7489: 7438: 7363: 7314: 7282:High-Throughput Next Generation Sequencing 7151: 7102: 6142:Journal of Biomedicine & Biotechnology 5482: 5049:Shalon D, Smith SJ, Brown PO (July 1996). 2865: 2165: 2001:. Investigation of gene expression during 1700: 1656:assembly methods to identify transcripts. 1437:reciprocal best hit count. Once assembled 766:labelled transcripts to these probes. The 746: 564: 85:Measuring the expression of an organism's 10809: 10799: 10699: 10650: 10601: 10591: 10550: 10500: 10450: 10440: 10388: 10233: 10184: 10117: 10100: 10090: 10049: 10039: 9998: 9988: 9947: 9937: 9854: 9844: 9796: 9739: 9729: 9680: 9631: 9571: 9561: 9470: 9371: 9319: 9184: 9084: 9032: 9022: 8981: 8924: 8914: 8873: 8863: 8814: 8773: 8724: 8714: 8673: 8577: 8486: 8449: 8439: 8398: 8349: 8339: 8298: 8241: 8192: 8131: 8086: 8037: 7988: 7978: 7937: 7888: 7878: 7837: 7788: 7736: 7687: 7634: 7624: 7567: 7526: 7472: 7462: 7391: 7346: 7279: 7186: 7134: 7085: 7033: 6997: 6975:. Meadows, Lisa A. Burlington: Elsevier. 6937: 6904: 6869: 6852: 6817: 6800: 6765: 6691: 6659: 6642: 6584: 6574: 6547: 6530: 6520: 6491: 6474: 6439: 6388: 6345: 6276: 6244: 6201: 6163: 6153: 6135: 6126: 6109: 6099: 6075: 6058: 6023: 6006: 5966: 5923: 5880: 5837: 5820: 5788: 5771: 5738: 5721: 5681: 5664: 5615: 5605: 5570: 5553: 5543: 5519: 5473: 5456: 5415: 5398: 5388: 5364: 5347: 5304: 5295: 5269: 5176: 5159: 5127: 5066: 5007: 4974: 4951: 4891: 4881: 4845: 4828: 4818: 4777: 4760: 4728: 4702: 4692: 4668: 4554: 4412: 4369: 4352: 4342: 4315: 4271: 4227: 4211:Analyzing Microarray Gene Expression Data 4141: 4131: 4104: 4015: 3972: 3955: 3945: 3921: 3904: 3872: 3855: 3823: 3785: 3721: 3704: 3677: 3660: 3650: 3618: 3601: 3558: 3541: 3434: 3393: 3361: 3344: 3295: 3285: 3143: 3133: 3084: 3020: 3003: 2966: 2949: 2894: 2831: 2743: 2683: 2492: 2432: 2414: 1581:Low, single-thread, high RAM requirement 1191:steps that can otherwise introduce bias. 953:, can be purified based on their size by 809:events. Spotted arrays use two different 386:100,000:1 (limited by sequence coverage) 164: 10299: 6731: 6729: 5083: 5042: 4627: 4600: 4516: 4053: 4027:Journal of the National Cancer Institute 3773: 3517: 3459: 3410: 3312: 3211: 3160: 3101: 2848: 2807: 2766: 2760: 2700: 2643: 2589: 2546: 1883:(SNPs), allele-specific expression, and 1671: 1649:High, multi-thread, low RAM requirement 1632:High, multi-thread, low RAM requirement 1615:High, multi-thread, low RAM requirement 1598:High, multi-thread, low RAM requirement 1321: 1276: 841: 725: 597: 488:and comparison of human transcriptomes. 124: 9294:Proserpio V, Mahata B (February 2016). 8796: 8421: 7444: 7422: 4172:Annual Review of Biomedical Engineering 3253: 3060: 460:Massively parallel signature sequencing 456:high-throughput sequencing technologies 14: 10858: 10422: 10123: 9822: 9820: 9818: 9816: 6970: 6716:"ENCODE: Encyclopedia of DNA Elements" 4184:10.1146/annurev.bioeng.4.020702.153438 4169: 4024: 3769: 3767: 3765: 3763: 3761: 3759: 3582:Medical Science Monitor Basic Research 408:The dominant contemporary techniques, 262:Development of contemporary techniques 235:reverse transcriptase quantitative PCR 7659:http://www.ebi.ac.uk/~nf/hts_mappers/ 6927: 6726: 3524:Audic S, Claverie JM (October 1997). 2978:Ozsolak F, Milos PM (February 2011). 1764:Flexible data types, low replication 130:Transcriptomics method use over time. 10827:Comparative Transcriptomics Analysis 8322:Kumar S, Blaxter ML (October 2010). 2022:. Analysis of over 1000 isolates of 1332:List of RNA-Seq bioinformatics tools 740:measurement of fluorescent intensity 345:~90% (limited by sequence coverage) 201:was predominant until the advent of 10208:Kishore S, Stamm S (January 2006). 10138:10.1146/annurev.bi.60.070191.001203 9813: 9496:"Dual RNA-seq of pathogen and host" 9404:Current Opinion in Chemical Biology 8697:Love MI, Huber W, Anders S (2014). 3756: 3742:10.1152/physiolgenomics.2000.3.2.93 3046:10.1146/annurev-genom-082908-145957 2868:Current Opinion in Chemical Biology 1838:study in the organism of interest. 1282:Microarray and sequencing flow cell 1272: 653:of the gene that the tag came from. 507: 24: 10835:Software used in transcriptomics: 10767: 9165:European Journal of Human Genetics 7861:O'Neil ST, Emrich SJ (July 2013). 7259:10.1023/B:VLSI.0000042488.08307.ad 7224:10.1023/B:VLSI.0000042488.08307.ad 6668:"ENCODE data at the ENCODE portal" 5752:Journal of Biomolecular Techniques 4213:. Hoboken: John Wiley & Sons. 2085:identifying the functions of genes 1353: 658:Serial analysis of gene expression 466:sequences via a complex series of 268:Comparison of contemporary methods 247:serial analysis of gene expression 25: 10902: 10876:Externally peer reviewed articles 10831:Reference Module in Life Sciences 9706:"The origin of malignant malaria" 8012:Zerbino DR, Birney E (May 2008). 7445:Lo CC, Chain PS (November 2014). 6973:Microarray Technology in Practice 4963:Critical Reviews in Biotechnology 3781:. 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If a high-quality 621:) to produce mature 473:Arabidopsis thaliana 353:Sequence resolution 10792:2017PLSCB..13E5457L 10692:10.1093/nar/gkv1252 10593:10.1155/2008/420747 10543:10.1093/nar/gkv1045 10493:10.1093/nar/gku1057 10442:10.1100/tsw.2009.57 10226:2006Sci...311..230K 9887:The New Phytologist 9781:2015Sci...347..431M 9722:2009PNAS..10614902R 9616:2016NatSR...619228G 9515:10.1038/nrmicro2852 9455:2014NatCo...5.5792S 9364:10.1038/nrg.2016.10 9222:10.1038/nrg.2015.17 9122:10.1042/EBC20180003 8966:2008Sci...322.1845C 8909:(7): RESEARCH0034. 8856:2009PLSCB...5E0598R 8517:(3 Suppl): S56–68. 8291:10.1038/nature03959 8283:2005Natur.437..376M 6751:10.1093/bib/bbab259 6684:10.1093/nar/gkv1160 6635:10.1038/nature11247 6627:2012Natur.489...57T 6417:10.1038/nature08390 6409:2009Natur.461..814O 6269:10.1038/nrg.2016.49 6155:10.1155/2012/251364 5991:2014Sci...343..776J 5706:2016NatSR...625533P 5598:2011PLoSO...628240K 5441:2014Sci...343.1360L 5332:2008Sci...320.1344N 5288:2015Sci...349..544T 4929:2014Ana...139.1303R 4874:2003PNAS..10015776S 4811:2017PLSCB..13E5457L 4486:2008Sci...321..956S 4442:10.1038/nature07002 4434:2008Natur.453.1239W 4133:10.1093/bfgp/elm014 4074:1995Sci...270..467S 3643:2014PLoSO...978644Z 3543:10.1101/gr.7.10.986 3487:1995Sci...270..484V 3278:1977PNAS...74.5350A 3181:1983Natur.302..718P 3126:1982PNAS...79.4942S 2728:2015Sci...348..660M 2676:10.1038/nature12531 2668:2013Natur.501..506L 2613:2008Sci...321..956S 2516:1991Sci...252.1651A 2278:Microarray RNA-Seq 2254:Microarray RNA-Seq 2207:Microarray RNA-Seq 2179: 2148:protein translation 2034:developmental cycle 1991:transcript isoforms 1873:regulatory elements 1721: 1452: 1181:nanopore sequencing 1043:Commercial release 1036: 987:transposase enzymes 967:chemical hydrolysis 965:may be achieved by 955:gel electrophoresis 898:orders of magnitude 816:Affymetrix GeneChip 670:restriction enzymes 639:restriction enzymes 518:disruption of cells 356:RNA-Seq can detect 270: 209:(Solexa/Illumina). 145:sequences from the 10643:10.1093/nar/gkn613 10390:10.1038/ng1201-365 10267:Trends in Genetics 9604:Scientific Reports 9463:10.1038/ncomms6792 8816:10.1093/bib/bbr004 8523:10.1038/nmeth.1436 8428:BMC Bioinformatics 8234:10.1101/gr.1917404 8124:10.1038/nmeth.1517 7451:BMC Bioinformatics 7423:Andrews S (2010). 7339:10.1093/nar/gkr344 7127:10.1093/nar/gkr854 6897:10.1038/nmeth.3252 6793:10.1093/nar/gkv007 6366:10.1038/nmeth.3444 6323:10.1038/nmeth.4577 6051:10.1038/nmeth.1491 5944:10.1038/nmeth.2772 5901:10.1038/nmeth.1315 5858:10.1038/nmeth.1778 5694:Scientific Reports 5152:10.1093/nar/gng015 5068:10.1101/gr.6.7.639 4937:10.1039/c3an01577g 4390:10.1038/nmeth.1226 3934:BMC Bioinformatics 3386:10.1101/gr.9.2.195 3227:Trends in Genetics 2785:10.1038/nmeth.2764 2304:3D representations 2275:Privately curated 2177: 1830:by an independent 1717: 1685: 1444: 1384:alignment software 1300:for each feature. 1286: 1224:images, requiring 1032: 1027:genes that overlap 865: 744: 655: 266: 138: 10891:Molecular biology 10744:) molecules 10171:(18): 6993–7004. 9624:10.1038/srep19228 9312:10.1111/imm.12553 7299:978-1-61779-088-1 6720:encodeproject.org 5714:10.1038/srep25533 5198:10.1002/gcc.20243 4653:978-0-89603-494-5 4428:(7199): 1239–43. 4220:978-0-471-72612-8 2331: 2326: 2325: 2102:, tolerance, and 2040:in isolates from 2005:formation by the 1943:infection control 1931:virulence factors 1925:RNA-Seq of human 1867:usage, and novel 1794: 1790: 1789: 1664: 1660: 1659: 1500:SOAPdenovo-trans 1450:assembly software 1414:may occur at the 1246:assembly of reads 1239:reference genomes 1226:feature detection 1175: 1171: 1170: 1063:454 Life Sciences 1002:Spike-in controls 678:Sanger sequencing 643:Sanger sequencing 575:complementary DNA 536:from solution or 500:, or RNA-Seq) or 406: 405: 314:Labour intensity 309:High ~ 1 μg mRNA 251:Sanger sequencing 227:Northern blotting 183:complementary DNA 157:, or even single 16:(Redirected from 10898: 10823: 10813: 10803: 10762: 10759: 10753: 10730: 10714: 10713: 10703: 10671: 10665: 10664: 10654: 10622: 10616: 10615: 10605: 10595: 10571: 10565: 10564: 10554: 10521: 10515: 10514: 10504: 10471: 10465: 10464: 10454: 10444: 10420: 10411: 10410: 10392: 10367: 10358: 10357: 10355: 10354: 10340: 10334: 10333: 10297: 10291: 10290: 10262: 10256: 10255: 10237: 10205: 10199: 10198: 10188: 10156: 10150: 10149: 10121: 10115: 10114: 10104: 10094: 10070: 10064: 10063: 10053: 10043: 10019: 10013: 10012: 10002: 9992: 9968: 9962: 9961: 9951: 9941: 9917: 9911: 9910: 9884: 9875: 9869: 9868: 9858: 9848: 9824: 9811: 9810: 9800: 9760: 9754: 9753: 9743: 9733: 9701: 9695: 9694: 9684: 9652: 9646: 9645: 9635: 9595: 9586: 9585: 9575: 9565: 9541: 9535: 9534: 9500: 9491: 9485: 9484: 9474: 9434: 9428: 9427: 9401: 9392: 9386: 9385: 9375: 9343: 9334: 9333: 9323: 9291: 9285: 9284: 9248: 9242: 9241: 9205: 9199: 9198: 9188: 9156: 9150: 9149: 9105: 9099: 9098: 9088: 9056: 9047: 9046: 9036: 9026: 9002: 8996: 8995: 8985: 8960:(5909): 1845–8. 8945: 8939: 8938: 8928: 8918: 8894: 8888: 8887: 8877: 8867: 8850:(12): e1000598. 8835: 8829: 8828: 8818: 8794: 8788: 8787: 8777: 8766:10.1038/nbt.3172 8745: 8739: 8738: 8728: 8718: 8694: 8688: 8687: 8677: 8645: 8639: 8638: 8619:10.1038/nbt.3519 8598: 8592: 8591: 8581: 8549: 8543: 8542: 8506: 8500: 8499: 8497: 8495: 8490: 8470: 8464: 8463: 8453: 8443: 8419: 8413: 8412: 8402: 8370: 8364: 8363: 8353: 8343: 8319: 8313: 8312: 8302: 8277:(7057): 376–80. 8262: 8256: 8255: 8245: 8213: 8207: 8206: 8196: 8185:10.1038/nbt.1883 8163: 8154: 8153: 8135: 8107: 8101: 8100: 8090: 8058: 8052: 8051: 8041: 8009: 8003: 8002: 7992: 7982: 7958: 7952: 7951: 7941: 7909: 7903: 7902: 7892: 7882: 7858: 7852: 7851: 7841: 7809: 7803: 7802: 7792: 7781:10.1038/nbt.1621 7760: 7751: 7750: 7740: 7708: 7702: 7701: 7691: 7667: 7661: 7655: 7649: 7648: 7638: 7628: 7604: 7598: 7597: 7571: 7550: 7541: 7540: 7530: 7519:10.1038/nbt.2450 7498: 7487: 7486: 7476: 7466: 7442: 7436: 7435: 7433: 7432: 7420: 7414: 7413: 7395: 7367: 7361: 7360: 7350: 7318: 7312: 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5413: 5412: 5402: 5392: 5368: 5362: 5361: 5351: 5326:(5881): 1344–9. 5311: 5302: 5301: 5299: 5267: 5261: 5260: 5229:Nature Protocols 5224: 5218: 5217: 5180: 5174: 5173: 5163: 5131: 5125: 5124: 5087: 5081: 5080: 5070: 5046: 5040: 5039: 5011: 5005: 5004: 4978: 4958: 4949: 4948: 4912: 4906: 4905: 4895: 4885: 4868:(26): 15776–81. 4852: 4843: 4842: 4832: 4822: 4790: 4775: 4774: 4764: 4741:Plant Physiology 4732: 4726: 4723: 4717: 4716: 4706: 4696: 4672: 4666: 4665: 4631: 4625: 4624: 4604: 4598: 4597: 4566:Nature Protocols 4561: 4552: 4551: 4523: 4514: 4513: 4480:(5891): 956–60. 4468: 4462: 4461: 4416: 4410: 4409: 4373: 4367: 4366: 4356: 4346: 4322: 4313: 4312: 4275: 4269: 4268: 4231: 4225: 4224: 4202: 4196: 4195: 4167: 4156: 4155: 4145: 4135: 4111: 4102: 4101: 4068:(5235): 467–70. 4057: 4051: 4050: 4022: 4013: 4012: 3993:10.1038/nmeth757 3976: 3970: 3969: 3959: 3949: 3925: 3919: 3918: 3908: 3897:10.1038/nbt.2957 3876: 3870: 3869: 3859: 3827: 3821: 3820: 3792: 3783: 3782: 3780: 3771: 3754: 3753: 3725: 3719: 3718: 3708: 3684: 3675: 3674: 3664: 3654: 3622: 3616: 3615: 3605: 3573: 3556: 3555: 3545: 3521: 3515: 3514: 3470: 3457: 3456: 3438: 3414: 3408: 3407: 3397: 3365: 3359: 3358: 3348: 3316: 3310: 3309: 3299: 3289: 3257: 3251: 3250: 3222: 3209: 3208: 3189:10.1038/302718a0 3175:(5910): 718–21. 3164: 3158: 3157: 3147: 3137: 3105: 3099: 3098: 3088: 3064: 3058: 3057: 3029: 3018: 3017: 3007: 2975: 2964: 2963: 2953: 2921: 2892: 2891: 2863: 2846: 2845: 2835: 2811: 2805: 2804: 2764: 2758: 2757: 2747: 2707: 2698: 2697: 2687: 2662:(7468): 506–11. 2647: 2641: 2640: 2607:(5891): 956–60. 2596: 2587: 2586: 2550: 2544: 2543: 2510:(5013): 1651–6. 2499: 2490: 2489: 2487: 2486: 2472: 2463: 2462: 2436: 2418: 2391:reviewer reports 2384: 2377: 2328: 2245:Expression Atlas 2180: 2176: 2172:public databases 2131:sequenced genome 2119:reference genome 2073:gene annotations 2012:Candida albicans 1937:, and unveiling 1891:Retrotransposons 1804:quantitative PCR 1792: 1722: 1716: 1662: 1453: 1443: 1408:splice junctions 1330:tools (see also 1290:image processing 1273:Image processing 1173: 1037: 1031: 957:and extraction. 890:de novo assembly 686:reference genome 526:chaotropic salts 508:Isolation of RNA 325:Prior knowledge 298:Input RNA amount 271: 265: 21: 10906: 10905: 10901: 10900: 10899: 10897: 10896: 10895: 10856: 10855: 10786:(5): e1005457. 10770: 10768:Further reading 10765: 10760: 10756: 10731: 10727: 10723: 10718: 10717: 10672: 10668: 10623: 10619: 10572: 10568: 10537:(D1): D746–52. 10522: 10518: 10472: 10468: 10421: 10414: 10377:Nature Genetics 10368: 10361: 10352: 10350: 10342: 10341: 10337: 10314:10.1038/nrg3074 10298: 10294: 10263: 10259: 10220:(5758): 230–2. 10206: 10202: 10157: 10153: 10122: 10118: 10071: 10067: 10020: 10016: 9969: 9965: 9918: 9914: 9882: 9876: 9872: 9825: 9814: 9775:(6220): 431–5. 9761: 9757: 9716:(35): 14902–7. 9702: 9698: 9661:Eukaryotic Cell 9653: 9649: 9596: 9589: 9542: 9538: 9498: 9492: 9488: 9435: 9431: 9399: 9393: 9389: 9344: 9337: 9292: 9288: 9265:10.1038/nrg2072 9249: 9245: 9206: 9202: 9157: 9153: 9106: 9102: 9057: 9050: 9017:(4): e1000834. 9003: 8999: 8946: 8942: 8895: 8891: 8836: 8832: 8795: 8791: 8746: 8742: 8695: 8691: 8646: 8642: 8599: 8595: 8550: 8546: 8507: 8503: 8493: 8491: 8471: 8467: 8420: 8416: 8371: 8367: 8320: 8316: 8263: 8259: 8222:Genome Research 8214: 8210: 8164: 8157: 8108: 8104: 8059: 8055: 8018:Genome Research 8010: 8006: 7959: 7955: 7918:Genome Research 7910: 7906: 7859: 7855: 7810: 7806: 7761: 7754: 7709: 7705: 7682:(24): 3169–77. 7668: 7664: 7656: 7652: 7605: 7601: 7551: 7544: 7499: 7490: 7443: 7439: 7430: 7428: 7421: 7417: 7368: 7364: 7319: 7315: 7300: 7278: 7274: 7243: 7239: 7208: 7204: 7159: 7152: 7107: 7103: 7058: 7051: 7020:(8): 1494–512. 7005: 6998: 6983: 6969: 6965: 6958: 6939:10.1.1.361.8519 6926: 6922: 6877: 6870: 6825: 6818: 6773: 6766: 6734: 6727: 6714: 6713: 6709: 6678:(D1): D726–32. 6664: 6660: 6621:(7414): 57–74. 6606: 6602: 6555: 6548: 6501: 6492: 6447: 6440: 6403:(7265): 814–8. 6393: 6389: 6350: 6346: 6306: 6302: 6249: 6245: 6206: 6202: 6192: 6190: 6186: 6185: 6181: 6134: 6127: 6080: 6076: 6031: 6024: 5985:(6172): 776–9. 5971: 5967: 5928: 5924: 5885: 5881: 5842: 5838: 5801:Genome Research 5793: 5789: 5743: 5739: 5686: 5682: 5637: 5633: 5578: 5571: 5524: 5520: 5497:10.1038/nbt1486 5491:(10): 1135–45. 5481: 5474: 5420: 5416: 5369: 5365: 5312: 5305: 5268: 5264: 5225: 5221: 5181: 5177: 5132: 5128: 5099:(13): 1675–80. 5088: 5084: 5055:Genome Research 5047: 5043: 5012: 5008: 4976:10.1.1.661.6833 4959: 4952: 4913: 4909: 4853: 4846: 4805:(5): e1005457. 4791: 4778: 4733: 4729: 4724: 4720: 4673: 4669: 4654: 4632: 4628: 4605: 4601: 4562: 4555: 4524: 4517: 4469: 4465: 4417: 4413: 4374: 4370: 4323: 4316: 4276: 4272: 4232: 4228: 4221: 4203: 4199: 4168: 4159: 4112: 4105: 4058: 4054: 4023: 4016: 3977: 3973: 3926: 3922: 3877: 3873: 3836:Genome Research 3828: 3824: 3793: 3786: 3778: 3772: 3757: 3726: 3722: 3685: 3678: 3623: 3619: 3574: 3559: 3530:Genome Research 3522: 3518: 3481:(5235): 484–7. 3471: 3460: 3415: 3411: 3374:Genome Research 3366: 3362: 3331:(22): 9437–46. 3317: 3313: 3258: 3254: 3223: 3212: 3165: 3161: 3106: 3102: 3065: 3061: 3030: 3021: 2996:10.1038/nrg2934 2976: 2967: 2942:10.1038/nrg2484 2922: 2895: 2864: 2849: 2812: 2808: 2765: 2761: 2722:(6235): 660–5. 2708: 2701: 2648: 2644: 2597: 2590: 2555:Nature Genetics 2551: 2547: 2500: 2493: 2484: 2482: 2474: 2473: 2469: 2409:(5): e1005457. 2394: 2380: 2378: 2371: 2337: 2302:projected onto 2168: 2152:DNA replication 2143: 2081: 2020:drug resistance 1963: 1923: 1915:B cell receptor 1849: 1844: 1800: 1783:R/Bioconductor 1772:R/Bioconductor 1761:R/Bioconductor 1750:R/Bioconductor 1731:Specialisation 1703: 1670: 1412:gene regulation 1369: 1356: 1354:Quality control 1324: 1302:Image artefacts 1275: 1250:sequence graphs 1222:high-resolution 1218: 1201:number of genes 1196:number of reads 930: 870: 856:high-throughput 840: 791: 781:sequence, or a 749: 733:DNA Microarrays 724: 596: 567: 510: 494: 420:to an array of 264: 256:splice variants 243: 167: 123: 59:non-coding RNAs 39:RNA transcripts 28: 23: 22: 18:Transcriptomics 15: 12: 11: 5: 10904: 10894: 10893: 10888: 10883: 10878: 10873: 10868: 10854: 10853: 10852: 10851: 10846: 10841: 10833: 10824: 10769: 10766: 10764: 10763: 10754: 10748: to 10724: 10722: 10719: 10716: 10715: 10686:(D1): D203–8. 10666: 10617: 10566: 10516: 10466: 10412: 10359: 10335: 10308:(12): 861–74. 10292: 10257: 10200: 10151: 10116: 10065: 10014: 9963: 9912: 9870: 9812: 9755: 9696: 9647: 9587: 9536: 9486: 9429: 9387: 9335: 9286: 9243: 9200: 9151: 9116:(4): 487–500. 9100: 9071:(6): 591–605. 9048: 9011:PLOS Pathogens 8997: 8940: 8903:Genome Biology 8889: 8830: 8789: 8740: 8703:Genome Biology 8689: 8660:(16): 2078–9. 8654:Bioinformatics 8640: 8593: 8558:Bioinformatics 8544: 8511:Nature Methods 8501: 8488:10.1101/694554 8465: 8414: 8365: 8314: 8257: 8228:(6): 1147–59. 8208: 8155: 8118:(11): 909–12. 8112:Nature Methods 8102: 8073:(8): 1086–92. 8067:Bioinformatics 8053: 8004: 7967:Genome Biology 7953: 7924:(8): 1134–44. 7904: 7853: 7804: 7752: 7723:(9): 1105–11. 7717:Bioinformatics 7703: 7676:Bioinformatics 7662: 7650: 7599: 7562:(12): 1660–6. 7556:Bioinformatics 7542: 7488: 7437: 7415: 7362: 7313: 7298: 7272: 7253:(3): 211–226. 7237: 7218:(3): 211–226. 7202: 7150: 7101: 7049: 6996: 6981: 6963: 6956: 6920: 6885:Nature Methods 6868: 6833:Bioinformatics 6816: 6764: 6725: 6707: 6658: 6600: 6563:Genome Biology 6546: 6509:Genome Biology 6490: 6438: 6387: 6354:Nature Methods 6344: 6317:(3): 201–206. 6311:Nature Methods 6300: 6243: 6200: 6179: 6125: 6074: 6039:Nature Methods 6022: 5965: 5932:Nature Methods 5922: 5889:Nature Methods 5879: 5846:Nature Methods 5836: 5807:(9): 1543–51. 5787: 5737: 5680: 5651:(16): 2610–6. 5631: 5592:(11): e28240. 5569: 5532:Genome Biology 5518: 5472: 5414: 5377:Genome Biology 5363: 5303: 5262: 5235:(4): 599–604. 5219: 5175: 5126: 5082: 5041: 5006: 4950: 4923:(6): 1303–26. 4907: 4844: 4776: 4727: 4718: 4667: 4652: 4626: 4599: 4553: 4515: 4463: 4411: 4378:Nature Methods 4368: 4314: 4293:10.1038/nbt992 4287:(8): 1006–11. 4270: 4226: 4219: 4205:McLachlan GJ, 4197: 4157: 4103: 4052: 4014: 3981:Nature Methods 3971: 3920: 3871: 3842:(9): 1509–17. 3822: 3803:(2): 385–403. 3784: 3755: 3720: 3676: 3617: 3557: 3536:(10): 986–95. 3516: 3458: 3409: 3380:(2): 195–209. 3360: 3311: 3272:(12): 5350–4. 3252: 3210: 3159: 3120:(16): 4942–6. 3100: 3079:(4): 1303–16. 3059: 3019: 2965: 2893: 2847: 2820:Molecular Cell 2806: 2773:Nature Methods 2759: 2699: 2642: 2588: 2567:10.1038/ng.259 2561:(12): 1413–5. 2545: 2491: 2480:dan.corlan.net 2466: 2465: 2370: 2367: 2366: 2365: 2364: 2363: 2358: 2353: 2348: 2336: 2333: 2324: 2323: 2320: 2317: 2314: 2308: 2307: 2296: 2293: 2288: 2284: 2283: 2279: 2276: 2273: 2271:Genevestigator 2267: 2266: 2255: 2252: 2247: 2241: 2240: 2236: 2233: 2228: 2224: 2223: 2208: 2205: 2200: 2194: 2193: 2190: 2187: 2184: 2167: 2164: 2142: 2141:Non-coding RNA 2139: 2080: 2077: 2042:Southeast Asia 1962: 1959: 1922: 1919: 1848: 1845: 1843: 1840: 1799: 1796: 1788: 1787: 1784: 1781: 1777: 1776: 1773: 1770: 1766: 1765: 1762: 1759: 1755: 1754: 1751: 1748: 1744: 1743: 1740: 1737: 1733: 1732: 1729: 1726: 1702: 1699: 1669: 1668:Quantification 1666: 1658: 1657: 1650: 1647: 1644: 1641: 1637: 1636: 1633: 1630: 1627: 1624: 1620: 1619: 1616: 1613: 1610: 1607: 1603: 1602: 1599: 1596: 1593: 1590: 1586: 1585: 1582: 1579: 1576: 1573: 1569: 1568: 1565: 1562: 1559: 1556: 1552: 1551: 1548: 1545: 1542: 1539: 1535: 1534: 1527: 1524: 1521: 1518: 1514: 1513: 1510: 1507: 1504: 1501: 1497: 1496: 1493: 1490: 1487: 1484: 1480: 1479: 1474: 1469: 1464: 1459: 1368: 1365: 1355: 1352: 1338:, either in a 1323: 1320: 1315:pyrosequencing 1274: 1271: 1217: 1214: 1185:modified bases 1169: 1168: 1165: 1162: 1159: 1156: 1153: 1147: 1146: 1143: 1140: 1137: 1134: 1131: 1125: 1124: 1121: 1118: 1115: 1112: 1109: 1103: 1102: 1099: 1096: 1093: 1090: 1087: 1081: 1080: 1077: 1074: 1071: 1068: 1065: 1059: 1058: 1053: 1050: 1047: 1044: 1041: 943:ribosomal RNAs 929: 926: 869: 866: 839: 836: 790: 787: 748: 745: 723: 720: 595: 592: 566: 563: 514:RNA extraction 509: 506: 493: 492:Data gathering 490: 486:quantification 478:454 technology 468:hybridisations 404: 403: 400: 397: 391: 390: 387: 384: 378: 377: 374: 371: 365: 364: 361: 354: 350: 349: 346: 343: 336: 335: 329: 326: 322: 321: 318: 315: 311: 310: 307: 300: 294: 293: 290: 287: 281: 280: 277: 274: 263: 260: 242: 241:Early attempts 239: 166: 163: 122: 119: 105:of previously 26: 9: 6: 4: 3: 2: 10903: 10892: 10889: 10887: 10884: 10882: 10879: 10877: 10874: 10872: 10869: 10867: 10864: 10863: 10861: 10850: 10847: 10845: 10842: 10840: 10837: 10836: 10834: 10832: 10828: 10825: 10821: 10817: 10812: 10807: 10802: 10797: 10793: 10789: 10785: 10781: 10777: 10772: 10771: 10758: 10751: 10747: 10743: 10739: 10735: 10734:hybridisation 10729: 10725: 10711: 10707: 10702: 10697: 10693: 10689: 10685: 10681: 10677: 10670: 10662: 10658: 10653: 10648: 10644: 10640: 10636: 10632: 10628: 10621: 10613: 10609: 10604: 10599: 10594: 10589: 10585: 10581: 10577: 10570: 10562: 10558: 10553: 10548: 10544: 10540: 10536: 10532: 10528: 10520: 10512: 10508: 10503: 10498: 10494: 10490: 10486: 10482: 10478: 10470: 10462: 10458: 10453: 10448: 10443: 10438: 10434: 10430: 10426: 10419: 10417: 10408: 10404: 10400: 10396: 10391: 10386: 10383:(4): 365–71. 10382: 10378: 10374: 10366: 10364: 10349: 10345: 10339: 10331: 10327: 10323: 10319: 10315: 10311: 10307: 10303: 10296: 10288: 10284: 10280: 10276: 10273:(5): 289–97. 10272: 10268: 10261: 10253: 10249: 10245: 10241: 10236: 10231: 10227: 10223: 10219: 10215: 10211: 10204: 10196: 10192: 10187: 10182: 10178: 10174: 10170: 10166: 10162: 10155: 10147: 10143: 10139: 10135: 10131: 10127: 10120: 10112: 10108: 10103: 10098: 10093: 10088: 10084: 10080: 10076: 10069: 10061: 10057: 10052: 10047: 10042: 10037: 10033: 10029: 10025: 10018: 10010: 10006: 10001: 9996: 9991: 9986: 9982: 9978: 9974: 9967: 9959: 9955: 9950: 9945: 9940: 9935: 9931: 9927: 9923: 9916: 9908: 9904: 9900: 9896: 9893:(4): 759–76. 9892: 9888: 9881: 9874: 9866: 9862: 9857: 9852: 9847: 9842: 9838: 9834: 9830: 9823: 9821: 9819: 9817: 9808: 9804: 9799: 9794: 9790: 9786: 9782: 9778: 9774: 9770: 9766: 9759: 9751: 9747: 9742: 9737: 9732: 9727: 9723: 9719: 9715: 9711: 9707: 9700: 9692: 9688: 9683: 9678: 9674: 9670: 9667:(2): 536–45. 9666: 9662: 9658: 9651: 9643: 9639: 9634: 9629: 9625: 9621: 9617: 9613: 9609: 9605: 9601: 9594: 9592: 9583: 9579: 9574: 9569: 9564: 9559: 9555: 9551: 9547: 9540: 9532: 9528: 9524: 9520: 9516: 9512: 9509:(9): 618–30. 9508: 9504: 9497: 9490: 9482: 9478: 9473: 9468: 9464: 9460: 9456: 9452: 9448: 9444: 9440: 9433: 9425: 9421: 9417: 9413: 9410:(1): 93–101. 9409: 9405: 9398: 9391: 9383: 9379: 9374: 9369: 9365: 9361: 9358:(5): 257–71. 9357: 9353: 9349: 9342: 9340: 9331: 9327: 9322: 9317: 9313: 9309: 9306:(2): 133–40. 9305: 9301: 9297: 9290: 9282: 9278: 9274: 9270: 9266: 9262: 9259:(4): 272–85. 9258: 9254: 9247: 9239: 9235: 9231: 9227: 9223: 9219: 9216:(2): 93–108. 9215: 9211: 9204: 9196: 9192: 9187: 9182: 9178: 9174: 9171:(2): 134–42. 9170: 9166: 9162: 9155: 9147: 9143: 9139: 9135: 9131: 9127: 9123: 9119: 9115: 9111: 9104: 9096: 9092: 9087: 9082: 9078: 9074: 9070: 9066: 9062: 9055: 9053: 9044: 9040: 9035: 9030: 9025: 9020: 9016: 9012: 9008: 9001: 8993: 8989: 8984: 8979: 8975: 8971: 8967: 8963: 8959: 8955: 8951: 8944: 8936: 8932: 8927: 8922: 8917: 8912: 8908: 8904: 8900: 8893: 8885: 8881: 8876: 8871: 8866: 8861: 8857: 8853: 8849: 8845: 8841: 8834: 8826: 8822: 8817: 8812: 8808: 8804: 8800: 8793: 8785: 8781: 8776: 8771: 8767: 8763: 8759: 8755: 8751: 8744: 8736: 8732: 8727: 8722: 8717: 8712: 8708: 8704: 8700: 8693: 8685: 8681: 8676: 8671: 8667: 8663: 8659: 8655: 8651: 8644: 8636: 8632: 8628: 8624: 8620: 8616: 8612: 8608: 8604: 8597: 8589: 8585: 8580: 8575: 8571: 8567: 8563: 8559: 8555: 8548: 8540: 8536: 8532: 8528: 8524: 8520: 8516: 8512: 8505: 8489: 8484: 8480: 8476: 8469: 8461: 8457: 8452: 8447: 8442: 8437: 8433: 8429: 8425: 8418: 8410: 8406: 8401: 8396: 8392: 8388: 8385:(5): 455–77. 8384: 8380: 8376: 8369: 8361: 8357: 8352: 8347: 8342: 8337: 8333: 8329: 8325: 8318: 8310: 8306: 8301: 8296: 8292: 8288: 8284: 8280: 8276: 8272: 8268: 8261: 8253: 8249: 8244: 8239: 8235: 8231: 8227: 8223: 8219: 8212: 8204: 8200: 8195: 8190: 8186: 8182: 8179:(7): 644–52. 8178: 8174: 8170: 8162: 8160: 8151: 8147: 8143: 8139: 8134: 8129: 8125: 8121: 8117: 8113: 8106: 8098: 8094: 8089: 8084: 8080: 8076: 8072: 8068: 8064: 8057: 8049: 8045: 8040: 8035: 8031: 8027: 8023: 8019: 8015: 8008: 8000: 7996: 7991: 7986: 7981: 7976: 7972: 7968: 7964: 7957: 7949: 7945: 7940: 7935: 7931: 7927: 7923: 7919: 7915: 7908: 7900: 7896: 7891: 7886: 7881: 7876: 7872: 7868: 7864: 7857: 7849: 7845: 7840: 7835: 7831: 7827: 7824:(6): 315–27. 7823: 7819: 7815: 7808: 7800: 7796: 7791: 7786: 7782: 7778: 7774: 7770: 7766: 7759: 7757: 7748: 7744: 7739: 7734: 7730: 7726: 7722: 7718: 7714: 7707: 7699: 7695: 7690: 7685: 7681: 7677: 7673: 7666: 7660: 7657:HTS Mappers. 7654: 7646: 7642: 7637: 7632: 7627: 7622: 7618: 7614: 7610: 7603: 7595: 7591: 7587: 7583: 7579: 7575: 7570: 7565: 7561: 7557: 7549: 7547: 7538: 7534: 7529: 7524: 7520: 7516: 7512: 7508: 7504: 7497: 7495: 7493: 7484: 7480: 7475: 7470: 7465: 7460: 7456: 7452: 7448: 7441: 7426: 7419: 7411: 7407: 7403: 7399: 7394: 7389: 7385: 7381: 7377: 7373: 7366: 7358: 7354: 7349: 7344: 7340: 7336: 7332: 7328: 7324: 7317: 7309: 7305: 7301: 7295: 7291: 7287: 7283: 7276: 7268: 7264: 7260: 7256: 7252: 7248: 7241: 7233: 7229: 7225: 7221: 7217: 7213: 7206: 7198: 7194: 7189: 7184: 7180: 7176: 7173:(1): 207–10. 7172: 7168: 7164: 7157: 7155: 7146: 7142: 7137: 7132: 7128: 7124: 7120: 7116: 7112: 7105: 7097: 7093: 7088: 7083: 7079: 7075: 7071: 7067: 7063: 7056: 7054: 7045: 7041: 7036: 7031: 7027: 7023: 7019: 7015: 7011: 7003: 7001: 6992: 6988: 6984: 6982:9780080919768 6978: 6974: 6967: 6959: 6957:9780387251462 6953: 6949: 6945: 6940: 6935: 6931: 6924: 6916: 6912: 6907: 6902: 6898: 6894: 6891:(2): 115–21. 6890: 6886: 6882: 6875: 6873: 6864: 6860: 6855: 6850: 6846: 6842: 6839:(1): 139–40. 6838: 6834: 6830: 6823: 6821: 6812: 6808: 6803: 6798: 6794: 6790: 6786: 6782: 6778: 6771: 6769: 6760: 6756: 6752: 6748: 6744: 6740: 6732: 6730: 6721: 6717: 6711: 6703: 6699: 6694: 6689: 6685: 6681: 6677: 6673: 6669: 6662: 6654: 6650: 6645: 6640: 6636: 6632: 6628: 6624: 6620: 6616: 6612: 6604: 6596: 6592: 6587: 6582: 6577: 6572: 6568: 6564: 6560: 6553: 6551: 6542: 6538: 6533: 6528: 6523: 6518: 6514: 6510: 6506: 6499: 6497: 6495: 6486: 6482: 6477: 6472: 6468: 6464: 6461:(12): 970–8. 6460: 6456: 6452: 6445: 6443: 6434: 6430: 6426: 6422: 6418: 6414: 6410: 6406: 6402: 6398: 6391: 6383: 6379: 6375: 6371: 6367: 6363: 6359: 6355: 6348: 6340: 6336: 6332: 6328: 6324: 6320: 6316: 6312: 6304: 6296: 6292: 6288: 6284: 6279: 6274: 6270: 6266: 6263:(6): 333–51. 6262: 6258: 6254: 6247: 6239: 6235: 6231: 6227: 6223: 6219: 6215: 6211: 6204: 6189: 6183: 6175: 6171: 6166: 6161: 6156: 6151: 6147: 6143: 6139: 6132: 6130: 6121: 6117: 6112: 6107: 6102: 6097: 6093: 6089: 6085: 6078: 6070: 6066: 6061: 6056: 6052: 6048: 6045:(9): 709–15. 6044: 6040: 6036: 6029: 6027: 6018: 6014: 6009: 6004: 6000: 5996: 5992: 5988: 5984: 5980: 5976: 5969: 5961: 5957: 5953: 5949: 5945: 5941: 5937: 5933: 5926: 5918: 5914: 5910: 5906: 5902: 5898: 5895:(5): 377–82. 5894: 5890: 5883: 5875: 5871: 5867: 5863: 5859: 5855: 5851: 5847: 5840: 5832: 5828: 5823: 5818: 5814: 5810: 5806: 5802: 5798: 5791: 5783: 5779: 5774: 5769: 5765: 5761: 5757: 5753: 5749: 5741: 5733: 5729: 5724: 5719: 5715: 5711: 5707: 5703: 5699: 5695: 5691: 5684: 5676: 5672: 5667: 5662: 5658: 5654: 5650: 5646: 5642: 5635: 5627: 5623: 5618: 5613: 5608: 5603: 5599: 5595: 5591: 5587: 5583: 5576: 5574: 5565: 5561: 5556: 5551: 5546: 5541: 5537: 5533: 5529: 5522: 5514: 5510: 5506: 5502: 5498: 5494: 5490: 5486: 5479: 5477: 5468: 5464: 5459: 5454: 5450: 5446: 5442: 5438: 5434: 5430: 5426: 5418: 5410: 5406: 5401: 5396: 5391: 5386: 5382: 5378: 5374: 5367: 5359: 5355: 5350: 5345: 5341: 5337: 5333: 5329: 5325: 5321: 5317: 5310: 5308: 5298: 5293: 5289: 5285: 5282:(6247): 544. 5281: 5277: 5273: 5266: 5258: 5254: 5250: 5246: 5242: 5238: 5234: 5230: 5223: 5215: 5211: 5207: 5203: 5199: 5195: 5192:(3): 305–19. 5191: 5187: 5179: 5171: 5167: 5162: 5157: 5153: 5149: 5146:(4): 15e–15. 5145: 5141: 5137: 5130: 5122: 5118: 5114: 5110: 5106: 5102: 5098: 5094: 5086: 5078: 5074: 5069: 5064: 5061:(7): 639–45. 5060: 5056: 5052: 5045: 5037: 5033: 5029: 5025: 5021: 5017: 5010: 5002: 4998: 4994: 4990: 4986: 4982: 4977: 4972: 4969:(4): 237–59. 4968: 4964: 4957: 4955: 4946: 4942: 4938: 4934: 4930: 4926: 4922: 4918: 4911: 4903: 4899: 4894: 4889: 4884: 4879: 4875: 4871: 4867: 4863: 4859: 4851: 4849: 4840: 4836: 4831: 4826: 4821: 4816: 4812: 4808: 4804: 4800: 4796: 4789: 4787: 4785: 4783: 4781: 4772: 4768: 4763: 4758: 4754: 4750: 4746: 4742: 4738: 4731: 4722: 4714: 4710: 4705: 4700: 4695: 4690: 4686: 4682: 4678: 4671: 4663: 4659: 4655: 4649: 4645: 4641: 4637: 4630: 4622: 4618: 4614: 4610: 4609:BioTechniques 4603: 4595: 4591: 4587: 4583: 4579: 4575: 4571: 4567: 4560: 4558: 4549: 4545: 4541: 4537: 4533: 4529: 4522: 4520: 4511: 4507: 4503: 4499: 4495: 4491: 4487: 4483: 4479: 4475: 4467: 4459: 4455: 4451: 4447: 4443: 4439: 4435: 4431: 4427: 4423: 4415: 4407: 4403: 4399: 4395: 4391: 4387: 4383: 4379: 4372: 4364: 4360: 4355: 4350: 4345: 4340: 4336: 4332: 4328: 4321: 4319: 4310: 4306: 4302: 4298: 4294: 4290: 4286: 4282: 4274: 4266: 4262: 4258: 4254: 4250: 4249:10.1038/76469 4246: 4242: 4238: 4230: 4222: 4216: 4212: 4208: 4201: 4193: 4189: 4185: 4181: 4177: 4173: 4166: 4164: 4162: 4153: 4149: 4144: 4139: 4134: 4129: 4125: 4121: 4117: 4110: 4108: 4099: 4095: 4091: 4087: 4083: 4079: 4075: 4071: 4067: 4063: 4056: 4048: 4044: 4040: 4036: 4032: 4028: 4021: 4019: 4010: 4006: 4002: 3998: 3994: 3990: 3987:(5): 337–44. 3986: 3982: 3975: 3967: 3963: 3958: 3953: 3948: 3943: 3939: 3935: 3931: 3924: 3916: 3912: 3907: 3902: 3898: 3894: 3891:(9): 903–14. 3890: 3886: 3882: 3875: 3867: 3863: 3858: 3853: 3849: 3845: 3841: 3837: 3833: 3826: 3818: 3814: 3810: 3806: 3802: 3798: 3791: 3789: 3777: 3770: 3768: 3766: 3764: 3762: 3760: 3751: 3747: 3743: 3739: 3735: 3731: 3724: 3716: 3712: 3707: 3702: 3699:(3): 666–73. 3698: 3694: 3690: 3683: 3681: 3672: 3668: 3663: 3658: 3653: 3648: 3644: 3640: 3637:(1): e78644. 3636: 3632: 3628: 3621: 3613: 3609: 3604: 3599: 3595: 3591: 3587: 3583: 3579: 3572: 3570: 3568: 3566: 3564: 3562: 3553: 3549: 3544: 3539: 3535: 3531: 3527: 3520: 3512: 3508: 3504: 3500: 3496: 3492: 3488: 3484: 3480: 3476: 3469: 3467: 3465: 3463: 3454: 3450: 3446: 3442: 3437: 3432: 3429:(2): 243–51. 3428: 3424: 3420: 3413: 3405: 3401: 3396: 3391: 3387: 3383: 3379: 3375: 3371: 3364: 3356: 3352: 3347: 3342: 3338: 3334: 3330: 3326: 3322: 3315: 3307: 3303: 3298: 3293: 3288: 3283: 3279: 3275: 3271: 3267: 3263: 3256: 3248: 3244: 3240: 3236: 3232: 3228: 3221: 3219: 3217: 3215: 3206: 3202: 3198: 3194: 3190: 3186: 3182: 3178: 3174: 3170: 3163: 3155: 3151: 3146: 3141: 3136: 3131: 3127: 3123: 3119: 3115: 3111: 3104: 3096: 3092: 3087: 3082: 3078: 3074: 3070: 3063: 3055: 3051: 3047: 3043: 3039: 3035: 3028: 3026: 3024: 3015: 3011: 3006: 3001: 2997: 2993: 2989: 2985: 2981: 2974: 2972: 2970: 2961: 2957: 2952: 2947: 2943: 2939: 2935: 2931: 2927: 2920: 2918: 2916: 2914: 2912: 2910: 2908: 2906: 2904: 2902: 2900: 2898: 2889: 2885: 2881: 2877: 2873: 2869: 2862: 2860: 2858: 2856: 2854: 2852: 2843: 2839: 2834: 2829: 2826:(4): 610–20. 2825: 2821: 2817: 2810: 2802: 2798: 2794: 2790: 2786: 2782: 2778: 2774: 2770: 2763: 2755: 2751: 2746: 2741: 2737: 2733: 2729: 2725: 2721: 2717: 2713: 2706: 2704: 2695: 2691: 2686: 2681: 2677: 2673: 2669: 2665: 2661: 2657: 2653: 2646: 2638: 2634: 2630: 2626: 2622: 2618: 2614: 2610: 2606: 2602: 2595: 2593: 2584: 2580: 2576: 2572: 2568: 2564: 2560: 2556: 2549: 2541: 2537: 2533: 2529: 2525: 2521: 2517: 2513: 2509: 2505: 2498: 2496: 2481: 2477: 2471: 2467: 2464: 2460: 2456: 2452: 2448: 2444: 2440: 2435: 2430: 2426: 2422: 2417: 2412: 2408: 2404: 2403: 2398: 2392: 2388: 2383: 2376: 2362: 2359: 2357: 2354: 2352: 2349: 2347: 2344: 2343: 2342: 2339: 2338: 2332: 2321: 2318: 2315: 2313: 2310: 2309: 2305: 2301: 2297: 2294: 2292: 2289: 2286: 2285: 2280: 2277: 2274: 2272: 2269: 2268: 2264: 2260: 2259:Gene Ontology 2256: 2253: 2251: 2248: 2246: 2243: 2242: 2237: 2234: 2232: 2229: 2227:ArrayExpress 2226: 2225: 2221: 2220:repeatability 2217: 2213: 2209: 2206: 2204: 2201: 2199: 2196: 2195: 2191: 2188: 2185: 2182: 2181: 2175: 2173: 2163: 2161: 2157: 2153: 2149: 2138: 2136: 2132: 2128: 2124: 2120: 2115: 2113: 2109: 2105: 2101: 2097: 2093: 2090: 2086: 2076: 2074: 2070: 2069:invertebrates 2066: 2062: 2058: 2057:global change 2054: 2050: 2045: 2043: 2039: 2035: 2031: 2027: 2026: 2021: 2016: 2014: 2013: 2008: 2004: 2000: 1996: 1992: 1988: 1984: 1980: 1976: 1972: 1968: 1958: 1956: 1950: 1948: 1944: 1940: 1936: 1933:, predicting 1932: 1928: 1918: 1916: 1912: 1908: 1904: 1900: 1896: 1892: 1888: 1886: 1882: 1878: 1874: 1870: 1866: 1862: 1858: 1854: 1839: 1837: 1833: 1829: 1824: 1821: 1817: 1813: 1809: 1805: 1795: 1785: 1782: 1779: 1778: 1774: 1771: 1768: 1767: 1763: 1760: 1757: 1756: 1752: 1749: 1746: 1745: 1741: 1738: 1735: 1734: 1723: 1720: 1715: 1712: 1708: 1698: 1694: 1691: 1681: 1679: 1674: 1665: 1655: 1651: 1648: 1645: 1642: 1639: 1638: 1634: 1631: 1628: 1625: 1622: 1621: 1617: 1614: 1611: 1608: 1605: 1604: 1600: 1597: 1594: 1591: 1588: 1587: 1583: 1580: 1577: 1574: 1571: 1570: 1566: 1563: 1560: 1557: 1554: 1553: 1549: 1546: 1543: 1540: 1537: 1536: 1532: 1528: 1525: 1522: 1519: 1516: 1515: 1511: 1508: 1505: 1502: 1499: 1498: 1494: 1491: 1488: 1485: 1483:Velvet-Oases 1482: 1481: 1478: 1475: 1473: 1470: 1468: 1465: 1463: 1460: 1458: 1455: 1454: 1451: 1449: 1442: 1440: 1436: 1432: 1427: 1423: 1419: 1417: 1413: 1409: 1405: 1401: 1397: 1394:Alignment of 1392: 1390: 1385: 1381: 1379: 1374: 1364: 1362: 1351: 1349: 1345: 1341: 1337: 1333: 1329: 1319: 1316: 1312: 1306: 1303: 1299: 1295: 1291: 1283: 1279: 1270: 1268: 1264: 1259: 1255: 1251: 1247: 1245: 1240: 1236: 1232: 1227: 1223: 1216:Data analysis 1213: 1211: 1207: 1202: 1197: 1192: 1190: 1189:amplification 1186: 1182: 1176: 1166: 1163: 1160: 1157: 1154: 1152: 1149: 1148: 1144: 1141: 1138: 1135: 1132: 1130: 1127: 1126: 1122: 1119: 1116: 1113: 1110: 1108: 1105: 1104: 1100: 1097: 1094: 1091: 1088: 1086: 1083: 1082: 1078: 1075: 1072: 1069: 1066: 1064: 1061: 1060: 1057: 1054: 1051: 1048: 1045: 1042: 1039: 1038: 1035: 1030: 1028: 1023: 1019: 1013: 1011: 1007: 1003: 999: 995: 990: 988: 984: 980: 976: 972: 968: 964: 963:Fragmentation 958: 956: 952: 948: 944: 940: 934: 925: 923: 919: 918: 913: 908: 906: 901: 899: 895: 894:dynamic range 891: 887: 883: 882:deep sampling 878: 874: 861: 857: 852: 850: 844: 835: 832: 827: 825: 821: 817: 812: 808: 804: 800: 795: 786: 784: 780: 777: 772: 769: 765: 764:fluorescently 761: 757: 753: 741: 736: 734: 728: 719: 716: 714: 710: 706: 702: 698: 693: 691: 687: 683: 682:deconvolution 679: 675: 671: 667: 663: 659: 652: 651:transcription 648: 644: 640: 636: 632: 628: 624: 620: 616: 612: 608: 606: 600: 591: 589: 585: 584:EST libraries 580: 576: 572: 562: 560: 559:Snap-freezing 556: 551: 547: 546:ribosomal RNA 543: 539: 535: 534:precipitation 531: 527: 523: 519: 515: 505: 503: 502:hybridisation 499: 489: 487: 483: 479: 475: 474: 469: 465: 461: 457: 453: 449: 444: 442: 438: 434: 430: 426: 423: 422:complementary 419: 418:hybridisation 415: 411: 401: 398: 396: 393: 392: 388: 385: 383: 382:Dynamic range 380: 379: 375: 372: 370: 367: 366: 362: 359: 355: 352: 351: 347: 344: 341: 338: 337: 334: 330: 327: 324: 323: 319: 316: 313: 312: 308: 305: 301: 299: 296: 295: 291: 288: 286: 283: 282: 278: 275: 273: 272: 269: 259: 257: 252: 248: 238: 236: 232: 228: 224: 220: 216: 212: 208: 204: 200: 196: 192: 188: 184: 180: 176: 172: 162: 160: 156: 152: 148: 144: 135: 131: 127: 118: 116: 112: 108: 104: 100: 96: 92: 89:in different 88: 83: 80: 77:, which uses 76: 72: 66: 64: 60: 56: 52: 51:transcription 48: 44: 40: 36: 35:transcriptome 32: 19: 10783: 10779: 10757: 10733: 10728: 10683: 10679: 10669: 10634: 10630: 10620: 10583: 10579: 10569: 10534: 10530: 10519: 10484: 10480: 10469: 10432: 10428: 10380: 10376: 10351:. Retrieved 10347: 10338: 10305: 10301: 10295: 10270: 10266: 10260: 10217: 10213: 10203: 10168: 10164: 10154: 10129: 10125: 10119: 10082: 10079:BMC Genomics 10078: 10068: 10031: 10028:BMC Genomics 10027: 10017: 9980: 9977:BMC Genomics 9976: 9966: 9929: 9926:BMC Genomics 9925: 9915: 9890: 9886: 9873: 9856:10072/428702 9836: 9832: 9772: 9768: 9758: 9713: 9709: 9699: 9664: 9660: 9650: 9607: 9603: 9553: 9549: 9539: 9506: 9502: 9489: 9446: 9442: 9432: 9407: 9403: 9390: 9355: 9351: 9303: 9299: 9289: 9256: 9252: 9246: 9213: 9209: 9203: 9168: 9164: 9154: 9113: 9109: 9103: 9068: 9064: 9014: 9010: 9000: 8957: 8953: 8943: 8906: 8902: 8892: 8847: 8843: 8833: 8809:(3): 280–7. 8806: 8802: 8792: 8760:(3): 243–6. 8757: 8753: 8743: 8706: 8702: 8692: 8657: 8653: 8643: 8613:(5): 525–7. 8610: 8606: 8596: 8564:(2): 166–9. 8561: 8557: 8547: 8514: 8510: 8504: 8492:. Retrieved 8478: 8468: 8431: 8427: 8417: 8382: 8378: 8368: 8331: 8328:BMC Genomics 8327: 8317: 8274: 8270: 8260: 8225: 8221: 8211: 8176: 8172: 8115: 8111: 8105: 8070: 8066: 8056: 8024:(5): 821–9. 8021: 8017: 8007: 7970: 7966: 7956: 7921: 7917: 7907: 7870: 7867:BMC Genomics 7866: 7856: 7821: 7817: 7807: 7775:(5): 511–5. 7772: 7768: 7720: 7716: 7706: 7679: 7675: 7665: 7653: 7616: 7612: 7602: 7559: 7555: 7513:(1): 46–53. 7510: 7506: 7454: 7450: 7440: 7429:. Retrieved 7418: 7378:(4): 175–9. 7375: 7371: 7365: 7330: 7326: 7316: 7281: 7275: 7250: 7246: 7240: 7215: 7211: 7205: 7170: 7166: 7118: 7114: 7104: 7072:(3): 290–5. 7069: 7065: 7017: 7013: 6972: 6966: 6929: 6923: 6888: 6884: 6836: 6832: 6784: 6780: 6742: 6738: 6719: 6710: 6675: 6671: 6661: 6618: 6614: 6603: 6566: 6562: 6512: 6508: 6458: 6454: 6400: 6396: 6390: 6360:(8): 733–5. 6357: 6353: 6347: 6314: 6310: 6303: 6260: 6256: 6246: 6216:(5): 434–9. 6213: 6209: 6203: 6191:. Retrieved 6182: 6145: 6141: 6091: 6088:BMC Genomics 6087: 6077: 6042: 6038: 5982: 5978: 5968: 5938:(2): 163–6. 5935: 5931: 5925: 5892: 5888: 5882: 5849: 5845: 5839: 5804: 5800: 5790: 5755: 5751: 5740: 5697: 5693: 5683: 5648: 5644: 5634: 5589: 5585: 5535: 5531: 5521: 5488: 5484: 5432: 5428: 5417: 5380: 5376: 5366: 5323: 5319: 5279: 5275: 5265: 5232: 5228: 5222: 5189: 5185: 5178: 5143: 5139: 5129: 5096: 5092: 5085: 5058: 5054: 5044: 5022:(7): 374–9. 5019: 5015: 5009: 4966: 4962: 4920: 4916: 4910: 4865: 4861: 4802: 4798: 4747:(3): 960–8. 4744: 4740: 4730: 4721: 4684: 4681:BMC Genomics 4680: 4670: 4635: 4629: 4612: 4608: 4602: 4572:(2): 581–5. 4569: 4565: 4534:(1): 156–9. 4531: 4527: 4477: 4473: 4466: 4425: 4421: 4414: 4384:(7): 621–8. 4381: 4377: 4371: 4334: 4331:BMC Genomics 4330: 4284: 4280: 4273: 4243:(6): 630–4. 4240: 4236: 4229: 4210: 4200: 4175: 4171: 4126:(2): 141–8. 4123: 4119: 4065: 4061: 4055: 4033:(7): 492–4. 4030: 4026: 3984: 3980: 3974: 3937: 3933: 3923: 3888: 3884: 3874: 3839: 3835: 3825: 3800: 3796: 3733: 3729: 3723: 3696: 3693:Cell Reports 3692: 3634: 3630: 3620: 3585: 3581: 3533: 3529: 3519: 3478: 3474: 3426: 3422: 3412: 3377: 3373: 3363: 3328: 3324: 3314: 3269: 3265: 3255: 3230: 3226: 3172: 3168: 3162: 3117: 3113: 3103: 3076: 3072: 3062: 3037: 3033: 2990:(2): 87–98. 2987: 2983: 2936:(1): 57–63. 2933: 2929: 2871: 2867: 2823: 2819: 2809: 2776: 2772: 2762: 2719: 2715: 2659: 2655: 2645: 2604: 2600: 2558: 2554: 2548: 2507: 2503: 2483:. Retrieved 2479: 2470: 2406: 2400: 2372: 2356:Metabolomics 2327: 2316:noncode.org 2192:Description 2169: 2156:RNA splicing 2144: 2126: 2116: 2100:metal uptake 2088: 2082: 2046: 2023: 2017: 2010: 1964: 1951: 1924: 1889: 1885:gene fusions 1850: 1842:Applications 1825: 1801: 1791: 1728:Environment 1718: 1704: 1695: 1686: 1676: 1661: 1653: 1531:MPI-parallel 1517:Trans-ABySS 1476: 1471: 1467:Last updated 1466: 1461: 1456: 1447: 1445: 1438: 1425: 1421: 1420: 1416:mRNA isoform 1393: 1377: 1370: 1357: 1348:Bioconductor 1325: 1307: 1294:regular grid 1287: 1281: 1263:fastq format 1257: 1243: 1219: 1193: 1177: 1172: 1055: 1033: 1014: 991: 971:nebulisation 959: 939:poly-A tails 935: 931: 915: 909: 902: 871: 846: 828: 811:fluorophores 796: 792: 773: 758:, known as " 750: 730: 717: 694: 656: 647:deconvoluted 602: 577:(cDNA) by a 568: 555:5’ mRNA ends 530:biomolecules 511: 495: 471: 447: 445: 407: 340:Quantitation 267: 244: 233:, and later 215:gene content 197:(ESTs). The 168: 139: 129: 84: 67: 30: 29: 10132:: 191–227. 8709:(12): 550. 7973:(12): 553. 7393:1874/309456 7333:(13): e90. 5852:(1): 72–4. 5758:(1): 4–18. 5383:(12): 523. 4917:The Analyst 3736:(2): 93–9. 2874:(1): 4–11. 2779:(1): 22–4. 2235:Microarray 2123:Douglas fir 2104:homeostasis 2089:Arabidopsis 2067:, although 1769:Limma/Voom 1739:Unix-based 1288:Microarray 1129:Ion Torrent 847:Summary of 752:Microarrays 731:Summary of 722:Microarrays 611:transcribed 603:Summary of 410:microarrays 369:Sensitivity 279:Microarray 221:the entire 171:transcripts 147:human brain 107:unannotated 71:microarrays 10860:Categories 10586:: 420747. 10353:2018-03-26 9983:(1): 786. 9300:Immunology 8481:: 694554. 8133:1885/51040 7619:(4): 787. 7457:(1): 366. 7431:2017-05-23 6787:(7): e47. 6569:(9): R95. 6193:2016-10-06 6148:: 251364. 5538:(6): R86. 4687:(1): 419. 4178:: 129–53. 3588:: 138–42. 3233:(1): 4–7. 3040:: 135–51. 2485:2016-10-05 2369:References 2351:Proteomics 2053:adaptation 1832:knock-down 1798:Validation 1736:Cuffdiff2 1640:StringTie 1400:eukaryotes 1179:RNA using 1158:10,000 bp 1092:50–300 bp 1006:GC-content 975:sonication 951:micro RNAs 826:one gene. 619:eukaryotes 433:Affymetrix 306:total RNA 285:Throughput 219:sequencing 95:conditions 10839:cufflinks 10435:: 420–3. 9865:2296-7745 9610:: 19228. 9531:205498287 9130:0071-1365 8635:205282743 8539:205419270 8494:27 August 7569:1305.6760 7410:205453732 6991:437246554 6934:CiteSeerX 5700:: 25533. 4971:CiteSeerX 4458:205213499 4406:205418589 2459:Q33703532 2425:1553-734X 2385:license ( 2382:CC BY 4.0 2061:pollution 2009:pathogen 1927:pathogens 1857:profiling 1853:diagnosis 1828:phenotype 1812:amplicons 1780:Ballgown 1725:Software 1367:Alignment 1298:intensity 1040:Platform 924:tissues. 863:variants. 799:picolitre 776:annotated 756:oligomers 342:accuracy 175:Libraries 103:functions 99:regulated 47:expressed 10844:kallisto 10820:28545146 10710:26586799 10661:18835852 10612:19956698 10561:26481351 10511:25361974 10461:19484163 10399:11726920 10330:13036469 10322:22094949 10287:15851066 10252:44527461 10244:16357227 10195:16943439 10111:26772543 10060:23445355 10009:25214207 9958:22047402 9907:19192189 9807:25502316 9750:19666593 9691:15075282 9642:26759178 9582:25914674 9523:22890146 9481:25517437 9449:: 5792. 9424:18284925 9382:26996076 9330:26551575 9273:17363976 9238:14433306 9230:26781813 9195:22739340 9146:52922135 9138:30287586 9095:19224247 9043:20368969 8992:19056941 8935:12184808 8884:20011106 8825:21498551 8784:25748911 8735:25516281 8684:19505943 8627:27043002 8588:25260700 8531:20195258 8460:21816040 8409:22506599 8360:20950480 8309:16056220 8252:15140833 8203:21572440 8142:20935650 8097:22368243 8048:18349386 7999:25608678 7948:27252236 7899:23837739 7848:20211242 7818:Genomics 7799:20436464 7747:19289445 7698:23060614 7645:33923758 7586:24532719 7537:23222703 7483:25408143 7402:23481128 7357:21576222 7267:31598448 7232:31598448 7197:11752295 7145:22009675 7096:25690850 7044:23845962 6915:25633503 6863:19910308 6811:25605792 6759:34329375 6702:26527727 6653:22955616 6595:24020486 6541:26813401 6485:23961961 6425:19776739 6382:15053702 6374:26076426 6331:29334379 6287:27184599 6278:10373632 6230:22522955 6174:22829749 6120:22827831 6069:20711195 6017:24531970 5952:24363023 5917:16570747 5909:19349980 5874:39225091 5866:22101854 5831:21816910 5782:25649271 5732:27156886 5675:26116762 5626:22140562 5586:PLOS ONE 5564:24981968 5505:18846087 5467:24578530 5409:25633159 5358:18451266 5249:29494575 5214:39437458 5206:16075461 5170:12582260 5121:35232673 5036:15978318 5001:13712888 4993:17095434 4945:24479125 4902:14663149 4839:28545146 4771:15020760 4713:24888378 4594:28653075 4586:17406285 4510:10013179 4502:18599741 4450:18488015 4398:18516045 4363:17010196 4309:15336496 4301:15247925 4265:13884154 4257:10835600 4192:12117754 4152:17644526 4047:11287436 4009:16088782 4001:15846360 3966:17961233 3915:25150838 3866:18550803 3817:24194394 3750:11015604 3715:22939981 3671:24454679 3631:PLOS ONE 3612:25149683 3511:16281846 3453:11430660 3404:10022985 3054:19715439 3014:21191423 2960:19015660 2888:23290152 2842:26000846 2801:27632439 2793:24524133 2754:25954002 2694:24037378 2637:10013179 2629:18599741 2575:18978789 2540:13436211 2455:Wikidata 2443:28545146 2361:Venomics 2346:Genomics 2335:See also 2319:RNA-Seq 2300:heatmaps 2263:InterPro 2108:cucumber 2092:ecotypes 1987:salinity 1979:chickpea 1967:pathways 1871:. These 1865:promoter 1572:Newbler 1555:miraEST 1538:Trinity 1462:Released 1457:Software 1446:RNA-Seq 1117:320 Gbp 1095:900 Gbp 1085:Illumina 1073:0.7 Gbp 886:aligning 709:promoter 448:in vitro 402:>99% 399:>99% 302:Low ~ 1 276:RNA-Seq 205:such as 179:silkmoth 153:states, 53:. Here, 49:through 10811:5436640 10788:Bibcode 10701:4702886 10652:2686534 10603:2777001 10552:4702781 10502:4383899 10452:5823224 10407:6994467 10222:Bibcode 10214:Science 10186:1592862 10146:1883196 10102:4715275 10051:3673906 10034:: 137. 10000:4247155 9949:3219749 9932:: 540. 9798:5642863 9777:Bibcode 9769:Science 9741:2720412 9718:Bibcode 9633:4725360 9612:Bibcode 9573:4391036 9556:: 235. 9472:4351646 9451:Bibcode 9373:7097555 9321:4717243 9281:9719784 9186:3548270 9086:2757612 9034:2848557 8983:2833333 8962:Bibcode 8954:Science 8875:2781110 8852:Bibcode 8775:4792117 8726:4302049 8675:2723002 8579:4287950 8479:bioRxiv 8451:3163565 8434:: 323. 8400:3342519 8351:3091720 8334:: 571. 8300:1464427 8279:Bibcode 8194:3571712 8150:1034682 8088:3324515 8039:2336801 7990:4298084 7939:4971766 7890:3733778 7873:: 465. 7839:2874646 7790:3146043 7738:2672628 7636:8074181 7594:5152689 7528:3869392 7474:4246454 7348:3141275 7308:3684245 7136:3245110 7087:4643835 7035:3875132 6906:4509590 6854:2796818 6802:4402510 6693:4702836 6644:3439153 6623:Bibcode 6586:4054597 6532:4728800 6476:3842884 6433:4426760 6405:Bibcode 6339:3589823 6295:8295541 6238:5300923 6165:3398667 6111:3431227 6094:: 341. 6060:3005310 6008:4412462 5987:Bibcode 5979:Science 5960:6765530 5822:3166838 5773:4310221 5723:4860583 5702:Bibcode 5666:4523409 5617:3227650 5594:Bibcode 5555:4197826 5513:6384349 5458:4140943 5437:Bibcode 5429:Science 5400:4290828 5349:2951732 5328:Bibcode 5320:Science 5284:Bibcode 5276:Science 5257:3560001 5113:9634850 5077:8796352 4925:Bibcode 4870:Bibcode 4830:5436640 4807:Bibcode 4704:4070569 4662:9664454 4621:1699561 4548:2440339 4482:Bibcode 4474:Science 4430:Bibcode 4354:1592491 4337:: 246. 4098:6720459 4090:7569999 4070:Bibcode 4062:Science 3957:2204045 3940:: 412. 3906:4321899 3857:2527709 3662:3894192 3639:Bibcode 3603:4152252 3552:9331369 3503:7570003 3483:Bibcode 3475:Science 3445:9008165 3355:2479917 3274:Bibcode 3247:9448457 3205:4364361 3197:6687628 3177:Bibcode 3154:6956902 3122:Bibcode 3005:3031867 2951:2949280 2745:4547472 2724:Bibcode 2716:Science 2685:3918453 2664:Bibcode 2609:Bibcode 2601:Science 2583:9228930 2532:2047873 2512:Bibcode 2504:Science 2451:3714586 2434:5436640 2312:NONCODE 2261:terms, 2216:MINSEQE 2127:de novo 2065:animals 2051:" and " 2003:biofilm 1983:drought 1820:primers 1808:control 1758:DEseq2 1678:Heatmap 1654:de novo 1606:SPAdes 1448:de novo 1439:de novo 1426:de novo 1422:De novo 1380:aligned 1378:de novo 1373:aligned 1258:de novo 1244:De novo 1139:30 Gbp 1136:400 bp 1101:362903 1070:700 bp 1018:isoform 947:taxon's 928:Methods 917:in situ 873:RNA-Seq 860:aligned 849:RNA-Seq 838:RNA-Seq 789:Methods 783:library 713:cloning 615:spliced 414:RNA-Seq 155:tissues 151:disease 121:History 111:disease 91:tissues 75:RNA-Seq 10849:tophat 10818: 10808: 10746:anneal 10708: 10698: 10659: 10649: 10610: 10600: 10559: 10549: 10509: 10499: 10459: 10449: 10405: 10397: 10328: 10320: 10285: 10250: 10242: 10193: 10183: 10144: 10109: 10099: 10085:: 63. 10058: 10048: 10007: 9997: 9956: 9946: 9905: 9863: 9805: 9795: 9748: 9738: 9689: 9682:387656 9679: 9640: 9630: 9580: 9570: 9529: 9521: 9479: 9469: 9422: 9380: 9370: 9328: 9318: 9279: 9271: 9236: 9228: 9193: 9183: 9144: 9136: 9128: 9093: 9083: 9041: 9031: 8990: 8980: 8933: 8926:126239 8923: 8882: 8872: 8823: 8782: 8772: 8733: 8723: 8682: 8672: 8633: 8625: 8586: 8576: 8537: 8529: 8458: 8448: 8407: 8397: 8358: 8348: 8307: 8297: 8271:Nature 8250: 8243:419793 8240: 8201: 8191: 8148: 8140: 8095: 8085: 8046: 8036: 7997: 7987: 7946: 7936: 7897: 7887: 7846: 7836: 7797: 7787: 7745: 7735: 7696: 7643: 7633: 7613:Plants 7592: 7584: 7535: 7525: 7481: 7471: 7408: 7400: 7355: 7345: 7306: 7296: 7265: 7230: 7195: 7185: 7143: 7133: 7094: 7084: 7042: 7032: 6989: 6979: 6954: 6936: 6913: 6903: 6861: 6851: 6809: 6799: 6757: 6700: 6690: 6651: 6641: 6615:Nature 6593: 6583: 6539: 6529: 6515:: 13. 6483: 6473: 6431: 6423: 6397:Nature 6380: 6372: 6337: 6329: 6293: 6285: 6275: 6236: 6228: 6172: 6162: 6118: 6108: 6067: 6057: 6015: 6005: 5958: 5950: 5915: 5907: 5872: 5864: 5829: 5819: 5780: 5770: 5730: 5720: 5673: 5663: 5624: 5614: 5562: 5552: 5511: 5503: 5465: 5455: 5407: 5397: 5356: 5346: 5255: 5247: 5212: 5204: 5168: 5161:150247 5158: 5119: 5111: 5075: 5034: 4999: 4991: 4973: 4943: 4900: 4893:307644 4890: 4837: 4827: 4769: 4762:389919 4759: 4711: 4701: 4660: 4650: 4619: 4592: 4584: 4546: 4508: 4500: 4456: 4448: 4422:Nature 4404: 4396: 4361: 4351: 4307: 4299: 4263: 4255: 4217: 4190: 4150: 4096: 4088: 4045: 4007: 3999: 3964: 3954: 3913: 3903: 3864: 3854: 3815: 3748: 3713: 3669: 3659: 3610: 3600: 3550: 3509: 3501: 3451: 3443: 3402: 3395:310711 3392: 3353: 3346:335144 3343: 3306:414220 3304: 3297:431715 3294: 3245: 3203: 3195: 3169:Nature 3152: 3145:346801 3142: 3095:519770 3093: 3052: 3012: 3002: 2958: 2948: 2886: 2840: 2799: 2791: 2752: 2742: 2692: 2682: 2656:Nature 2635: 2627: 2581: 2573: 2538: 2530: 2457: 2449: 2441: 2431: 2423: 2287:RefEx 2158:, and 2049:stress 2007:fungal 1971:biotic 1911:T cell 1836:rescue 1747:EdgeR 1435:contig 1404:intron 1361:k-mers 1231:intron 1161:2 Gbp 1151:PacBio 1120:99.9% 1114:50 bp 1098:99.9% 1076:99.9% 1022:strand 905:genome 779:genome 760:probes 701:5’ end 674:joined 588:barley 550:poly-A 425:probes 333:probes 223:genome 191:Sanger 115:assays 43:genome 10886:Omics 10721:Notes 10403:S2CID 10326:S2CID 10248:S2CID 9883:(PDF) 9527:S2CID 9499:(PDF) 9400:(PDF) 9277:S2CID 9234:S2CID 9142:S2CID 8631:S2CID 8535:S2CID 8146:S2CID 7590:S2CID 7564:arXiv 7406:S2CID 7304:S2CID 7263:S2CID 7228:S2CID 7188:99122 6745:(6). 6429:S2CID 6378:S2CID 6335:S2CID 6291:S2CID 6234:S2CID 6188:"SRA" 5956:S2CID 5913:S2CID 5870:S2CID 5509:S2CID 5253:S2CID 5210:S2CID 5117:S2CID 4997:S2CID 4590:S2CID 4506:S2CID 4454:S2CID 4402:S2CID 4305:S2CID 4261:S2CID 4207:Do KA 4094:S2CID 4005:S2CID 3779:(PDF) 3507:S2CID 3449:S2CID 3201:S2CID 2797:S2CID 2633:S2CID 2579:S2CID 2536:S2CID 2447:S2CID 2341:omics 2212:MIAME 2189:Data 2186:Host 2183:Name 2112:koala 2094:that 1999:EREBP 1816:3’UTR 1646:2019 1643:2015 1629:2017 1626:2011 1623:RSEM 1612:2017 1609:2012 1595:2014 1592:2008 1578:2012 1575:2004 1561:2016 1558:1999 1544:2017 1541:2011 1523:2016 1520:2010 1506:2014 1503:2011 1489:2011 1486:2008 1311:Roche 1155:2011 1145:1953 1133:2010 1123:7032 1111:2008 1107:SOLiD 1089:2006 1079:3548 1067:2005 981:with 977:, or 922:fixed 896:of 5 824:assay 803:cDNAs 542:DNase 524:with 522:RNase 452:cDNAs 437:model 159:cells 87:genes 10816:PMID 10706:PMID 10657:PMID 10608:PMID 10584:2008 10557:PMID 10507:PMID 10457:PMID 10395:PMID 10318:PMID 10283:PMID 10240:PMID 10191:PMID 10142:PMID 10107:PMID 10056:PMID 10005:PMID 9954:PMID 9903:PMID 9861:ISSN 9803:PMID 9746:PMID 9687:PMID 9638:PMID 9578:PMID 9519:PMID 9477:PMID 9420:PMID 9378:PMID 9326:PMID 9269:PMID 9226:PMID 9191:PMID 9134:PMID 9126:ISSN 9091:PMID 9039:PMID 8988:PMID 8931:PMID 8880:PMID 8821:PMID 8780:PMID 8731:PMID 8680:PMID 8623:PMID 8584:PMID 8527:PMID 8496:2019 8456:PMID 8405:PMID 8356:PMID 8305:PMID 8248:PMID 8199:PMID 8138:PMID 8093:PMID 8044:PMID 7995:PMID 7944:PMID 7895:PMID 7844:PMID 7795:PMID 7743:PMID 7694:PMID 7641:PMID 7582:PMID 7533:PMID 7479:PMID 7398:PMID 7353:PMID 7294:ISBN 7193:PMID 7141:PMID 7092:PMID 7040:PMID 6987:OCLC 6977:ISBN 6952:ISBN 6911:PMID 6859:PMID 6807:PMID 6755:PMID 6698:PMID 6649:PMID 6591:PMID 6537:PMID 6481:PMID 6421:PMID 6370:PMID 6327:PMID 6283:PMID 6226:PMID 6170:PMID 6146:2012 6116:PMID 6065:PMID 6013:PMID 5948:PMID 5905:PMID 5862:PMID 5827:PMID 5778:PMID 5728:PMID 5671:PMID 5622:PMID 5560:PMID 5501:PMID 5463:PMID 5405:PMID 5354:PMID 5245:PMID 5202:PMID 5166:PMID 5109:PMID 5073:PMID 5032:PMID 4989:PMID 4941:PMID 4898:PMID 4835:PMID 4767:PMID 4709:PMID 4658:PMID 4648:ISBN 4617:PMID 4582:PMID 4544:PMID 4498:PMID 4446:PMID 4394:PMID 4359:PMID 4297:PMID 4253:PMID 4215:ISBN 4188:PMID 4148:PMID 4086:PMID 4043:PMID 3997:PMID 3962:PMID 3911:PMID 3862:PMID 3813:PMID 3746:PMID 3711:PMID 3667:PMID 3608:PMID 3548:PMID 3499:PMID 3441:PMID 3423:Cell 3400:PMID 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