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detectable on the RNA level. These altered expression profiles are potentially the result of changes in the gut environment in patients with IBD, which include increased levels of inflammation, higher concentrations of oxygen and a diminished mucous layer. Metatranscriptomics has the advantage of allowing researchers to skip the assaying of biochemical products in situ (like mucus or oxygen) and enables evaluation of effects of environmental changes on microbial expression patterns in vivo for large human populations. In addition, it can be coupled with
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learnt about the microbiome community in the last years, the wide diversity of microorganisms and molecules in the gut requires new tools to enable new discoveries. By focusing on changes in the expression of the genes, metatrascriptomics can generate a more dynamic picture of the state and activity of the microbiome than metagenomics. It has been observed that metatranscriptomic functional profiles are more variable than what might have been reckoned only by metagenomic information. This suggests that non-housekeeping genes are not stably expressed
302:, is demonstrably well-suited for quantification of basal transcriptional activity of microbial community members. Depending on environmental conditions, the number of transcripts per cell varies for most genes. An exception to this are housekeeping genes that are expressed constitutively and with low variability under different conditions. Thus, the abundance of transcripts from such genes strongly correlate with the abundance of active cells in a community.
234:(HMP), implementing a “tiered search” approach. In the first tier, HUMAnN2 screens DNA or RNA reads with MetaPhlAn2 in order to identify already-known microbes and constructing a sample-specific database by merging pangenomes of annotated species; in the second tier, the algorithm performs a mapping of the reads against the assembled pangenome database; in the third tier, non-aligned reads are used for a translated search against a protein database.
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reads). For the taxonomic analysis, sequences are mapped against 16S rRNA Greengenes v13.5 database using SOAP2, while for functional analysis sequences are mapped against a functional database such as MetaHIT-2014 always by using SOAP2 tool. This pipeline is highly flexible, since it offers the possibility to use third-party tools and improve single modules as long as the general structure is preserved.
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more pronounced variations in patient with IBD. The functional potential of an organism, meaning the genes and pathways encoded in its genome, provides only indirect information about the level or extent of activation of such functions. So, the measurement of functional activity (gene expression) is critical to understand the mechanism of the gut microbiome dysbiosis.
110:(different extraction methods for different kinds of samples have been reported in the literature), mRNA enrichment, cDNA synthesis and preparation of metatranscriptomic libraries, sequencing and data processing and analysis. mRNA enrichment is one of the most technically challenging steps, for which different strategies have been proposed:
314:. In particular, microarrays have been used to measure microbial transcription levels, to detect new transcripts and to obtain information about the structure of mRNAs (for instance, the UTR boundaries). Recently, it has also been used to find new regulatory ncRNA. However, microarrays are affected by some pitfalls:
208:
A quantitative pipeline for transcriptomic analysis was developed by Li and Dewey and called RSEM (RNA-Seq by
Expectation Maximization). It can work as stand-alone software or as a plug-in for Trinity. RSEM starts with a reference transcriptome or assembly along with RNA-Seq reads generated from the
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A metatranscriptomics analysis measuring the functional activity of the gut microbiome reveals insights only partially observable in metagenomic functional potential, including disease-linked observations for IBD. It has been reported that many IBD-specific signals are either more pronounced or only
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in patients with IBD but microbial taxonomic profiles can be highly different among patients, making it difficult to implicate specific microbial species or strains in disease onset and progression. In addition, the gut microbiome composition presents a high variability over time among people, with
221:
The use of computational analysis tools has become more important as DNA sequencing capabilities have grown, particularly in metagenomic and metatranscriptomic analysis, which can generate a huge volume of data. Many different bioinformatic pipelines have been developed for these purposes, often as
276:
This pipeline does not have an official name and is usually referred to using the first author of the article in which it is described. This algorithm foresees the implementation of alignment tools such as BLAST and MegaBLAST. Reads are clustered in groups of identical sequences and then processed
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server for metagenomics. This pipeline is simple to use, requires low technical preparation and computational power and can be applied to a wide range of microbes. First, sequences from raw sequencing data are filtered for quality and then submitted to MG-RAST (which performs further steps such as
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has emerged in recent years as an important player in human health. Its prevalent functions are related to the fermentation of indigestible food components, competitions with pathogen, strengthening of the intestinal barrier, stimulation and regulation of the immune system. Although much has been
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for taxonomy and mRNA for gene expression levels. The pipeline is divided in 4 major steps. Firstly, paired-end reads are filtered for quality control purposes, then sorted and filtered for taxonomic analysis (by removal of tRNA sequences) or functional analysis (by removal of both tRNA and rRNA
105:
are the preferred techniques in metatranscriptomics. The protocol that is used to perform a metatranscriptome analysis may vary depending on the type of sample that needs to be analysed. Indeed, many different protocols have been developed for studying the metatranscriptome of microbial samples.
54:
has been defined as a microbial community occupying a well-defined habitat. These communities are ubiquitous and can play a key role in maintenance of the characteristics of their environment, and an imbalance in these communities can negatively affect the activities of the setting in which they
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sequences. Remaining reads are then mapped to NCBI databases using BLAST and MegaBLAST, then classified by their bitscore. Sequences with higher bitscores are used to predict phylogenetic origin and function, and lower-score reads are aligned with the more sensitive BLASTX and eventually can be
423:
to associate modulation of activity with the disease progression. Indeed, it has been shown that while a particular path may remain stable over time at the genomic level, the corresponding expression varies with the disease severity. This suggests that microbial dysbiosis affect the gut health
205:, in comparison with other de novo transcriptome assemblers, was reported to recover more full-length transcripts over a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. This is particularly important in the absence of a reference genome.
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Dual RNA-Seq: this technique allows the simultaneous study of both host and pathogen transcriptomes as well. It is possible to monitor the expression of genes at different time points of the infection process; in this way could it be possible to study the changes in cellular networks in both
37:
focuses on studying the genomic content and on identifying which microbes are present within a community, metatranscriptomics can be used to study the diversity of the active genes within such community, to quantify their expression levels and to monitor how these levels change in different
328:
RNA-Seq can overcome these limitations: it does not require any previous knowledge about the genomes that have to be analysed and it provides high throughput validation of genes prediction, structure, expression. Thus, by combining the two approaches it is possible to have a more complete
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Experimental issues can affect the quantification of differences in expression among multiple samples: They can influence integrity and input RNA, as well as the amount of rRNA remaining in the samples, size section and gene models. Moreover, molecular base techniques are very prone to
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Examples of techniques applied: Microarrays: allow the monitoring of changes in the expression levels of many genes in parallel for both host and pathogen. First microarray approaches have shown the first global analysis of gene expression changes in pathogens such as
404:(IBD) is a group of chronic diseases of the digestive tract that affects millions of people worldwide. Several human genetic mutations have been linked to an increased susceptibility to IBD, but additional factors are needed for the full development of the disease.
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Generally, large populations of cells are exploited in metatranscriptomic analysis, so it is difficult to resolve important variances that can exist between subpopulations. High variability in pathogen populations was demonstrated to affect disease progression and
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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).
76:
Since metatranscriptomics focuses on what genes are expressed, it enables the characterization of the active functional profile of the entire microbial community. The overview of the gene expression in a given sample is obtained by capturing the total
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The first strategy maps reads to reference genomes in databases, to collect information that is useful to deduce the relative expression of the single genes. Metatranscriptomic reads are mapped against databases using alignment tools, such as
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Require the pathogen and host cells to be physically separated before gene expression analysis (eukaryotic cells’ transcriptomes are larger in comparison to the pathogens’ ones, so could happen that the signal from pathogens’ RNAs is
212:
Although both
Trinity and RSEM were designed for transcriptomic datasets (i.e., obtained from a single organism), it may be possible to apply them to metatranscriptomic data (i.e., obtained from a whole microbial community).
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Identify host-specific biological processes and interactions For this purpose, it's important to develop new technologies which allow the detection, at the same time, of changes in the expression levels of some
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profile by analysing which genes are expressed by the community. It is possible to infer what genes are expressed under specific conditions, and this can be done using functional annotations of expressed genes.
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Some technical limitations of the RNA measurements in stool are related to the fact that the extracted RNA can be degraded and, if not, it still represents only the organisms presents in the stool sample.
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Directed culturing: has been used to understand nutritional preferences of organisms in order to allow the preparation of a proper culture medium, resulting in a successful isolation of microbes in vitro.
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Difficulties in differentiating between host and microbial RNA, although commercial kits for microbial enrichment are available. This may also be done in silico if a reference genome is available for the
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Alterations in transcriptional activity in IBD, established on the rRNA expression, indicate that some bacterial populations are active in patients with IBD, while other groups are inactive or latent.
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through changing in the transcriptional programmes in a stable community. In this way, metatranscriptomic profiling emerges as an important tool for understanding the mechanisms of that relationship.
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Halfvarson J, Brislawn CJ, Lamendella R, Vázquez-Baeza Y, Walters WA, Bramer LM, D'Amato M, Bonfiglio F, McDonald D, Gonzalez A, McClure EE, Dunklebarger MF, Knight R, Jansson JK (February 2017).
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conditions (e.g., physiological vs. pathological conditions in an organism). The advantage of metatranscriptomics is that it can provide information about differences in the active functions of
477:, as the effects of bacterial infection on the expression of various host factor. Anyway, the detection through microarrays of both organisms at the same time could be problematic. Problems:
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Despite the increasing sensitivity and depth of sequencing now available, there are still few published RNA-Seq studies concerning the response of the mammalian host cell to the infection.
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Identify potential virulence factors: through comparative transcriptomics, in order to compare different transcriptional responses of related strains or species after specific stimuli.
469:, revealing the strategies that are used by these microorganisms to adapt to the host. In addition, microarrays only provide the first global insights about the host
1939:
Franzosa EA, Morgan XC, Segata N, Waldron L, Reyes J, Earl AM, Giannoukos G, Boylan MR, Ciulla D, Gevers D, Izard J, Garrett WS, Chan AT, Huttenhower C (June 2014).
757:
Dumont MG, Pommerenke B, Casper P (October 2013). "Using stable isotope probing to obtain a targeted metatranscriptome of aerobic methanotrophs in lake sediment".
1600:
LeBlanc JG, Milani C, de Giori GS, Sesma F, van
Sinderen D, Ventura M (April 2013). "Bacteria as vitamin suppliers to their host: a gut microbiota perspective".
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With its dominating abundance, ribosomal RNA strongly reduces the coverage of mRNA (usually the main focus of transcriptomic studies) in the total collected RNA.
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Both for microarray and RNA-Seq, it is difficult to set a real threshold to classify genes as “expressed”, due to the high dynamic range in gene expression.
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The second strategy retrieves the abundance in the expression of the different genes by assembling metatranscriptomic reads into longer fragments called
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sample and calculates normalized transcript abundance (meaning the number of RNA-Seq reads cor-responding to each reference transcriptome or assembly).
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Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, Lipson KS, Knight R, Caporaso JG, Segata N, Huttenhower C (November 2018).
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sequences and use of sBLAT on each cluster to detect the best matches). Matches are then aggregated for taxonomic and functional analysis purposes.
1737:
Abreu MT (February 2010). "Toll-like receptor signalling in the intestinal epithelium: how bacterial recognition shapes intestinal function".
182:. The final analysis of the results is carried out depending on the aim of the study. One of the latest metatranscriptomics techniques is
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Leimena MM, Ramiro-Garcia J, Davids M, van den Bogert B, Smidt H, Smid EJ, Boekhorst J, Zoetendal EG, Schaap PJ, Kleerebezem M (2013).
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Moreover, RNA-Seq is an important approach for identifying coregulated genes, enabling the organization of pathogen genomes into
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microbes in lake sediment. The limitation of this strategy is its reliance on the information of reference genomes in databases.
474:
1090:"Functional signatures of oral dysbiosis during periodontitis progression revealed by microbial metatranscriptome analysis"
1039:"Functional signatures of oral dysbiosis during periodontitis progression revealed by microbial metatranscriptome analysis"
895:"De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis"
2340:
988:"A comprehensive metatranscriptome analysis pipeline and its validation using human small intestine microbiota datasets"
198:
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Gosalbes MJ, Durbán A, Pignatelli M, Abellan JJ, Jiménez-Hernández N, Pérez-Cobas AE, Latorre A, Moya A (March 2011).
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2095:"Inflammation, Antibiotics, and Diet as Environmental Stressors of the Gut Microbiome in Pediatric Crohn's Disease"
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One example of metatranscriptomic application is in the study of the gut microbiome in inflammatory bowel disease.
55:
reside. To study these communities, and to then determine their impact and correlation with their niche, different
1694:
Kamada N, Seo SU, Chen GY, Núñez G (May 2013). "Role of the gut microbiota in immunity and inflammatory disease".
1302:"Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes"
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This pipeline is designed specifically for metatranscriptomics data analysis, by working in conjunction with the
2144:"Transcriptional activity of the dominant gut mucosal microbiota in chronic inflammatory bowel disease patients"
1555:
Karasov WH, MartĂnez del Rio C, Caviedes-Vidal E (2011). "Ecological physiology of diet and digestive systems".
163:
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Saliba AE, C Santos S, Vogel J (February 2017). "New RNA-seq approaches for the study of bacterial pathogens".
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HUMAnN2 is a bioinformatic pipeline designed from the previous HUMAnN software, which was developed during the
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Probe-independent approach (RNA-seq provides transcript information without prior knowledge of mRNA sequences)
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Extraction of high-quality RNA from some biological or environmental samples (such as feces) can be difficult.
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open source platforms such as HUMAnN and the more recent HUMAnN2, MetaTrans, SAMSA, Leimena-2013 and mOTUs2.
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organisms starting from the initial contact until the manipulation of the host (interplay host-patogen).
102:
1390:
Martinez X, Pozuelo M, Pascal V, Campos D, Gut I, Gut M, Azpiroz F, Guarner F, Manichanh C (May 2016).
1243:"Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing"
401:
98:
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Sommer F, Bäckhed F (April 2013). "The gut microbiota--masters of host development and physiology".
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Xiong X, Frank DN, Robertson CE, Hung SS, Markle J, Canty AJ, McCoy KD, Macpherson AJ, Poussier P,
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The presence of mRNA is not always associated with the actual presence of the respective protein.
1141:"Community-wide transcriptome of the oral microbiome in subjects with and without periodontitis"
843:"Rsem: accurate transcript quantification from RNA-seq data with or without a reference genome"
183:
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Possibility of studying the expression levels of even unknown genes under different conditions
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2093:
Lewis JD, Chen EZ, Baldassano RN, Otley AR, Griffiths AM, Lee D, et al. (October 2015).
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Duran-Pinedo AE, Chen T, Teles R, Starr JR, Wang X, Krishnan K, Frias-Lopez J (August 2014).
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The last two strategies are not recommended as they have been reported to be highly biased.
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Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. (May 2011).
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Regarding the relationship between IBD and gut microbiome, it is known that there is a
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can be exploited to determine the gene expression profiles of some model organisms,
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2016:
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1647:"The gut microbiota: a major player in the toxicity of environmental pollutants?"
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1267:
1238:
794:"Full-length transcriptome assembly from RNA-Seq data without a reference genome"
524:. Indeed, genome annotation has been done for some eukaryotic pathogens, such as
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2110:
1945:
Proceedings of the
National Academy of Sciences of the United States of America
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1523:
744:
Field
Guidelines for Genetic Experimental Designs in High-Throughput Sequencing
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performs de novo assembly of the reads into transcript contigs and supercontigs
107:
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Rehman A, Lepage P, Nolte A, Hellmig S, Schreiber S, Ott SJ (September 2010).
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approaches have been used. While metagenomics can help researchers generate a
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1037:
Yost S, Duran-Pinedo AE, Teles R, Krishnan K, Frias-Lopez J (December 2015).
1004:
859:
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Instability of mRNA that compromises sample integrity even before sequencing.
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to improve efficiency. Data is obtained from paired-end RNA-Seq, mainly from
171:
115:
1965:
1882:"Metatranscriptomic approach to analyze the functional human gut microbiota"
1848:
1500:"Microbial abundance, activity and population genomic profiling with mOTUs2"
1190:"Metatranscriptomics of the human oral microbiome during health and disease"
770:
699:
Apirion D, Miczak A (February 1993). "RNA processing in prokaryotic cells".
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Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M (April 2014).
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186:(SIP), which has been used to retrieve specific targeted transcriptomes of
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34:
1343:"Species-level functional profiling of metagenomes and metatranscriptomes"
1205:
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720:
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Filiatrault MJ (October 2011). "Progress in prokaryotic transcriptomics".
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aligned in protein databases so that their function can be characterized.
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Another method that can be exploited for metatranscriptomic purposes is
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809:
668:"Metatranscriptomics: eavesdropping on complex microbial communities"
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Need of expensive chips (with the proper design; high-density arrays)
408:
360:
2185:"Interaction of microbes with mucus and mucins: recent developments"
2046:"Dynamics of the human gut microbiome in inflammatory bowel disease"
1750:
1707:
1088:
Yost S, Duran-Pinedo AE, Teles R, Krishnan K, Frias-Lopez J (2015).
1300:
Niu SY, Yang J, McDermaid A, Zhao J, Kang Y, Ma Q (November 2018).
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using a 5-3 exonuclease to degrade processed RNAs (mostly rRNA and
27:
597:
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1941:"Relating the metatranscriptome and metagenome of the human gut"
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Transcriptome reference databases are limited in their coverage.
1879:
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De Bona F, Ossowski S, Schneeberger K, Rätsch G (August 2008).
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655:. Manchester, UK: Manchester University Press. pp. 161–87.
521:
194:
1087:
1036:
495:
56:
1449:"SAMSA: a comprehensive metatranscriptome analysis pipeline"
1392:"MetaTrans: an open-source pipeline for metatranscriptomics"
1997:
1938:
1825:"Interactions between the microbiota and the immune system"
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Westreich ST, Korf I, Mills DA, Lemay DG (September 2016).
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170:. Then, the results are annotated using resources, such as
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of the microbiome and performing whole-metatranscriptomics
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Probe selection (hundreds of millions of different probes)
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within natural environments, i.e., the metatranscriptome.
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using antibodies to capture mRNAs that bind to specific
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adding poly(A) to mRNAs by using a polyA polymerase (in
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Burisch J, Jess T, Martinato M, Lakatos PL (May 2013).
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profile of the sample, metatranscriptomics provides a
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that would otherwise appear to have similar make-up.
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Westermann AJ, Gorski SA, Vogel J (September 2012).
2000:"The burden of inflammatory bowel disease in Europe"
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944:"Optimal spliced alignments of short sequence reads"
2278:
604:"Use of Metatranscriptomics in Microbiome Research"
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1823:Hooper LV, Littman DR, Macpherson AJ (June 2012).
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494:Loss of RNA molecules during the eukaryotic cells
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2183:Naughton J, Duggan G, Bourke B, Clyne M (2014).
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106:Generally, the steps include sample harvesting,
16:Techniques used to study microbe gene expression
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741:
549:
150:A typical metatranscriptome analysis pipeline:
1645:Claus SP, Guillou H, Ellero-Simatos S (2016).
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264:quality control, gene calling, clustering of
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653:Mycoparasitism and Plant Disease Control
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22:is the set of techniques used to study
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298:profiler, which is based on essential
242:MetaTrans is a pipeline that exploits
1736:
1569:10.1146/annurev-physiol-012110-142152
665:
651:Whipps JM, Lewis K, Cooke RC (1988).
507:Potential: No need of expensive chips
598:Bashiardes S, Zilberman-Schapira G,
154:maps reads to a reference genome, or
2234:"Dual RNA-seq of pathogen and host"
608:Bioinformatics and Biology Insights
13:
759:Environmental Microbiology Reports
14:
2362:
216:
2004:Journal of Crohn's & Colitis
1602:Current Opinion in Biotechnology
324:prior knowledge of gene targets.
2281:Current Opinion in Microbiology
2225:
2148:Journal of Medical Microbiology
1491:
1440:
1383:
1334:
1293:
1030:
565:Current Opinion in Microbiology
373:
271:
45:
1498:Milanese, et al. (2019).
834:
735:
659:
644:
332:
305:
197:using different software. The
1:
961:10.1093/bioinformatics/btn300
746:. Springer. pp. 313–342.
542:
118:through Ribosomal RNA capture
2017:10.1016/j.crohns.2013.01.010
1907:10.1371/journal.pone.0017447
1782:Nature Reviews. Microbiology
1651:npj Biofilms and Microbiomes
1614:10.1016/j.copbio.2012.08.005
1268:10.1371/journal.pone.0036009
237:
7:
2238:Nature Reviews Microbiology
1557:Annual Review of Physiology
1306:Briefings in Bioinformatics
318:requirement of probe design
103:third-generation sequencing
71:
10:
2367:
2341:Environmental microbiology
2111:10.1016/j.chom.2015.09.008
1739:Nature Reviews. Immunology
1696:Nature Reviews. Immunology
1663:10.1038/npjbiofilms.2016.3
1524:10.1038/s41467-019-08844-4
402:Inflammatory bowel disease
382:
225:
99:next-generation sequencing
2293:10.1016/j.mib.2017.01.001
2062:10.1038/nmicrobiol.2017.4
1466:10.1186/s12859-016-1270-8
1359:10.1038/s41592-018-0176-y
1107:10.1186/s13073-015-0153-3
1056:10.1186/s13073-015-0153-3
577:10.1016/j.mib.2011.07.023
289:
277:for in-silico removal of
1005:10.1186/1471-2164-14-530
860:10.1186/1471-2105-12-323
431:
254:
232:Human Microbiome Project
2346:Microbiology techniques
2099:Cell Host & Microbe
1966:10.1073/pnas.1319284111
1849:10.1126/science.1223490
841:Li B, Dewey CN (2011).
771:10.1111/1758-2229.12078
685:10.1128/microbe.4.329.1
2161:10.1099/jmm.0.021170-0
1241:, Parkinson J (2012).
911:10.1038/nprot.2013.084
713:10.1002/bies.950150207
471:innate immune response
184:stable isotope probing
146:Computational analysis
1504:Nature Communications
1206:10.1128/mBio.01012-14
1157:10.1038/ismej.2014.23
534:Plasmodium falciparum
459:Chlamydia trachomatis
421:longitudinal sampling
40:microbial communities
798:Nature Biotechnology
463:Chlamydia pneumoniae
455:Borrelia burgdorferi
379:Human gut microbiome
89:Tools and techniques
2250:10.1038/nrmicro2852
2050:Nature Microbiology
1957:2014PNAS..111E2329F
1898:2011PLoSO...617447G
1841:2012Sci...336.1268H
1794:10.1038/nrmicro2974
1516:2019NatCo..10.1014M
1408:2016NatSR...626447M
1259:2012PLoSO...736009X
484:Cross-hybridization
467:Salmonella enterica
20:Metatranscriptomics
2201:10.4161/gmic.26680
1453:BMC Bioinformatics
1396:Scientific Reports
1319:10.1093/bib/bbx051
847:BMC Bioinformatics
620:10.4137/BBI.S34610
530:Trypanosoma brucei
312:tiling microarrays
300:housekeeping genes
83:shotgun sequencing
2154:(Pt 9): 1114–22.
1835:(6086): 1268–73.
1416:10.1038/srep26447
666:Moran MA (2009).
513:High sensitivity.
2358:
2315:
2314:
2304:
2276:
2270:
2269:
2229:
2223:
2222:
2212:
2180:
2174:
2173:
2163:
2139:
2133:
2132:
2122:
2090:
2084:
2083:
2073:
2041:
2030:
2029:
2019:
1995:
1989:
1988:
1978:
1968:
1951:(22): E2329–38.
1936:
1930:
1929:
1919:
1909:
1877:
1871:
1870:
1860:
1820:
1814:
1813:
1777:
1771:
1770:
1734:
1728:
1727:
1691:
1685:
1684:
1674:
1642:
1636:
1635:
1625:
1597:
1591:
1590:
1580:
1552:
1546:
1545:
1535:
1495:
1489:
1488:
1478:
1468:
1444:
1438:
1437:
1427:
1387:
1381:
1380:
1370:
1338:
1332:
1331:
1321:
1312:(6): 1415–1429.
1297:
1291:
1290:
1280:
1270:
1234:
1228:
1227:
1217:
1200:(2): e01012–14.
1185:
1179:
1178:
1168:
1145:The ISME Journal
1136:
1130:
1129:
1119:
1109:
1085:
1079:
1078:
1068:
1058:
1034:
1028:
1027:
1017:
1007:
983:
974:
973:
963:
939:
933:
932:
922:
899:Nature Protocols
889:
883:
882:
872:
862:
838:
832:
831:
821:
810:10.1038/nbt.1883
789:
783:
782:
754:
748:
747:
739:
733:
732:
696:
690:
689:
687:
672:Microbe Magazine
663:
657:
656:
648:
642:
641:
631:
595:
589:
588:
560:
526:Candida albicans
199:Trinity software
2366:
2365:
2361:
2360:
2359:
2357:
2356:
2355:
2321:
2320:
2319:
2318:
2277:
2273:
2230:
2226:
2181:
2177:
2140:
2136:
2091:
2087:
2042:
2033:
1996:
1992:
1937:
1933:
1878:
1874:
1821:
1817:
1778:
1774:
1751:10.1038/nri2707
1735:
1731:
1708:10.1038/nri3430
1692:
1688:
1643:
1639:
1598:
1594:
1553:
1549:
1496:
1492:
1445:
1441:
1388:
1384:
1353:(11): 962–968.
1339:
1335:
1298:
1294:
1235:
1231:
1186:
1182:
1137:
1133:
1094:Genome Medicine
1086:
1082:
1043:Genome Medicine
1035:
1031:
984:
977:
954:(16): i174–80.
940:
936:
905:(8): 1494–512.
890:
886:
839:
835:
790:
786:
755:
751:
740:
736:
697:
693:
664:
660:
649:
645:
596:
592:
561:
550:
545:
451:Vibrio cholerae
434:
387:
381:
376:
335:
321:low sensitivity
308:
292:
274:
257:
240:
228:
219:
148:
91:
74:
48:
17:
12:
11:
5:
2364:
2354:
2353:
2348:
2343:
2338:
2333:
2331:Bioinformatics
2317:
2316:
2271:
2244:(9): 618–630.
2224:
2175:
2134:
2105:(4): 489–500.
2085:
2031:
1990:
1931:
1872:
1815:
1772:
1729:
1686:
1637:
1592:
1547:
1490:
1439:
1382:
1347:Nature Methods
1333:
1292:
1229:
1180:
1151:(8): 1659–72.
1131:
1080:
1029:
975:
948:Bioinformatics
934:
884:
833:
784:
749:
734:
691:
658:
643:
590:
547:
546:
544:
541:
518:
517:
514:
511:
508:
500:
499:
492:
488:
485:
482:
446:
445:
441:
438:
433:
430:
391:gut microbiome
385:Gut microbiota
380:
377:
375:
372:
371:
370:
367:
364:
356:
353:
349:
345:
342:
339:
334:
331:
326:
325:
322:
319:
307:
304:
291:
288:
273:
270:
256:
253:
244:multithreading
239:
236:
227:
224:
218:
217:Bioinformatics
215:
159:
158:
155:
147:
144:
140:
139:
133:
126:
119:
108:RNA extraction
90:
87:
73:
70:
47:
44:
26:expression of
15:
9:
6:
4:
3:
2:
2363:
2352:
2349:
2347:
2344:
2342:
2339:
2337:
2334:
2332:
2329:
2328:
2326:
2312:
2308:
2303:
2298:
2294:
2290:
2286:
2282:
2275:
2267:
2263:
2259:
2255:
2251:
2247:
2243:
2239:
2235:
2228:
2220:
2216:
2211:
2206:
2202:
2198:
2194:
2190:
2186:
2179:
2171:
2167:
2162:
2157:
2153:
2149:
2145:
2138:
2130:
2126:
2121:
2116:
2112:
2108:
2104:
2100:
2096:
2089:
2081:
2077:
2072:
2067:
2063:
2059:
2055:
2051:
2047:
2040:
2038:
2036:
2027:
2023:
2018:
2013:
2010:(4): 322–37.
2009:
2005:
2001:
1994:
1986:
1982:
1977:
1972:
1967:
1962:
1958:
1954:
1950:
1946:
1942:
1935:
1927:
1923:
1918:
1913:
1908:
1903:
1899:
1895:
1892:(3): e17447.
1891:
1887:
1883:
1876:
1868:
1864:
1859:
1854:
1850:
1846:
1842:
1838:
1834:
1830:
1826:
1819:
1811:
1807:
1803:
1799:
1795:
1791:
1788:(4): 227–38.
1787:
1783:
1776:
1768:
1764:
1760:
1756:
1752:
1748:
1745:(2): 131–44.
1744:
1740:
1733:
1725:
1721:
1717:
1713:
1709:
1705:
1702:(5): 321–35.
1701:
1697:
1690:
1682:
1678:
1673:
1668:
1664:
1660:
1656:
1652:
1648:
1641:
1633:
1629:
1624:
1619:
1615:
1611:
1607:
1603:
1596:
1588:
1584:
1579:
1574:
1570:
1566:
1562:
1558:
1551:
1543:
1539:
1534:
1529:
1525:
1521:
1517:
1513:
1509:
1505:
1501:
1494:
1486:
1482:
1477:
1472:
1467:
1462:
1458:
1454:
1450:
1443:
1435:
1431:
1426:
1421:
1417:
1413:
1409:
1405:
1401:
1397:
1393:
1386:
1378:
1374:
1369:
1364:
1360:
1356:
1352:
1348:
1344:
1337:
1329:
1325:
1320:
1315:
1311:
1307:
1303:
1296:
1288:
1284:
1279:
1274:
1269:
1264:
1260:
1256:
1253:(4): e36009.
1252:
1248:
1244:
1240:
1233:
1225:
1221:
1216:
1211:
1207:
1203:
1199:
1195:
1191:
1184:
1176:
1172:
1167:
1162:
1158:
1154:
1150:
1146:
1142:
1135:
1127:
1123:
1118:
1113:
1108:
1103:
1099:
1095:
1091:
1084:
1076:
1072:
1067:
1062:
1057:
1052:
1048:
1044:
1040:
1033:
1025:
1021:
1016:
1011:
1006:
1001:
997:
993:
989:
982:
980:
971:
967:
962:
957:
953:
949:
945:
938:
930:
926:
921:
916:
912:
908:
904:
900:
896:
888:
880:
876:
871:
866:
861:
856:
852:
848:
844:
837:
829:
825:
820:
815:
811:
807:
804:(7): 644–52.
803:
799:
795:
788:
780:
776:
772:
768:
765:(5): 757–64.
764:
760:
753:
745:
738:
730:
726:
722:
718:
714:
710:
707:(2): 113–20.
706:
702:
695:
686:
681:
678:(7): 329–34.
677:
673:
669:
662:
654:
647:
639:
635:
630:
625:
621:
617:
613:
609:
605:
601:
594:
586:
582:
578:
574:
571:(5): 579–86.
570:
566:
559:
557:
555:
553:
548:
540:
537:
535:
531:
527:
523:
515:
512:
509:
506:
505:
504:
497:
493:
489:
486:
483:
480:
479:
478:
476:
472:
468:
464:
460:
456:
452:
442:
439:
436:
435:
429:
425:
422:
416:
413:
410:
405:
403:
398:
397:
392:
386:
368:
365:
362:
357:
354:
350:
346:
343:
340:
337:
336:
330:
323:
320:
317:
316:
315:
313:
303:
301:
297:
287:
284:
280:
269:
267:
262:
252:
249:
245:
235:
233:
223:
214:
210:
206:
204:
200:
196:
191:
189:
185:
181:
177:
173:
169:
165:
156:
153:
152:
151:
143:
138:
134:
131:
127:
124:
120:
117:
113:
112:
111:
109:
104:
100:
96:
86:
84:
80:
69:
66:
62:
58:
53:
43:
41:
36:
31:
29:
25:
21:
2351:Metagenomics
2302:10033/621506
2284:
2280:
2274:
2241:
2237:
2227:
2195:(1): 48–52.
2192:
2189:Gut Microbes
2188:
2178:
2151:
2147:
2137:
2102:
2098:
2088:
2056:(5): 17004.
2053:
2049:
2007:
2003:
1993:
1948:
1944:
1934:
1889:
1885:
1875:
1832:
1828:
1818:
1785:
1781:
1775:
1742:
1738:
1732:
1699:
1695:
1689:
1654:
1650:
1640:
1608:(2): 160–8.
1605:
1601:
1595:
1560:
1556:
1550:
1507:
1503:
1493:
1456:
1452:
1442:
1399:
1395:
1385:
1350:
1346:
1336:
1309:
1305:
1295:
1250:
1246:
1232:
1197:
1193:
1183:
1148:
1144:
1134:
1097:
1093:
1083:
1046:
1042:
1032:
995:
992:BMC Genomics
991:
951:
947:
937:
902:
898:
887:
850:
846:
836:
801:
797:
787:
762:
758:
752:
743:
737:
704:
700:
694:
675:
671:
661:
652:
646:
611:
607:
593:
568:
564:
538:
519:
501:
447:
426:
417:
414:
406:
399:
388:
374:Applications
327:
309:
293:
275:
272:Leimena-2013
258:
241:
229:
220:
211:
207:
192:
160:
149:
141:
92:
75:
64:
60:
49:
46:Introduction
35:metagenomics
32:
19:
18:
1578:11336/14704
1510:(1): 1014.
333:Limitations
306:Microarrays
178:, COG, and
166:, BWA, and
95:microarrays
2325:Categories
1623:11336/2561
1459:(1): 399.
998:(1): 530.
853:(1): 323.
543:References
383:See also:
348:artefacts.
266:amino acid
180:Swiss-Prot
65:functional
52:microbiome
2287:: 78–87.
2266:205498287
1724:205491968
1657:: 16003.
1563:: 69–93.
1402:: 26447.
1239:Danska JS
1100:(1): 27.
1049:(1): 27.
701:BioEssays
614:: 19–25.
409:dysbiosis
361:virulence
238:MetaTrans
114:removing
93:Although
61:taxonomic
2336:Genomics
2311:28214646
2258:22890146
2219:24149677
2170:20522625
2129:26468751
2080:28191884
2026:23395397
1985:24843156
1926:21408168
1886:PLOS ONE
1867:22674334
1810:22798964
1802:23435359
1767:21789611
1759:20098461
1716:23618829
1681:28721242
1632:22940212
1587:21314432
1542:30833550
1485:27687690
1434:27211518
1377:30377376
1328:28481971
1287:22558305
1247:PLOS ONE
1224:24692635
1175:24599074
1126:25918553
1075:25918553
1024:23915218
970:18689821
929:23845962
879:21816040
828:21572440
779:24115627
729:42365781
638:27127406
602:(2016).
600:Elinav E
585:21839669
491:hidden).
137:proteins
72:Function
28:microbes
2210:4049936
2120:4633303
2071:5319707
1976:4050606
1953:Bibcode
1917:3050895
1894:Bibcode
1858:4420145
1837:Bibcode
1829:Science
1672:5515271
1533:6399450
1512:Bibcode
1476:5041328
1425:4876386
1404:Bibcode
1368:6235447
1278:3338770
1255:Bibcode
1215:3977359
1166:4817619
1117:4410737
1066:4410737
1015:3750648
920:3875132
870:3163565
819:3571712
721:7682412
629:4839964
522:operons
396:in situ
261:MG-RAST
248:16S RNA
226:HUMAnN2
203:RNA-seq
195:contigs
188:aerobic
164:Bowtie2
130:E. coli
2309:
2264:
2256:
2217:
2207:
2168:
2127:
2117:
2078:
2068:
2024:
1983:
1973:
1924:
1914:
1865:
1855:
1808:
1800:
1765:
1757:
1722:
1714:
1679:
1669:
1630:
1585:
1540:
1530:
1483:
1473:
1432:
1422:
1375:
1365:
1326:
1285:
1275:
1222:
1212:
1173:
1163:
1124:
1114:
1073:
1063:
1022:
1012:
968:
927:
917:
877:
867:
826:
816:
777:
727:
719:
636:
626:
583:
444:genes.
296:mOTUs2
290:mOTUs2
33:While
2262:S2CID
1806:S2CID
1763:S2CID
1720:S2CID
725:S2CID
496:lysis
475:PAMPs
432:Other
352:host.
255:SAMSA
168:BLAST
57:omics
2307:PMID
2254:PMID
2215:PMID
2166:PMID
2125:PMID
2076:PMID
2022:PMID
1981:PMID
1922:PMID
1863:PMID
1798:PMID
1755:PMID
1712:PMID
1677:PMID
1628:PMID
1583:PMID
1538:PMID
1481:PMID
1430:PMID
1373:PMID
1324:PMID
1283:PMID
1220:PMID
1194:mBio
1171:PMID
1122:PMID
1071:PMID
1020:PMID
966:PMID
925:PMID
875:PMID
824:PMID
775:PMID
717:PMID
634:PMID
581:PMID
532:and
465:and
389:The
294:The
283:rRNA
281:and
279:tRNA
201:for
176:KEGG
123:tRNA
116:rRNA
101:and
79:mRNA
50:The
24:gene
2297:hdl
2289:doi
2246:doi
2205:PMC
2197:doi
2156:doi
2115:PMC
2107:doi
2066:PMC
2058:doi
2012:doi
1971:PMC
1961:doi
1949:111
1912:PMC
1902:doi
1853:PMC
1845:doi
1833:336
1790:doi
1747:doi
1704:doi
1667:PMC
1659:doi
1618:hdl
1610:doi
1573:hdl
1565:doi
1528:PMC
1520:doi
1471:PMC
1461:doi
1420:PMC
1412:doi
1363:PMC
1355:doi
1314:doi
1273:PMC
1263:doi
1210:PMC
1202:doi
1161:PMC
1153:doi
1112:PMC
1102:doi
1061:PMC
1051:doi
1010:PMC
1000:doi
956:doi
915:PMC
907:doi
865:PMC
855:doi
814:PMC
806:doi
767:doi
709:doi
680:doi
624:PMC
616:doi
573:doi
473:to
2327::
2305:.
2295:.
2285:35
2283:.
2260:.
2252:.
2242:10
2240:.
2236:.
2213:.
2203:.
2191:.
2187:.
2164:.
2152:59
2150:.
2146:.
2123:.
2113:.
2103:18
2101:.
2097:.
2074:.
2064:.
2052:.
2048:.
2034:^
2020:.
2006:.
2002:.
1979:.
1969:.
1959:.
1947:.
1943:.
1920:.
1910:.
1900:.
1888:.
1884:.
1861:.
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