33:
1542:(PCA) which can efficiently reduce the dimensions of a dataset to a few which explain the greatest variation. When analyzed in the lower-dimensional PCA space, clustering of samples with similar metabolic fingerprints can be detected. PCA algorithms aim to replace all correlated variables with a much smaller number of uncorrelated variables (referred to as principal components (PCs)) and retain most of the information in the original dataset. This clustering can elucidate patterns and assist in the determination of disease biomarkers – metabolites that correlate most with class membership.
1084:-based techniques, this is simply because of usages amongst different groups that have popularized the different terms. While there is still no absolute agreement, there is a growing consensus that 'metabolomics' places a greater emphasis on metabolic profiling at a cellular or organ level and is primarily concerned with normal endogenous metabolism. 'Metabonomics' extends metabolic profiling to include information about perturbations of metabolism caused by environmental factors (including diet and toxins), disease processes, and the involvement of extragenomic influences, such as
1698:
microflora are also a very significant potential confounder of metabolic profiles and could be classified as either an endogenous or exogenous factor. The main exogenous factors are diet and drugs. Diet can then be broken down to nutrients and non-nutrients. Metabolomics is one means to determine a biological endpoint, or metabolic fingerprint, which reflects the balance of all these forces on an individual's metabolism. Thanks to recent cost reductions, metabolomics has now become accessible for companion animals, such as pregnant dogs.
1123:
221:. METLIN has since grown and as of December, 2023, METLIN contains MS/MS experimental data on over 930,000 molecular standards and other chemical entities, each compound having experimental tandem mass spectrometry data generated from molecular standards at multiple collision energies and in positive and negative ionization modes. METLIN is the largest repository of tandem mass spectrometry data of its kind. The dedicated academic journal Metabolomics first appeared in 2005, founded by its current editor-in-chief
1276:(SIMS) was one of the first matrix-free desorption/ionization approaches used to analyze metabolites from biological samples. SIMS uses a high-energy primary ion beam to desorb and generate secondary ions from a surface. The primary advantage of SIMS is its high spatial resolution (as small as 50 nm), a powerful characteristic for tissue imaging with MS. However, SIMS has yet to be readily applied to the analysis of biofluids and tissues because of its limited sensitivity at
5725:
937:
1728:
1714:
5713:
386:
1284:(DESI) is a matrix-free technique for analyzing biological samples that uses a charged solvent spray to desorb ions from a surface. Advantages of DESI are that no special surface is required and the analysis is performed at ambient pressure with full access to the sample during acquisition. A limitation of DESI is spatial resolution because "focusing" the charged solvent spray is difficult. However, a recent development termed
325:(HMDB) is perhaps the most extensive public metabolomic spectral database to date and is a freely available electronic database (www.hmdb.ca) containing detailed information about small molecule metabolites found in the human body. It is intended to be used for applications in metabolomics, clinical chemistry, biomarker discovery and general education. The database is designed to contain or link three kinds of data:
1182:(GC/FID) or a mass spectrometer (GC-MS). The method is especially useful for identification and quantification of small and volatile molecules. However, a practical limitation of GC is the requirement of chemical derivatization for many biomolecules as only volatile chemicals can be analysed without derivatization. In cases where greater resolving power is required, two-dimensional chromatography (
1742:
1261:
largely because of the limits imposed by the complexity of these samples, which contain thousands to tens of thousands of metabolites. Among the technologies being developed to address this challenge is
Nanostructure-Initiator MS (NIMS), a desorption/ ionization approach that does not require the application of matrix and thereby facilitates small-molecule (i.e., metabolite) identification.
1088:. This is not a trivial difference; metabolomic studies should, by definition, exclude metabolic contributions from extragenomic sources, because these are external to the system being studied. However, in practice, within the field of human disease research there is still a large degree of overlap in the way both terms are used, and they are often in effect synonymous.
280:
1524:
The data generated in metabolomics usually consist of measurements performed on subjects under various conditions. These measurements may be digitized spectra, or a list of metabolite features. In its simplest form, this generates a matrix with rows corresponding to subjects and columns corresponding
1683:
Metabologenomics is a novel approach to integrate metabolomics and genomics data by correlating microbial-exported metabolites with predicted biosynthetic genes. This bioinformatics-based pairing method enables natural product discovery at a larger-scale by refining non-targeted metabolomic analyses
1632:
caused by a genetic manipulation, such as gene deletion or insertion. Sometimes this can be a sufficient goal in itself—for instance, to detect any phenotypic changes in a genetically modified plant intended for human or animal consumption. More exciting is the prospect of predicting the function of
1298:
is the only detection technique which does not rely on separation of the analytes, and the sample can thus be recovered for further analyses. All kinds of small molecule metabolites can be measured simultaneously - in this sense, NMR is close to being a universal detector. The main advantages of NMR
1269:
that complicates analysis of the low-mass range (i.e., metabolites). In addition, the size of the resulting matrix crystals limits the spatial resolution that can be achieved in tissue imaging. Because of these limitations, several other matrix-free desorption/ionization approaches have been applied
1238:. MS is both sensitive and can be very specific. There are also a number of techniques which use MS as a stand-alone technology: the sample is infused directly into the mass spectrometer with no prior separation, and the MS provides sufficient selectivity to both separate and to detect metabolites.
1159:
Initially, analytes in a metabolomic sample comprise a highly complex mixture. This complex mixture can be simplified prior to detection by separating some analytes from others. Separation achieves various goals: analytes which cannot be resolved by the detector may be separated in this step; in MS
348:
Each type of cell and tissue has a unique metabolic ‘fingerprint’ that can elucidate organ or tissue-specific information. Bio-specimens used for metabolomics analysis include but not limit to plasma, serum, urine, saliva, feces, muscle, sweat, exhaled breath and gastrointestinal fluid. The ease of
344:
contains >16,000 endogenous metabolites, >1,500 drugs and >22,000 food constituents or food metabolites. This information, available at the Human
Metabolome Database and based on analysis of information available in the current scientific literature, is far from complete. In contrast, much
1701:
Plant metabolomics is designed to study the overall changes in metabolites of plant samples and then conduct deep data mining and chemometric analysis. Specialized metabolites are considered components of plant defense systems biosynthesized in response to biotic and abiotic stresses. Metabolomics
1260:
In the 2000s, surface-based mass analysis has seen a resurgence, with new MS technologies focused on increasing sensitivity, minimizing background, and reducing sample preparation. The ability to analyze metabolites directly from biofluids and tissues continues to challenge current MS technology,
1596:
is a powerful tool that can be used in metabolomics analysis. Recently, scientists have developed retention time prediction software. These tools allow researchers to apply artificial intelligence to the retention time prediction of small molecules in complex mixture, such as human plasma, plant
87:
being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are
1612:
by metabolic profiling (especially of urine or blood plasma samples) detects the physiological changes caused by toxic insult of a chemical (or mixture of chemicals). In many cases, the observed changes can be related to specific syndromes, e.g. a specific lesion in liver or kidney. This is of
339:
The database contains 220,945 metabolite entries including both water-soluble and lipid soluble metabolites. Additionally, 8,610 protein sequences (enzymes and transporters) are linked to these metabolite entries. Each MetaboCard entry contains 130 data fields with 2/3 of the information being
236:
and in an effort to address the issue of statistically identifying the most relevant dysregulated metabolites across hundreds of LC/MS datasets, the first algorithm was developed to allow for the nonlinear alignment of mass spectrometry metabolomics data. Called XCMS, it has since (2012) been
1529:
data. A great number of free software are already available for the analysis of metabolomics data shown in the table. Some statistical tools listed in the table were designed for NMR data analyses were also useful for MS data. For mass spectrometry data, software is available that identifies
151:, which was discovered in the 1940s, was also undergoing rapid advances. In 1974, Seeley et al. demonstrated the utility of using NMR to detect metabolites in unmodified biological samples. This first study on muscle highlighted the value of NMR in that it was determined that 90% of cellular
1697:
is a generalised term which links genomics, transcriptomics, proteomics and metabolomics to human nutrition. In general, in a given body fluid, a metabolome is influenced by endogenous factors such as age, sex, body composition and genetics as well as underlying pathologies. The large bowel
1130:
The typical workflow of metabolomics studies is shown in the figure. First, samples are collected from tissue, plasma, urine, saliva, cells, etc. Next, metabolites extracted often with the addition of internal standards and derivatization. During sample analysis, metabolites are quantified
1690:
is a further development of metabolomics. The disadvantage of metabolomics is that it only provides the user with abundances or concentrations of metabolites, while fluxomics determines the reaction rates of metabolic reactions and can trace metabolites in a biological system over time.
1245:(EI) is the most common ionization technique applied to GC separations as it is amenable to low pressures. EI also produces fragmentation of the analyte, both providing structural information while increasing the complexity of the data and possibly obscuring the molecular ion.
1206:(CE) has a higher theoretical separation efficiency than HPLC (although requiring much more time per separation), and is suitable for use with a wider range of metabolite classes than is GC. As for all electrophoretic techniques, it is most appropriate for charged analytes.
301:
refers to the complete set of small-molecule (<1.5 kDa) metabolites (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites) to be found within a biological sample, such as a single organism. The word was coined in analogy with
1200:, HPLC has lower chromatographic resolution, but requires no derivatization for polar molecules, and separates molecules in the liquid phase. Additionally HPLC has the advantage that a much wider range of analytes can be measured with a higher sensitivity than GC methods.
4284:
Habchi B, Alves S, Jouan-Rimbaud
Bouveresse D, Appenzeller B, Paris A, Rutledge DN, et al. (January 2018). "Potential of dynamically harmonized Fourier transform ion cyclotron resonance cell for high-throughput metabolomics fingerprinting: control of data quality".
1533:
Once metabolite data matrix is determined, unsupervised data reduction techniques (e.g. PCA) can be used to elucidate patterns and connections. In many studies, including those evaluating drug-toxicity and some disease models, the metabolites of interest are not known
1233:
was the first hyphenated technique to be developed. Identification leverages the distinct patterns in which analytes fragment. These patterns can be thought of as a mass spectral fingerprint. Libraries exist that allow identification of a metabolite according to this
310:; like the transcriptome and the proteome, the metabolome is dynamic, changing from second to second. Although the metabolome can be defined readily enough, it is not currently possible to analyse the entire range of metabolites by a single analytical method.
263:
As late as mid-2010, metabolomics was still considered an "emerging field". Further, it was noted that further progress in the field depended in large part, through addressing otherwise "irresolvable technical challenges", by technical evolution of
32:
1249:(APCI) is an atmospheric pressure technique that can be applied to all the above separation techniques. APCI is a gas phase ionization method, which provides slightly more aggressive ionization than ESI which is suitable for less polar compounds.
345:
more is known about the metabolomes of other organisms. For example, over 50,000 metabolites have been characterized from the plant kingdom, and many thousands of metabolites have been identified and/or characterized from single plants.
128:. However, it was only through technological advancements in the 1960s and 1970s that it became feasible to quantitatively (as opposed to qualitatively) measure metabolic profiles. The term "metabolic profile" was introduced by Horning,
3580:
Crockford DJ, Maher AD, Ahmadi KR, Barrett A, Plumb RS, Wilson ID, et al. (September 2008). "1H NMR and UPLC-MS(E) statistical heterospectroscopy: characterization of drug metabolites (xenometabolome) in epidemiological studies".
1075:
There has been some disagreement over the exact differences between 'metabolomics' and 'metabonomics'. The difference between the two terms is not related to choice of analytical platform: although metabonomics is more associated with
1004:-based metabolomics studies of blood plasma. In plant-based metabolomics, it is common to refer to "primary" and "secondary" metabolites. A primary metabolite is directly involved in the normal growth, development, and reproduction. A
3665:
Nicholson JK, Lindon JC, Holmes E (November 1999). "'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data".
1151:, PCA). Many bioinformatic tools and software are available to identify associations with disease states and outcomes, determine significant correlations, and characterize metabolic signatures with existing biological knowledge.
248:, completed the first draft of the human metabolome, consisting of a database of approximately 2,500 metabolites, 1,200 drugs and 3,500 food components. Similar projects have been underway in several plant species, most notably
4510:
Gromski PS, Muhamadali H, Ellis DI, Xu Y, Correa E, Turner ML, et al. (June 2015). "A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding".
1058:
Metabonomics is defined as "the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification". The word origin is from the Greek
1071:
and has been used in toxicology, disease diagnosis and a number of other fields. Historically, the metabonomics approach was one of the first methods to apply the scope of systems biology to studies of metabolism.
4240:
Beckonert O, Keun HC, Ebbels TM, Bundy J, Holmes E, Lindon JC, et al. (2007). "Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts".
1253:(ESI) is the most common ionization technique applied in LC/MS. This soft ionization is most successful for polar molecules with ionizable functional groups. Another commonly used soft ionization technique is
1702:
approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops.
5648:
1597:
extracts, foods, or microbial cultures. Retention time prediction increases the identification rate in liquid chromatography and can lead to an improved biological interpretation of metabolomics data.
2611:
Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G (February 2006). "XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification".
1905:
Villate A, San
Nicolas M, Gallastegi M, Aulas PA, Olivares M, Usobiaga A, et al. (February 2021). "Review: Metabolomics as a prediction tool for plants performance under environmental stress".
171:. In 1984, Nicholson showed H NMR spectroscopy could potentially be used to diagnose diabetes mellitus, and later pioneered the application of pattern recognition methods to NMR spectroscopic data.
3962:
1164:
is reduced; the retention time of the analyte serves as information regarding its identity. This separation step is not mandatory and is often omitted in NMR and "shotgun" based approaches such as
67:. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. The
1569:, etc. are received increasing attention for untargeted metabolomics data analysis. In the case of univariate methods, variables are analyzed one by one using classical statistics tools (such as
120:
The concept that individuals might have a "metabolic profile" that could be reflected in the makeup of their biological fluids was introduced by Roger
Williams in the late 1940s, who used
4205:
Griffin JL (October 2003). "Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis".
2314:
Holmes E, Antti H (December 2002). "Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra".
202:, was observed and later shown to have sleep inducing properties. This work is one of the earliest such experiments combining liquid chromatography and mass spectrometry in metabolomics.
2800:"Metabolomics reveals novel pathways and differential mechanistic and elicitor-specific responses in phenylpropanoid and isoflavonoid biosynthesis in Medicago truncatula cell cultures"
4936:
Cotrim GD, Silva DM, Graça JP, Oliveira Junior A, Castro C, Zocolo GJ, et al. (January 2023). "Glycine max (L.) Merr. (Soybean) metabolome responses to potassium availability".
2451:
Cravatt BF, Prospero-Garcia O, Siuzdak G, Gilula NB, Henriksen SJ, Boger DL, et al. (June 1995). "Chemical characterization of a family of brain lipids that induce sleep".
1161:
1102:
Exometabolomics, or "metabolic footprinting", is the study of extracellular metabolites. It uses many techniques from other subfields of metabolomics, and has applications in
1299:
are high analytical reproducibility and simplicity of sample preparation. Practically, however, it is relatively insensitive compared to mass spectrometry-based techniques.
3986:
Gika HG, Theodoridis GA, Wingate JE, Wilson ID (August 2007). "Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine".
961:
4619:
Saghatelian A, Trauger SA, Want EJ, Hawkins EG, Siuzdak G, Cravatt BF (November 2004). "Assignment of endogenous substrates to enzymes by global metabolite profiling".
159:, NMR continues to be a leading analytical tool to investigate metabolism. Recent efforts to utilize NMR for metabolomics have been largely driven by the laboratory of
2967:
Griffin JL, Vidal-Puig A (June 2008). "Current challenges in metabolomics for diabetes research: a vital functional genomic tool or just a ploy for gaining funding?".
1577:
or mixed models) and only these with sufficient small p-values are considered relevant. However, correction strategies should be used to reduce false discoveries when
4021:
Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T (September 2003). "Quantitative metabolome analysis using capillary electrophoresis mass spectrometry".
2212:
Hoult DI, Busby SJ, Gadian DG, Radda GK, Richards RE, Seeley PJ (November 1974). "Observation of tissue metabolites using 31P nuclear magnetic resonance".
1538:. This makes unsupervised methods, those with no prior assumptions of class membership, a popular first choice. The most common of these methods includes
349:
collection facilitates high temporal resolution, and because they are always at dynamic equilibrium with the body, they can describe the host as a whole.
4697:"Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer"
1178:), is a widely used separation technique for metabolomic analysis. GC offers very high chromatographic resolution, and can be used in conjunction with a
4156:"Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics"
4056:
Northen TR, Yanes O, Northen MT, Marrinucci D, Uritboonthai W, Apon J, et al. (October 2007). "Clathrate nanostructures for mass spectrometry".
3484:"Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum"
2847:
5114:
Ellis DI, Goodacre R (August 2006). "Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy".
104:), which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. One of the ultimate challenges of
1530:
molecules that vary in subject groups on the basis of mass-over-charge value and sometimes retention time depending on the experimental design.
71:
represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes.
4547:"Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing"
1147:
spectroscopy). The raw output data can be used for metabolite feature extraction and further processed before statistical analysis (such as
2494:
Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, et al. (December 2005). "METLIN: a metabolite mass spectral database".
5643:
3701:
Nicholson JK, Connelly J, Lindon JC, Holmes E (February 2002). "Metabonomics: a platform for studying drug toxicity and gene function".
3309:
Nicholson JK, Wilson ID (August 2003). "Opinion: understanding 'global' systems biology: metabonomics and the continuum of metabolism".
3412:
Nicholson JK, Foxall PJ, Spraul M, Farrant RD, Lindon JC (March 1995). "750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma".
2885:
1637:
by comparison with the metabolic perturbations caused by deletion/insertion of known genes. Such advances are most likely to come from
1581:
are conducted since there is no standard method for measuring the total amount of metabolites directly in untargeted metabolomics. For
4546:
4330:"Development of a rapid profiling method for the analysis of polar analytes in urine using HILIC-MS and ion mobility enabled HILIC-MS"
2586:
1303:
5049:
4109:"Nanostructure-initiator mass spectrometry: a protocol for preparing and applying NIMS surfaces for high-sensitivity mass analysis"
1246:
3533:"Sensitive mass spectrometric analysis of carbonyl metabolites in human urine and fecal samples using chemoselective modification"
4475:
Ren S, Hinzman AA, Kang EL, Szczesniak RD, Lu LJ (December 2015). "Computational and statistical analysis of metabolomics data".
1827:
1295:
1077:
992:
in size. However, there are exceptions to this depending on the sample and detection method. For example, macromolecules such as
148:
1684:
to identify small molecules with related biosynthesis and to focus on those that may not have previously well known structures.
1302:
Although NMR and MS are the most widely used modern-day techniques for detection, there are other methods in use. These include
1285:
1222:
1189:
164:
5685:
968:
136:(GC-MS) could be used to measure compounds present in human urine and tissue extracts. The Horning group, along with that of
1525:
with metabolite features (or vice versa). Several statistical programs are currently available for analysis of both NMR and
1175:
568:
133:
5085:
Ellis DI, Dunn WB, Griffin JL, Allwood JW, Goodacre R (September 2007). "Metabolic fingerprinting as a diagnostic tool".
674:
5370:
1281:
5269:
5246:
1254:
669:
340:
devoted to chemical/clinical data and the other 1/3 devoted to enzymatic or biochemical data. The version 3.5 of the
5294:
1554:
573:
155:
is complexed with magnesium. As sensitivity has improved with the evolution of higher magnetic field strengths and
5717:
5663:
2877:
1273:
1241:
For analysis by mass spectrometry, the analytes must be imparted with a charge and transferred to the gas phase.
3255:
Ulaszewska MM, Weinert CH, Trimigno A, Portmann R, Andres
Lacueva C, Badertscher R, et al. (January 2019).
2932:
Oliver SG, Winson MK, Kell DB, Baganz F (September 1998). "Systematic functional analysis of the yeast genome".
1855:"Metabolomic characterization of human rectal adenocarcinoma with intact tissue magnetic resonance spectroscopy"
754:
5019:
Bundy JG, Davey MP, Viant MR (2009). "Environmental metabolomics: A critical review and future perspectives".
5691:
5313:
1948:
Hollywood K, Brison DR, Goodacre R (September 2006). "Metabolomics: current technologies and future trends".
2537:
Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, et al. (March 2018).
5196:"MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data"
4887:"The metabolic differences of anestrus, heat, pregnancy, pseudopregnancy, and lactation in 800 female dogs"
4656:"An enzyme that regulates ether lipid signaling pathways in cancer annotated by multidimensional profiling"
2907:
1800:
1578:
1539:
1148:
853:
175:
3839:"Exometabolomics and MSI: deconstructing how cells interact to transform their small molecule environment"
1288:(LAESI) is a promising approach to circumvent this limitation. Most recently, ion trap techniques such as
5607:
1553:
are thriving methods for high-dimensional correlated metabolomics data, of which the most popular one is
507:
17:
1192:(HPLC) has emerged as the most common separation technique for metabolomic analysis. With the advent of
4695:
Goering AW, McClure RA, Doroghazi JR, Albright JC, Haverland NA, Zhang Y, et al. (February 2016).
2851:
2169:
Griffiths WJ, Wang Y (July 2009). "Mass spectrometry: from proteomics to metabolomics and lipidomics".
2078:
1670:
1144:
1001:
480:
124:
to suggest characteristic metabolic patterns in urine and saliva were associated with diseases such as
5050:"Metabolomics: available results, current research projects in breast cancer, and future applications"
4979:
Schauer N, Fernie AR (October 2006). "Plant metabolomics: towards biological function and mechanism".
4834:
Arlt SP, Ottka C, Lohi H, Hinderer J, Lüdeke J, Müller E, et al. (2023-05-10). Mükremin Ö (ed.).
1020:. By contrast, in human-based metabolomics, it is more common to describe metabolites as being either
5697:
2042:
Gates SC, Sweeley CC (October 1978). "Quantitative metabolic profiling based on gas chromatography".
1307:
1226:
1203:
1197:
1179:
341:
322:
292:
3174:
De Luca V, St Pierre B (April 2000). "The cell and developmental biology of alkaloid biosynthesis".
1853:
Jordan KW, Nordenstam J, Lauwers GY, Rothenberger DA, Alavi K, Garwood M, et al. (March 2009).
988:. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1.5
144:
led the development of GC-MS methods to monitor the metabolites present in urine through the 1970s.
5756:
5653:
5498:
5430:
1643:
920:
915:
623:
241:
206:
5638:
5510:
1673:(FAAH) and the monoalkylglycerol ethers (MAGEs) as endogenous substrates for the uncharacterized
1550:
1250:
1193:
168:
3257:"Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies"
5679:
5595:
5398:
5363:
1775:
1566:
1047:
848:
361:
can tell what makes it happen and metabolome can tell what has happened and what is happening.
152:
2392:
Lerner RA, Siuzdak G, Prospero-Garcia O, Henriksen SJ, Boger DL, Cravatt BF (September 1994).
5658:
5612:
1582:
1235:
1132:
858:
659:
502:
318:
314:
5194:
Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, et al. (January 2013).
5418:
5403:
5123:
4945:
4379:"Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis"
4328:
King AM, Mullin LG, Wilson ID, Coen M, Rainville PD, Plumb RS, et al. (January 2019).
4065:
3544:
3368:
3130:
2460:
2405:
2323:
2272:
2221:
1654:
1649:
1613:
particular relevance to pharmaceutical companies wanting to test the toxicity of potential
1574:
1265:
is also used; however, the application of a MALDI matrix can add significant background at
1005:
954:
941:
819:
679:
664:
521:
256:
237:
developed as an online tool and as of 2019 (with METLIN) has over 30,000 registered users.
191:
156:
121:
3895:
88:
indeed quantifiable correlations between the metabolome and the other cellular ensembles (
8:
5751:
5628:
5602:
5460:
5447:
5408:
5077:
1785:
1625:
1570:
1242:
1111:
875:
738:
526:
471:
461:
250:
195:
160:
5127:
4949:
4862:
4835:
4069:
3927:
Rasmiena AA, Ng TW, Meikle PJ (March 2013). "Metabolomics and ischaemic heart disease".
3548:
3372:
3134:
2464:
2409:
2327:
2276:
2225:
5582:
5393:
5258:
5220:
5195:
5177:
5152:
5036:
4913:
4886:
4811:
4786:
4721:
4696:
4596:
4571:
4492:
4452:
4427:
4403:
4378:
4354:
4329:
4310:
4266:
4182:
4155:
4136:
4089:
3904:
3879:
3775:
3726:
3642:
3617:
3508:
3483:
3389:
3356:
3334:
3234:
3156:
3094:
3069:
3045:
3020:
2992:
2980:
2824:
2799:
2775:
2750:
2749:
Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, et al. (1 January 2009).
2726:
2701:
2672:
2647:
2563:
2538:
2519:
2507:
2296:
2245:
2194:
2128:
2019:
1993:"Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines"
1992:
1973:
1930:
1879:
1854:
1808:, a bioinformatics software designed for statistical analysis of mass spectrometry data
1790:
1780:
1311:
1218:
1171:
1165:
1136:
1068:
868:
728:
532:
516:
141:
3187:
2945:
2700:
Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. (January 1, 2007).
1839:
1067:
meaning a rule set or set of laws. This approach was pioneered by Jeremy
Nicholson at
5633:
5442:
5356:
5275:
5265:
5242:
5225:
5182:
5168:
5139:
5102:
5069:
4996:
4961:
4918:
4867:
4816:
4767:
4726:
4677:
4636:
4601:
4572:"Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics"
4528:
4457:
4408:
4359:
4302:
4270:
4258:
4222:
4187:
4128:
4081:
4038:
4003:
3944:
3909:
3860:
3819:
3767:
3718:
3683:
3647:
3598:
3562:
3531:
Lin W, Conway LP, Block A, Sommi G, Vujasinovic M, Löhr JM, et al. (June 2020).
3513:
3464:
3429:
3394:
3326:
3288:
3226:
3191:
3148:
3099:
3050:
2984:
2949:
2829:
2780:
2731:
2677:
2628:
2568:
2511:
2476:
2433:
2428:
2393:
2374:
2339:
2288:
2237:
2186:
2151:
2132:
2059:
2024:
1965:
1934:
1922:
1884:
1733:
1546:
1526:
1214:
1140:
1122:
1081:
1043:
638:
453:
432:
265:
5040:
4957:
4496:
4140:
3730:
3338:
3068:
Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. (January 2013).
2523:
2198:
1977:
5557:
5552:
5215:
5207:
5172:
5164:
5131:
5094:
5061:
5048:
Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di Leo A (July 2007).
5028:
4988:
4953:
4908:
4898:
4857:
4847:
4806:
4798:
4757:
4716:
4708:
4672:
4667:
4655:
4628:
4591:
4583:
4520:
4484:
4447:
4439:
4398:
4390:
4349:
4341:
4314:
4294:
4250:
4214:
4177:
4167:
4120:
4093:
4073:
4030:
3995:
3936:
3899:
3891:
3850:
3809:
3779:
3757:
3710:
3675:
3637:
3629:
3590:
3552:
3503:
3495:
3456:
3421:
3384:
3376:
3318:
3278:
3268:
3218:
3183:
3160:
3138:
3089:
3081:
3040:
3032:
2996:
2976:
2941:
2819:
2811:
2770:
2762:
2721:
2713:
2667:
2659:
2620:
2558:
2550:
2503:
2468:
2423:
2413:
2366:
2331:
2300:
2280:
2249:
2229:
2178:
2118:
2051:
2014:
2004:
1957:
1918:
1914:
1874:
1866:
1658:
1593:
1585:, models should always be validated to ensure that the results can be generalized.
788:
733:
707:
598:
494:
466:
395:
245:
218:
187:
4992:
3238:
3019:
Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, et al. (January 2022).
2055:
5572:
5562:
5547:
5483:
4852:
4802:
4744:
Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B (September 2005).
4587:
3855:
3838:
2554:
1870:
1747:
1669:-acyltaurines as previously uncharacterized endogenous substrates for the enzyme
1621:
on the grounds of adverse toxicity, it saves the enormous expense of the trials.
1327:
1097:
718:
702:
633:
427:
303:
105:
76:
5151:
Fan TW, Lorkiewicz PK, Sellers K, Moseley HN, Higashi RM, Lane AN (March 2012).
5761:
5729:
5567:
5470:
5413:
4903:
4712:
4394:
4218:
3762:
3745:
2398:
Proceedings of the
National Academy of Sciences of the United States of America
1991:
Cavicchioli MV, Santorsola M, Balboni N, Mercatelli D, Giorgi FM (March 2022).
1719:
1638:
1618:
1217:(MS) is used to identify and quantify metabolites after optional separation by
1085:
843:
723:
628:
183:
5098:
5032:
4524:
4488:
4443:
4345:
4298:
4283:
3460:
1028:. Metabolites of foreign substances such as drugs are termed xenometabolites.
5745:
5542:
5488:
5452:
5279:
5065:
2536:
2357:
Lenz EM, Wilson ID (February 2007). "Analytical strategies in metabonomics".
1694:
1562:
1107:
989:
895:
890:
648:
613:
354:
271:
In 2015, real-time metabolome profiling was demonstrated for the first time.
137:
125:
93:
72:
48:. Associated with each stage is the corresponding systems biology tool, from
5649:
Matrix-assisted laser desorption ionization-time of flight mass spectrometer
5318:
5153:"Stable isotope-resolved metabolomics and applications for drug development"
4762:
4745:
3814:
3797:
3679:
2472:
2418:
1046:
are inputs to other chemical reactions. Such systems have been described as
5493:
5425:
5229:
5186:
5143:
5106:
5073:
5000:
4965:
4922:
4871:
4820:
4771:
4730:
4681:
4640:
4605:
4532:
4461:
4412:
4363:
4306:
4262:
4254:
4226:
4191:
4132:
4124:
4085:
4042:
4007:
3948:
3913:
3864:
3823:
3771:
3722:
3687:
3651:
3602:
3566:
3517:
3468:
3398:
3330:
3292:
3273:
3256:
3230:
3209:
Griffin JL, Shockcor JP (July 2004). "Metabolic profiles of cancer cells".
3195:
3152:
3103:
3054:
3036:
2988:
2833:
2784:
2735:
2681:
2632:
2572:
2515:
2378:
2343:
2292:
2190:
2028:
1969:
1961:
1926:
1888:
1545:
Linear models are commonly used for metabolomics data, but are affected by
863:
783:
749:
437:
408:
279:
229:
222:
214:
179:
84:
5211:
3618:"Global systems biology, personalized medicine and molecular epidemiology"
3447:
Bentley R (1999). "Secondary metabolite biosynthesis: the first century".
3433:
3085:
2953:
2815:
2648:"XCMS Online: a web-based platform to process untargeted metabolomic data"
2480:
2437:
2241:
2155:
5505:
5437:
4172:
2766:
2717:
2263:
Nicholson JK, Lindon JC (October 2008). "Systems biology: Metabonomics".
2063:
2009:
1805:
1755:
1558:
1280:
and analyte fragmentation generated by the high-energy primary ion beam.
1013:
993:
833:
562:
422:
417:
4077:
3425:
3283:
5590:
5537:
5527:
5522:
3940:
3633:
3557:
3532:
2107:"Symbiosis of chemometrics and metabolomics: past, present, and future"
1795:
1770:
1614:
1609:
1032:
1021:
1008:
is not directly involved in those processes, but usually has important
985:
981:
885:
838:
799:
778:
643:
590:
537:
403:
377:
307:
298:
288:
80:
68:
64:
60:
4632:
4428:"Navigating freely-available software tools for metabolomics analysis"
4034:
3999:
3594:
3499:
3380:
2663:
2624:
2370:
1990:
5517:
5323:
5135:
2391:
2335:
2284:
2233:
2182:
1760:
1687:
1674:
1629:
1040:
1036:
1025:
824:
694:
603:
557:
63:, the small molecule substrates, intermediates, and products of cell
45:
37:
3878:
Lu W, Su X, Klein MS, Lewis IA, Fiehn O, Rabinowitz JD (June 2017).
3322:
3222:
3143:
3118:
2123:
2106:
5379:
5333:
4108:
3880:"Metabolite Measurement: Pitfalls to Avoid and Practices to Follow"
3714:
2539:"METLIN: A Technology Platform for Identifying Knowns and Unknowns"
2450:
1852:
1765:
1713:
1677:
1605:
1289:
608:
385:
358:
199:
112:
information to provide a better understanding of cellular biology.
101:
97:
49:
5260:
Metabolomics: Methods And
Protocols (Methods in Molecular Biology)
1727:
1388:
Large body of software and databases for metabolite identification
198:
from sleep deprived animals. One molecule of particular interest,
5478:
5047:
4377:
Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M (March 2012).
3254:
1904:
1103:
1017:
1009:
997:
794:
5338:
4654:
Chiang KP, Niessen S, Saghatelian A, Cravatt BF (October 2006).
3668:
Xenobiotica; the Fate of
Foreign Compounds in Biological Systems
5298:
4935:
4746:"Metabolomics in human nutrition: opportunities and challenges"
4694:
4653:
3481:
2394:"Cerebrodiene: a brain lipid isolated from sleep-deprived cats"
1662:
1314:
and radiolabel (when combined with thin-layer chromatography).
770:
350:
233:
210:
89:
4885:
Ottka C, Vapalahti K, Arlt SP, Bartel A, Lohi H (2023-02-02).
4570:
Bonini P, Kind T, Tsugawa H, Barupal DK, Fiehn O (June 2020).
2645:
1617:
candidates: if a compound can be eliminated before it reaches
5343:
5308:
4618:
4426:
Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C (2017).
4055:
3700:
1318:
Table 1. Comparison of most common used metabolomics methods
1292:
mass spectrometry are also applied to metabolomics research.
1262:
1230:
1183:
109:
3985:
3411:
2610:
1628:, metabolomics can be an excellent tool for determining the
213:, for characterizing human metabolites was developed in the
27:
Scientific study of chemical processes involving metabolites
5348:
5328:
5150:
3579:
2646:
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (June 2012).
2145:
1634:
232:
lab was engaged in identifying metabolites associated with
4376:
3963:"Gas Chromatography Mass Spectrometry (GC-MS) Information"
2798:
Farag MA, Huhman DV, Dixon RA, Sumner LW (February 2008).
3304:
3302:
2146:
Shapiro I, Kavkalo DN, Petrova GV, Ganzin AP (1989). "".
1174:(GC), especially when interfaced with mass spectrometry (
41:
4884:
4791:
Current Opinion in Clinical Nutrition and Metabolic Care
4569:
4509:
4474:
4425:
4239:
2493:
59:
is the scientific study of chemical processes involving
5193:
5084:
4743:
3067:
1947:
321:
completed the first draft of the human metabolome. The
5303:
4106:
3482:
Nordström A, O'Maille G, Qin C, Siuzdak G (May 2006).
3299:
2931:
2797:
4833:
4020:
3743:
3664:
2699:
2211:
4836:"Metabolomics during canine pregnancy and lactation"
4327:
4153:
4107:
Woo HK, Northen TR, Yanes O, Siuzdak G (July 2008).
3744:
Holmes E, Wilson ID, Nicholson JK (September 2008).
3354:
2748:
1709:
3530:
2104:
1588:
5257:
3250:
3248:
3021:"HMDB 5.0: the Human Metabolome Database for 2022"
3018:
1505:Cannot detect or identify salts and inorganic ions
1348:Can be used in metabolite imaging (MALDI or DESI)
984:are the substrates, intermediates and products of
3009:HMDB 4.0 – the human metabolome database in 2018.
2966:
2587:"The Analytical Scientist Innovation Awards 2023"
1495:Detects most organic and some inorganic molecules
1443:Detects most organic and some inorganic molecules
1391:Detects most organic and some inorganic molecules
5743:
3926:
3173:
3070:"HMDB 3.0—The Human Metabolome Database in 2013"
2751:"HMDB: a knowledgebase for the human metabolome"
1555:Projection to Latent Structures (PLS) regression
1511:Requires large sample volumes (0.1—0.5 mL)
40:of biology showing the flow of information from
5018:
3350:
3348:
3308:
3245:
3208:
2639:
2262:
1310:, electrochemical detection (coupled to HPLC),
5236:
4787:"Nutritional metabolomics in critical illness"
3877:
2695:
2693:
2691:
1900:
1898:
5364:
5239:Metabolomics: The Frontier of Systems Biology
5113:
4978:
4370:
3746:"Metabolic phenotyping in health and disease"
2871:
2869:
2168:
1941:
1557:and its classification version PLS-DA. Other
1296:Nuclear magnetic resonance (NMR) spectroscopy
962:
3920:
3836:
3345:
3061:
2882:Genetic Engineering & Biotechnology News
2307:
2100:
2098:
2096:
2041:
108:is to integrate metabolomics with all other
5644:Matrix-assisted laser desorption ionization
4784:
4419:
4160:International Journal of Molecular Sciences
4154:Ghaste M, Mistrik R, Shulaev V (May 2016).
2688:
2530:
2313:
2076:
1997:International Journal of Molecular Sciences
1895:
1196:, HPLC was coupled to MS. In contrast with
1114:, and studying intercellular interactions.
178:metabolomics experiments were performed by
5712:
5371:
5357:
4750:The American Journal of Clinical Nutrition
2908:"Real-time analysis of metabolic products"
2866:
2604:
2487:
2356:
2139:
1270:to the analysis of biofluids and tissues.
1117:
969:
955:
5255:
5219:
5176:
4912:
4902:
4861:
4851:
4810:
4761:
4720:
4671:
4595:
4451:
4402:
4353:
4181:
4171:
3903:
3854:
3813:
3795:
3791:
3789:
3761:
3641:
3615:
3556:
3507:
3388:
3355:Dettmer K, Aronov PA, Hammock BD (2007).
3282:
3272:
3142:
3093:
3044:
2875:
2823:
2774:
2725:
2671:
2562:
2427:
2417:
2256:
2122:
2093:
2018:
2008:
1878:
1304:Fourier-transform ion cyclotron resonance
1661:has recently applied this technology to
1255:secondary electrospray ionization (SESI)
1247:Atmospheric-pressure chemical ionization
1121:
278:
31:
5329:NIH Common Fund Metabolomics Consortium
4204:
3446:
3261:Molecular Nutrition & Food Research
3116:
2884:. Vol. 30, no. 7. p. 1.
2105:van der Greef J, Smilde AK (May 2005).
357:can tell what appears to be happening,
176:liquid chromatography mass spectrometry
14:
5744:
4287:Analytical and Bioanalytical Chemistry
3798:"Metabonomics in toxicology: a review"
3786:
3357:"Mass spectrometry-based metabolomics"
1825:
1519:
1508:Cannot detect non-protonated compounds
1190:High performance liquid chromatography
165:Birkbeck College, University of London
5686:European Molecular Biology Laboratory
5352:
3896:10.1146/annurev-biochem-061516-044952
3830:
2702:"HMDB: the Human Metabolobe Database"
1394:Excellent separation reproductibility
1154:
132:in 1971 after they demonstrated that
3837:Silva LP, Northen TR (August 2015).
1209:
335:Molecular biology/biochemistry data.
134:gas chromatography-mass spectrometry
4207:Current Opinion in Chemical Biology
2888:from the original on 12 August 2011
2878:"Mass Spec Central to Metabolomics"
1600:
1024:(produced by the host organism) or
313:In January 2007, scientists at the
24:
5011:
2981:10.1152/physiolgenomics.00009.2008
2508:10.1097/01.ftd.0000179845.53213.39
1282:Desorption electrospray ionization
1126:Key stages of a metabolomics study
1091:
1039:reactions, where outputs from one
25:
5773:
5288:
3449:Critical Reviews in Biotechnology
1456:Slow (15—40 min per sample)
1407:Slow (20—40 min per sample)
5724:
5723:
5711:
5304:Human Metabolome Database (HMDB)
5169:10.1016/j.pharmthera.2011.12.007
3843:Current Opinion in Biotechnology
1859:Diseases of the Colon and Rectum
1828:"Growing pains for metabolomics"
1740:
1726:
1712:
1589:Machine learning and data mining
936:
935:
384:
205:In 2005, the first metabolomics
5664:Chromosome conformation capture
5157:Pharmacology & Therapeutics
4972:
4958:10.1016/j.phytochem.2022.113472
4929:
4891:Frontiers in Veterinary Science
4878:
4827:
4778:
4737:
4688:
4647:
4612:
4563:
4539:
4503:
4468:
4321:
4277:
4233:
4198:
4147:
4100:
4049:
4014:
3979:
3955:
3871:
3737:
3694:
3658:
3609:
3573:
3524:
3475:
3440:
3405:
3202:
3167:
3110:
3012:
3003:
2960:
2925:
2900:
2840:
2791:
2742:
2579:
2444:
2385:
2350:
2205:
2162:
1385:Quantitative (with calibration)
1274:Secondary ion mass spectrometry
1053:
4673:10.1016/j.chembiol.2006.08.008
3703:Nature Reviews. Drug Discovery
3311:Nature Reviews. Drug Discovery
2077:Preti, George (June 6, 2005).
2070:
2035:
1984:
1919:10.1016/j.plantsci.2020.110789
1846:
1819:
364:
13:
1:
5692:National Institutes of Health
5237:Tomita M, Nishioka T (2005).
5206:(Database issue): D781–D786.
4993:10.1016/j.tplants.2006.08.007
4785:Christopher KB (March 2018).
3967:Thermo Fisher Scientific - US
3884:Annual Review of Biochemistry
3188:10.1016/S1360-1385(00)01575-2
3080:(Database issue): D801–D807.
2946:10.1016/S0167-7799(98)01214-1
2876:Morrow Jr KJ (1 April 2010).
2850:. Nov 7, 2012. Archived from
1812:
1450:Destructive (not recoverable)
1401:Destructive (not recoverable)
274:
5378:
5054:Journal of Clinical Oncology
4853:10.1371/journal.pone.0284570
4803:10.1097/MCO.0000000000000451
4588:10.1021/acs.analchem.9b05765
4023:Journal of Proteome Research
3988:Journal of Proteome Research
3856:10.1016/j.copbio.2015.03.015
2555:10.1021/acs.analchem.7b04424
2359:Journal of Proteome Research
2079:"Metabolomics comes of age?"
1871:10.1007/DCR.0b013e31819c9a2c
1540:principal component analysis
1149:principal component analysis
854:Microbial population biology
353:can tell what could happen,
283:The human metabolome project
7:
5608:Structure-based drug design
3119:"Meet the human metabolome"
2848:"www.Plantmetabolomics.org"
2496:Therapeutic Drug Monitoring
2056:10.1093/clinchem/24.10.1663
1705:
1459:Usually requires separation
1012:function. Examples include
83:analyses reveal the set of
10:
5778:
4904:10.3389/fvets.2023.1105113
4713:10.1021/acscentsci.5b00331
4395:10.2174/157489312799304431
4219:10.1016/j.cbpa.2003.08.008
3796:Robertson DG (June 2005).
3763:10.1016/j.cell.2008.08.026
1838:(8): 25–28. Archived from
1671:fatty acid amide hydrolase
1659:Scripps Research Institute
1502:Large instrument footprint
1404:Requires sample separation
1095:
481:Marine microbial symbiosis
286:
219:Scripps Research Institute
188:Scripps Research Institute
115:
5707:
5698:Wellcome Sanger Institute
5672:
5621:
5581:
5469:
5386:
5099:10.2217/14622416.8.9.1243
5033:10.1007/s11306-008-0152-0
4525:10.1016/j.aca.2015.02.012
4489:10.1007/s11306-015-0823-6
4444:10.1007/s11306-017-1242-7
4346:10.1007/s11306-019-1474-9
4299:10.1007/s00216-017-0738-3
3622:Molecular Systems Biology
3461:10.1080/0738-859991229189
3361:Mass Spectrometry Reviews
1665:systems, identifying the
1308:ion-mobility spectrometry
1204:Capillary electrophoresis
1180:flame ionization detector
1035:forms a large network of
1000:are reliably detected in
323:Human Metabolome Database
293:Human Metabolome Database
5654:Microfluidic-based tools
5499:Human Connectome Project
5431:Human Microbiome Project
5339:Golm Metabolome Database
5066:10.1200/JCO.2006.09.7550
3849:. Elsevier BV: 209–216.
3117:Pearson H (March 2007).
2591:The Analytical Scientist
2171:Chemical Society Reviews
1644:Saccharomyces cerevisiae
1492:Very flexible technology
1440:Very flexible technology
1339:Compatible with liquids
921:Earth Microbiome Project
916:Human Microbiome Project
675:Accessible carbohydrates
242:Human Metabolome Project
240:On 23 January 2007, the
207:tandem mass spectrometry
5639:Electrospray ionization
5511:Human Epigenome Project
4981:Trends in Plant Science
4660:Chemistry & Biology
3680:10.1080/004982599238047
3176:Trends in Plant Science
2934:Trends in Biotechnology
2473:10.1126/science.7770779
2419:10.1073/pnas.91.20.9505
2150:(in Russian) (9): 116.
2111:Journal of Chemometrics
1826:Daviss B (April 2005).
1567:support-vector machines
1551:multivariate statistics
1342:Compatible with solids
1251:Electrospray ionization
1194:electrospray ionization
1118:Analytical technologies
186:(then president of the
169:Imperial College London
5680:DNA Data Bank of Japan
5596:Human proteome project
5399:Computational genomics
5334:Metabolomics Workbench
5200:Nucleic Acids Research
4513:Analytica Chimica Acta
4383:Current Bioinformatics
4255:10.1038/nprot.2007.376
4125:10.1038/NPROT.2008.110
3802:Toxicological Sciences
3274:10.1002/mnfr.201800384
3211:Nature Reviews. Cancer
3074:Nucleic Acids Research
3025:Nucleic Acids Research
2969:Physiological Genomics
2755:Nucleic Acids Research
2706:Nucleic Acids Research
1962:10.1002/pmic.200600106
1776:Molecular epidemiology
1336:Compatible with gases
1127:
1080:and metabolomics with
849:Biological dark matter
284:
53:
5659:Isotope affinity tags
5613:Expression proteomics
4763:10.1093/ajcn/82.3.497
3815:10.1093/toxsci/kfi102
3616:Nicholson JK (2006).
2816:10.1104/pp.107.108431
1583:multivariate analysis
1549:. On the other hand,
1453:Not very quantitative
1236:fragmentation pattern
1133:liquid chromatography
1125:
1110:, determining drugs'
859:Microbial cooperation
319:University of Calgary
315:University of Alberta
282:
196:cerebral spinal fluid
35:
5419:Human Genome Project
5404:Comparative genomics
5256:Weckwerth W (2006).
4576:Analytical Chemistry
4173:10.3390/ijms17060816
3583:Analytical Chemistry
3488:Analytical Chemistry
3414:Analytical Chemistry
3037:10.1093/nar/gkab1062
2652:Analytical Chemistry
2613:Analytical Chemistry
2543:Analytical Chemistry
2148:Sovetskaia Meditsina
2010:10.3390/ijms23073867
1650:Arabidopsis thaliana
1579:multiple comparisons
1006:secondary metabolite
820:Biomass partitioning
755:hologenome evolution
680:Flora (microbiology)
257:Arabidopsis thaliana
157:magic angle spinning
122:paper chromatography
5629:2-D electrophoresis
5603:Call-map proteomics
5461:Structural genomics
5448:Population genomics
5409:Functional genomics
5212:10.1093/nar/gks1004
5128:2006Ana...131..875E
4950:2023PChem.205k3472C
4701:ACS Central Science
4627:(45): 14332–14339.
4078:10.1038/nature06195
4070:2007Natur.449.1033N
4064:(7165): 1033–1036.
3549:2020Ana...145.3822L
3426:10.1021/ac00101a004
3373:2007MSRv...26...51D
3135:2007Natur.446....8P
3086:10.1093/nar/gks1065
2465:1995Sci...268.1506C
2459:(5216): 1506–1509.
2410:1994PNAS...91.9505L
2328:2002Ana...127.1549H
2277:2008Natur.455.1054N
2271:(7216): 1054–1056.
2226:1974Natur.252..285H
1842:on 13 October 2008.
1786:Molecular pathology
1626:functional genomics
1520:Statistical methods
1319:
1243:Electron ionization
1112:mechanism of action
1063:meaning change and
876:Metatranscriptomics
670:Initial acquisition
665:Microbial community
372:Part of a series on
260:for several years.
251:Medicago truncatula
182:while working with
161:Jeremy K. Nicholson
5583:Structural biology
5394:Cognitive genomics
3941:10.1042/CS20120268
3634:10.1038/msb4100095
3558:10.1039/D0AN00150C
2767:10.1093/nar/gkn810
2718:10.1093/nar/gkl923
2044:Clinical Chemistry
1791:Precision medicine
1781:Molecular medicine
1655:Cravatt laboratory
1484:>US$ 1 million
1317:
1312:Raman spectroscopy
1286:laser ablation ESI
1186:) can be applied.
1172:Gas chromatography
1166:shotgun lipidomics
1155:Separation methods
1137:gas chromatography
1128:
1069:Murdoch University
454:Marine microbiomes
285:
217:laboratory at the
174:In 1994 and 1996,
142:Arthur B. Robinson
54:
5739:
5738:
5634:Mass spectrometer
5443:Personal genomics
5060:(19): 2840–2846.
4666:(10): 1041–1050.
4633:10.1021/bi0480335
4582:(11): 7515–7522.
4249:(11): 2692–2703.
4035:10.1021/pr034020m
4000:10.1021/pr070183p
3674:(11): 1181–1189.
3595:10.1021/ac801075m
3589:(18): 6835–6844.
3543:(11): 3822–3831.
3500:10.1021/ac060245f
3494:(10): 3289–3295.
3381:10.1002/mas.20108
3031:(D1): D622–D631.
2761:(D1): D603–D610.
2712:(D1): D521–D526.
2664:10.1021/ac300698c
2658:(11): 5035–5039.
2625:10.1021/ac051437y
2404:(20): 9505–9508.
2371:10.1021/pr0605217
2322:(12): 1549–1557.
2220:(5481): 285–287.
2050:(10): 1663–1673.
1956:(17): 4716–4723.
1734:Technology portal
1561:methods, such as
1547:multicollinearity
1527:mass spectrometry
1517:
1516:
1466:NMR spectroscopy
1215:Mass spectrometry
1210:Detection methods
1082:mass spectrometry
1044:chemical reaction
979:
978:
569:Built environment
551:Other microbiomes
495:Human microbiomes
396:Plant microbiomes
332:Clinical data and
268:instrumentation.
266:mass spectrometry
194:, to analyze the
16:(Redirected from
5769:
5727:
5726:
5715:
5714:
5558:Pharmacogenomics
5553:Pharmacogenetics
5373:
5366:
5359:
5350:
5349:
5283:
5264:. Humana Press.
5263:
5252:
5233:
5223:
5190:
5180:
5147:
5136:10.1039/b602376m
5110:
5093:(9): 1243–1266.
5087:Pharmacogenomics
5081:
5076:. Archived from
5044:
5005:
5004:
4976:
4970:
4969:
4933:
4927:
4926:
4916:
4906:
4882:
4876:
4875:
4865:
4855:
4831:
4825:
4824:
4814:
4782:
4776:
4775:
4765:
4741:
4735:
4734:
4724:
4692:
4686:
4685:
4675:
4651:
4645:
4644:
4616:
4610:
4609:
4599:
4567:
4561:
4560:
4558:
4557:
4543:
4537:
4536:
4507:
4501:
4500:
4472:
4466:
4465:
4455:
4423:
4417:
4416:
4406:
4374:
4368:
4367:
4357:
4325:
4319:
4318:
4281:
4275:
4274:
4243:Nature Protocols
4237:
4231:
4230:
4202:
4196:
4195:
4185:
4175:
4151:
4145:
4144:
4119:(8): 1341–1349.
4113:Nature Protocols
4104:
4098:
4097:
4053:
4047:
4046:
4018:
4012:
4011:
3994:(8): 3291–3303.
3983:
3977:
3976:
3974:
3973:
3959:
3953:
3952:
3929:Clinical Science
3924:
3918:
3917:
3907:
3875:
3869:
3868:
3858:
3834:
3828:
3827:
3817:
3793:
3784:
3783:
3765:
3741:
3735:
3734:
3698:
3692:
3691:
3662:
3656:
3655:
3645:
3613:
3607:
3606:
3577:
3571:
3570:
3560:
3528:
3522:
3521:
3511:
3479:
3473:
3472:
3444:
3438:
3437:
3409:
3403:
3402:
3392:
3352:
3343:
3342:
3306:
3297:
3296:
3286:
3276:
3252:
3243:
3242:
3206:
3200:
3199:
3171:
3165:
3164:
3146:
3114:
3108:
3107:
3097:
3065:
3059:
3058:
3048:
3016:
3010:
3007:
3001:
3000:
2964:
2958:
2957:
2929:
2923:
2922:
2920:
2918:
2904:
2898:
2897:
2895:
2893:
2873:
2864:
2863:
2861:
2859:
2844:
2838:
2837:
2827:
2804:Plant Physiology
2795:
2789:
2788:
2778:
2746:
2740:
2739:
2729:
2697:
2686:
2685:
2675:
2643:
2637:
2636:
2608:
2602:
2601:
2599:
2598:
2583:
2577:
2576:
2566:
2549:(5): 3156–3164.
2534:
2528:
2527:
2491:
2485:
2484:
2448:
2442:
2441:
2431:
2421:
2389:
2383:
2382:
2354:
2348:
2347:
2336:10.1039/b208254n
2311:
2305:
2304:
2285:10.1038/4551054a
2260:
2254:
2253:
2234:10.1038/252285a0
2209:
2203:
2202:
2183:10.1039/b618553n
2177:(7): 1882–1896.
2166:
2160:
2159:
2143:
2137:
2136:
2126:
2117:(5–7): 376–386.
2102:
2091:
2090:
2074:
2068:
2067:
2039:
2033:
2032:
2022:
2012:
1988:
1982:
1981:
1945:
1939:
1938:
1902:
1893:
1892:
1882:
1850:
1844:
1843:
1823:
1750:
1745:
1744:
1743:
1736:
1731:
1730:
1722:
1717:
1716:
1601:Key applications
1594:Machine learning
1571:Student's t-test
1472:10—100 μL
1420:10—100 μL
1320:
1316:
1279:
1268:
1078:NMR spectroscopy
971:
964:
957:
944:
939:
938:
708:Marine holobiont
508:Fecal transplant
388:
369:
368:
246:David S. Wishart
192:Benjamin Cravatt
149:NMR spectroscopy
52:to metabolomics.
21:
5777:
5776:
5772:
5771:
5770:
5768:
5767:
5766:
5757:Systems biology
5742:
5741:
5740:
5735:
5703:
5668:
5617:
5577:
5573:Transcriptomics
5563:Systems biology
5548:Paleopolyploidy
5484:Cheminformatics
5465:
5382:
5377:
5291:
5286:
5272:
5249:
5014:
5012:Further reading
5009:
5008:
4987:(10): 508–516.
4977:
4973:
4934:
4930:
4883:
4879:
4846:(5): e0284570.
4832:
4828:
4783:
4779:
4742:
4738:
4693:
4689:
4652:
4648:
4617:
4613:
4568:
4564:
4555:
4553:
4545:
4544:
4540:
4508:
4504:
4483:(6): 1492–513.
4473:
4469:
4424:
4420:
4375:
4371:
4326:
4322:
4282:
4278:
4238:
4234:
4203:
4199:
4152:
4148:
4105:
4101:
4054:
4050:
4019:
4015:
3984:
3980:
3971:
3969:
3961:
3960:
3956:
3925:
3921:
3876:
3872:
3835:
3831:
3794:
3787:
3742:
3738:
3699:
3695:
3663:
3659:
3614:
3610:
3578:
3574:
3529:
3525:
3480:
3476:
3445:
3441:
3410:
3406:
3353:
3346:
3323:10.1038/nrd1157
3307:
3300:
3267:(1): e1800384.
3253:
3246:
3223:10.1038/nrc1390
3207:
3203:
3172:
3168:
3144:10.1038/446008a
3115:
3111:
3066:
3062:
3017:
3013:
3008:
3004:
2965:
2961:
2930:
2926:
2916:
2914:
2906:
2905:
2901:
2891:
2889:
2874:
2867:
2857:
2855:
2846:
2845:
2841:
2796:
2792:
2747:
2743:
2698:
2689:
2644:
2640:
2609:
2605:
2596:
2594:
2585:
2584:
2580:
2535:
2531:
2492:
2488:
2449:
2445:
2390:
2386:
2355:
2351:
2312:
2308:
2261:
2257:
2210:
2206:
2167:
2163:
2144:
2140:
2124:10.1002/cem.941
2103:
2094:
2075:
2071:
2040:
2036:
1989:
1985:
1946:
1942:
1903:
1896:
1851:
1847:
1824:
1820:
1815:
1801:Transcriptomics
1748:Medicine portal
1746:
1741:
1739:
1732:
1725:
1718:
1711:
1708:
1639:model organisms
1619:clinical trials
1603:
1591:
1522:
1277:
1266:
1212:
1162:ion suppression
1157:
1120:
1100:
1098:Exometabolomics
1094:
1092:Exometabolomics
1056:
975:
934:
927:
926:
925:
910:
902:
901:
900:
829:
814:
806:
805:
804:
791:
773:
763:
762:
761:
745:
712:
703:Plant holobiont
697:
687:
686:
685:
684:
655:
593:
583:
582:
581:
565:
552:
544:
543:
542:
529:
512:
497:
487:
486:
485:
476:
456:
446:
445:
444:
433:soil microbiome
428:root microbiome
413:
398:
367:
304:transcriptomics
295:
277:
118:
106:systems biology
77:gene expression
28:
23:
22:
15:
12:
11:
5:
5775:
5765:
5764:
5759:
5754:
5737:
5736:
5734:
5733:
5721:
5708:
5705:
5704:
5702:
5701:
5695:
5689:
5683:
5676:
5674:
5670:
5669:
5667:
5666:
5661:
5656:
5651:
5646:
5641:
5636:
5631:
5625:
5623:
5622:Research tools
5619:
5618:
5616:
5615:
5610:
5605:
5600:
5599:
5598:
5587:
5585:
5579:
5578:
5576:
5575:
5570:
5568:Toxicogenomics
5565:
5560:
5555:
5550:
5545:
5540:
5535:
5530:
5525:
5520:
5515:
5514:
5513:
5503:
5502:
5501:
5491:
5486:
5481:
5475:
5473:
5471:Bioinformatics
5467:
5466:
5464:
5463:
5458:
5450:
5445:
5440:
5435:
5434:
5433:
5423:
5422:
5421:
5414:Genome project
5411:
5406:
5401:
5396:
5390:
5388:
5384:
5383:
5376:
5375:
5368:
5361:
5353:
5347:
5346:
5341:
5336:
5331:
5326:
5321:
5316:
5311:
5306:
5301:
5290:
5289:External links
5287:
5285:
5284:
5270:
5253:
5247:
5234:
5191:
5163:(3): 366–391.
5148:
5122:(8): 875–885.
5111:
5082:
5080:on 2008-01-20.
5045:
5015:
5013:
5010:
5007:
5006:
4971:
4938:Phytochemistry
4928:
4877:
4826:
4797:(2): 121–125.
4777:
4756:(3): 497–503.
4736:
4687:
4646:
4611:
4562:
4538:
4502:
4467:
4418:
4369:
4320:
4293:(2): 483–490.
4276:
4232:
4213:(5): 648–654.
4197:
4146:
4099:
4048:
4029:(5): 488–494.
4013:
3978:
3954:
3935:(5): 289–306.
3919:
3890:(1): 277–304.
3870:
3829:
3808:(2): 809–822.
3785:
3756:(5): 714–717.
3736:
3715:10.1038/nrd728
3709:(2): 153–161.
3693:
3657:
3608:
3572:
3523:
3474:
3439:
3420:(5): 793–811.
3404:
3344:
3317:(8): 668–676.
3298:
3244:
3217:(7): 551–561.
3201:
3182:(4): 168–173.
3166:
3109:
3060:
3011:
3002:
2959:
2940:(9): 373–378.
2924:
2899:
2865:
2839:
2810:(2): 387–402.
2790:
2741:
2687:
2638:
2619:(3): 779–787.
2603:
2578:
2529:
2502:(6): 747–751.
2486:
2443:
2384:
2365:(2): 443–458.
2349:
2306:
2255:
2204:
2161:
2138:
2092:
2069:
2034:
1983:
1940:
1894:
1865:(3): 520–525.
1845:
1817:
1816:
1814:
1811:
1810:
1809:
1803:
1798:
1793:
1788:
1783:
1778:
1773:
1768:
1763:
1758:
1752:
1751:
1737:
1723:
1720:Biology portal
1707:
1704:
1602:
1599:
1590:
1587:
1521:
1518:
1515:
1514:
1513:
1512:
1509:
1506:
1503:
1498:
1497:
1496:
1493:
1488:
1485:
1482:
1479:
1476:
1473:
1470:
1467:
1463:
1462:
1461:
1460:
1457:
1454:
1451:
1446:
1445:
1444:
1441:
1436:
1433:
1432:>$ 300,000
1430:
1427:
1424:
1421:
1418:
1415:
1411:
1410:
1409:
1408:
1405:
1402:
1397:
1396:
1395:
1392:
1389:
1386:
1381:
1378:
1377:<$ 300,000
1375:
1372:
1369:
1366:
1363:
1360:
1356:
1355:
1354:Disadvantages
1352:
1349:
1346:
1345:Start-up cost
1343:
1340:
1337:
1334:
1333:Sample volume
1331:
1324:
1211:
1208:
1156:
1153:
1119:
1116:
1096:Main article:
1093:
1090:
1086:gut microflora
1055:
1052:
977:
976:
974:
973:
966:
959:
951:
948:
947:
946:
945:
929:
928:
924:
923:
918:
912:
911:
908:
907:
904:
903:
899:
898:
893:
888:
883:
878:
873:
872:
871:
861:
856:
851:
846:
844:Quorum sensing
841:
836:
830:
828:
827:
822:
816:
815:
812:
811:
808:
807:
803:
802:
797:
792:
786:
781:
775:
774:
769:
768:
765:
764:
760:
759:
758:
757:
746:
744:
743:
742:
741:
736:
731:
726:
721:
713:
711:
710:
705:
699:
698:
693:
692:
689:
688:
683:
682:
677:
672:
667:
662:
656:
654:
653:
652:
651:
646:
641:
636:
631:
620:
619:
618:
617:
616:
611:
606:
595:
594:
589:
588:
585:
584:
580:
579:
571:
566:
560:
554:
553:
550:
549:
546:
545:
541:
540:
535:
530:
524:
519:
517:Gut–brain axis
513:
511:
510:
505:
499:
498:
493:
492:
489:
488:
484:
483:
477:
475:
474:
469:
464:
458:
457:
452:
451:
448:
447:
443:
442:
441:
440:
435:
430:
425:
414:
412:
411:
406:
400:
399:
394:
393:
390:
389:
381:
380:
374:
373:
366:
363:
337:
336:
333:
330:
329:Chemical data,
276:
273:
184:Richard Lerner
147:Concurrently,
117:
114:
26:
9:
6:
4:
3:
2:
5774:
5763:
5760:
5758:
5755:
5753:
5750:
5749:
5747:
5732:
5731:
5722:
5720:
5719:
5710:
5709:
5706:
5699:
5696:
5693:
5690:
5687:
5684:
5681:
5678:
5677:
5675:
5673:Organizations
5671:
5665:
5662:
5660:
5657:
5655:
5652:
5650:
5647:
5645:
5642:
5640:
5637:
5635:
5632:
5630:
5627:
5626:
5624:
5620:
5614:
5611:
5609:
5606:
5604:
5601:
5597:
5594:
5593:
5592:
5589:
5588:
5586:
5584:
5580:
5574:
5571:
5569:
5566:
5564:
5561:
5559:
5556:
5554:
5551:
5549:
5546:
5544:
5543:Nutrigenomics
5541:
5539:
5536:
5534:
5531:
5529:
5526:
5524:
5521:
5519:
5516:
5512:
5509:
5508:
5507:
5504:
5500:
5497:
5496:
5495:
5492:
5490:
5489:Chemogenomics
5487:
5485:
5482:
5480:
5477:
5476:
5474:
5472:
5468:
5462:
5459:
5457:
5455:
5451:
5449:
5446:
5444:
5441:
5439:
5436:
5432:
5429:
5428:
5427:
5424:
5420:
5417:
5416:
5415:
5412:
5410:
5407:
5405:
5402:
5400:
5397:
5395:
5392:
5391:
5389:
5385:
5381:
5374:
5369:
5367:
5362:
5360:
5355:
5354:
5351:
5345:
5342:
5340:
5337:
5335:
5332:
5330:
5327:
5325:
5322:
5320:
5317:
5315:
5312:
5310:
5307:
5305:
5302:
5300:
5296:
5293:
5292:
5281:
5277:
5273:
5271:1-588-29561-3
5267:
5262:
5261:
5254:
5250:
5248:4-431-25121-9
5244:
5240:
5235:
5231:
5227:
5222:
5217:
5213:
5209:
5205:
5201:
5197:
5192:
5188:
5184:
5179:
5174:
5170:
5166:
5162:
5158:
5154:
5149:
5145:
5141:
5137:
5133:
5129:
5125:
5121:
5117:
5112:
5108:
5104:
5100:
5096:
5092:
5088:
5083:
5079:
5075:
5071:
5067:
5063:
5059:
5055:
5051:
5046:
5042:
5038:
5034:
5030:
5026:
5022:
5017:
5016:
5002:
4998:
4994:
4990:
4986:
4982:
4975:
4967:
4963:
4959:
4955:
4951:
4947:
4943:
4939:
4932:
4924:
4920:
4915:
4910:
4905:
4900:
4896:
4892:
4888:
4881:
4873:
4869:
4864:
4859:
4854:
4849:
4845:
4841:
4837:
4830:
4822:
4818:
4813:
4808:
4804:
4800:
4796:
4792:
4788:
4781:
4773:
4769:
4764:
4759:
4755:
4751:
4747:
4740:
4732:
4728:
4723:
4718:
4714:
4710:
4707:(2): 99–108.
4706:
4702:
4698:
4691:
4683:
4679:
4674:
4669:
4665:
4661:
4657:
4650:
4642:
4638:
4634:
4630:
4626:
4622:
4615:
4607:
4603:
4598:
4593:
4589:
4585:
4581:
4577:
4573:
4566:
4552:
4548:
4542:
4534:
4530:
4526:
4522:
4518:
4514:
4506:
4498:
4494:
4490:
4486:
4482:
4478:
4471:
4463:
4459:
4454:
4449:
4445:
4441:
4437:
4433:
4429:
4422:
4414:
4410:
4405:
4400:
4396:
4392:
4389:(1): 96–108.
4388:
4384:
4380:
4373:
4365:
4361:
4356:
4351:
4347:
4343:
4339:
4335:
4331:
4324:
4316:
4312:
4308:
4304:
4300:
4296:
4292:
4288:
4280:
4272:
4268:
4264:
4260:
4256:
4252:
4248:
4244:
4236:
4228:
4224:
4220:
4216:
4212:
4208:
4201:
4193:
4189:
4184:
4179:
4174:
4169:
4165:
4161:
4157:
4150:
4142:
4138:
4134:
4130:
4126:
4122:
4118:
4114:
4110:
4103:
4095:
4091:
4087:
4083:
4079:
4075:
4071:
4067:
4063:
4059:
4052:
4044:
4040:
4036:
4032:
4028:
4024:
4017:
4009:
4005:
4001:
3997:
3993:
3989:
3982:
3968:
3964:
3958:
3950:
3946:
3942:
3938:
3934:
3930:
3923:
3915:
3911:
3906:
3901:
3897:
3893:
3889:
3885:
3881:
3874:
3866:
3862:
3857:
3852:
3848:
3844:
3840:
3833:
3825:
3821:
3816:
3811:
3807:
3803:
3799:
3792:
3790:
3781:
3777:
3773:
3769:
3764:
3759:
3755:
3751:
3747:
3740:
3732:
3728:
3724:
3720:
3716:
3712:
3708:
3704:
3697:
3689:
3685:
3681:
3677:
3673:
3669:
3661:
3653:
3649:
3644:
3639:
3635:
3631:
3627:
3623:
3619:
3612:
3604:
3600:
3596:
3592:
3588:
3584:
3576:
3568:
3564:
3559:
3554:
3550:
3546:
3542:
3538:
3534:
3527:
3519:
3515:
3510:
3505:
3501:
3497:
3493:
3489:
3485:
3478:
3470:
3466:
3462:
3458:
3454:
3450:
3443:
3435:
3431:
3427:
3423:
3419:
3415:
3408:
3400:
3396:
3391:
3386:
3382:
3378:
3374:
3370:
3366:
3362:
3358:
3351:
3349:
3340:
3336:
3332:
3328:
3324:
3320:
3316:
3312:
3305:
3303:
3294:
3290:
3285:
3280:
3275:
3270:
3266:
3262:
3258:
3251:
3249:
3240:
3236:
3232:
3228:
3224:
3220:
3216:
3212:
3205:
3197:
3193:
3189:
3185:
3181:
3177:
3170:
3162:
3158:
3154:
3150:
3145:
3140:
3136:
3132:
3128:
3124:
3120:
3113:
3105:
3101:
3096:
3091:
3087:
3083:
3079:
3075:
3071:
3064:
3056:
3052:
3047:
3042:
3038:
3034:
3030:
3026:
3022:
3015:
3006:
2998:
2994:
2990:
2986:
2982:
2978:
2974:
2970:
2963:
2955:
2951:
2947:
2943:
2939:
2935:
2928:
2913:
2909:
2903:
2887:
2883:
2879:
2872:
2870:
2854:on 2012-11-07
2853:
2849:
2843:
2835:
2831:
2826:
2821:
2817:
2813:
2809:
2805:
2801:
2794:
2786:
2782:
2777:
2772:
2768:
2764:
2760:
2756:
2752:
2745:
2737:
2733:
2728:
2723:
2719:
2715:
2711:
2707:
2703:
2696:
2694:
2692:
2683:
2679:
2674:
2669:
2665:
2661:
2657:
2653:
2649:
2642:
2634:
2630:
2626:
2622:
2618:
2614:
2607:
2592:
2588:
2582:
2574:
2570:
2565:
2560:
2556:
2552:
2548:
2544:
2540:
2533:
2525:
2521:
2517:
2513:
2509:
2505:
2501:
2497:
2490:
2482:
2478:
2474:
2470:
2466:
2462:
2458:
2454:
2447:
2439:
2435:
2430:
2425:
2420:
2415:
2411:
2407:
2403:
2399:
2395:
2388:
2380:
2376:
2372:
2368:
2364:
2360:
2353:
2345:
2341:
2337:
2333:
2329:
2325:
2321:
2317:
2310:
2302:
2298:
2294:
2290:
2286:
2282:
2278:
2274:
2270:
2266:
2259:
2251:
2247:
2243:
2239:
2235:
2231:
2227:
2223:
2219:
2215:
2208:
2200:
2196:
2192:
2188:
2184:
2180:
2176:
2172:
2165:
2157:
2153:
2149:
2142:
2134:
2130:
2125:
2120:
2116:
2112:
2108:
2101:
2099:
2097:
2088:
2084:
2083:The Scientist
2080:
2073:
2065:
2061:
2057:
2053:
2049:
2045:
2038:
2030:
2026:
2021:
2016:
2011:
2006:
2002:
1998:
1994:
1987:
1979:
1975:
1971:
1967:
1963:
1959:
1955:
1951:
1944:
1936:
1932:
1928:
1924:
1920:
1916:
1912:
1908:
1907:Plant Science
1901:
1899:
1890:
1886:
1881:
1876:
1872:
1868:
1864:
1860:
1856:
1849:
1841:
1837:
1833:
1832:The Scientist
1829:
1822:
1818:
1807:
1804:
1802:
1799:
1797:
1794:
1792:
1789:
1787:
1784:
1782:
1779:
1777:
1774:
1772:
1769:
1767:
1764:
1762:
1759:
1757:
1754:
1753:
1749:
1738:
1735:
1729:
1724:
1721:
1715:
1710:
1703:
1699:
1696:
1695:Nutrigenomics
1692:
1689:
1685:
1681:
1679:
1676:
1672:
1668:
1664:
1660:
1656:
1652:
1651:
1646:
1645:
1640:
1636:
1631:
1627:
1622:
1620:
1616:
1611:
1607:
1598:
1595:
1586:
1584:
1580:
1576:
1572:
1568:
1564:
1563:random forest
1560:
1556:
1552:
1548:
1543:
1541:
1537:
1531:
1528:
1510:
1507:
1504:
1501:
1500:
1499:
1494:
1491:
1490:
1489:
1486:
1483:
1480:
1477:
1474:
1471:
1468:
1465:
1464:
1458:
1455:
1452:
1449:
1448:
1447:
1442:
1439:
1438:
1437:
1434:
1431:
1428:
1425:
1422:
1419:
1416:
1413:
1412:
1406:
1403:
1400:
1399:
1398:
1393:
1390:
1387:
1384:
1383:
1382:
1379:
1376:
1373:
1370:
1367:
1364:
1361:
1358:
1357:
1353:
1350:
1347:
1344:
1341:
1338:
1335:
1332:
1329:
1326:Sensitivity (
1325:
1322:
1321:
1315:
1313:
1309:
1305:
1300:
1297:
1293:
1291:
1287:
1283:
1275:
1271:
1264:
1258:
1256:
1252:
1248:
1244:
1239:
1237:
1232:
1228:
1224:
1220:
1216:
1207:
1205:
1201:
1199:
1195:
1191:
1187:
1185:
1181:
1177:
1173:
1169:
1167:
1163:
1152:
1150:
1146:
1142:
1139:coupled with
1138:
1134:
1124:
1115:
1113:
1109:
1108:bioprocessing
1106:development,
1105:
1099:
1089:
1087:
1083:
1079:
1073:
1070:
1066:
1062:
1051:
1049:
1045:
1042:
1038:
1034:
1029:
1027:
1023:
1019:
1015:
1011:
1007:
1003:
999:
995:
991:
987:
983:
972:
967:
965:
960:
958:
953:
952:
950:
949:
943:
933:
932:
931:
930:
922:
919:
917:
914:
913:
906:
905:
897:
896:Symbiogenesis
894:
892:
891:Superorganism
889:
887:
884:
882:
879:
877:
874:
870:
867:
866:
865:
862:
860:
857:
855:
852:
850:
847:
845:
842:
840:
837:
835:
832:
831:
826:
823:
821:
818:
817:
810:
809:
801:
798:
796:
793:
790:
787:
785:
782:
780:
777:
776:
772:
767:
766:
756:
753:
752:
751:
748:
747:
740:
737:
735:
732:
730:
727:
725:
722:
720:
717:
716:
715:
714:
709:
706:
704:
701:
700:
696:
691:
690:
681:
678:
676:
673:
671:
668:
666:
663:
661:
658:
657:
650:
647:
645:
642:
640:
637:
635:
632:
630:
627:
626:
625:
622:
621:
615:
614:rhizobacteria
612:
610:
607:
605:
602:
601:
600:
597:
596:
592:
587:
586:
578:
576:
572:
570:
567:
564:
561:
559:
556:
555:
548:
547:
539:
536:
534:
531:
528:
525:
523:
520:
518:
515:
514:
509:
506:
504:
501:
500:
496:
491:
490:
482:
479:
478:
473:
470:
468:
465:
463:
460:
459:
455:
450:
449:
439:
436:
434:
431:
429:
426:
424:
421:
420:
419:
416:
415:
410:
407:
405:
402:
401:
397:
392:
391:
387:
383:
382:
379:
376:
375:
371:
370:
362:
360:
356:
355:transcriptome
352:
346:
343:
334:
331:
328:
327:
326:
324:
320:
316:
311:
309:
305:
300:
294:
290:
281:
272:
269:
267:
261:
259:
258:
253:
252:
247:
243:
238:
235:
231:
228:In 2005, the
226:
224:
220:
216:
212:
208:
203:
201:
197:
193:
189:
185:
181:
177:
172:
170:
167:and later at
166:
162:
158:
154:
150:
145:
143:
139:
138:Linus Pauling
135:
131:
127:
126:schizophrenia
123:
113:
111:
107:
103:
99:
95:
94:transcriptome
91:
86:
85:gene products
82:
78:
74:
73:Messenger RNA
70:
66:
62:
58:
51:
47:
43:
39:
34:
30:
19:
5728:
5716:
5538:Microbiomics
5533:Metabolomics
5532:
5494:Connectomics
5453:
5426:Metagenomics
5324:Metabolights
5259:
5241:. Springer.
5238:
5203:
5199:
5160:
5156:
5119:
5115:
5090:
5086:
5078:the original
5057:
5053:
5024:
5021:Metabolomics
5020:
4984:
4980:
4974:
4941:
4937:
4931:
4894:
4890:
4880:
4843:
4839:
4829:
4794:
4790:
4780:
4753:
4749:
4739:
4704:
4700:
4690:
4663:
4659:
4649:
4624:
4621:Biochemistry
4620:
4614:
4579:
4575:
4565:
4554:. Retrieved
4550:
4541:
4516:
4512:
4505:
4480:
4477:Metabolomics
4476:
4470:
4435:
4432:Metabolomics
4431:
4421:
4386:
4382:
4372:
4337:
4334:Metabolomics
4333:
4323:
4290:
4286:
4279:
4246:
4242:
4235:
4210:
4206:
4200:
4163:
4159:
4149:
4116:
4112:
4102:
4061:
4057:
4051:
4026:
4022:
4016:
3991:
3987:
3981:
3970:. Retrieved
3966:
3957:
3932:
3928:
3922:
3887:
3883:
3873:
3846:
3842:
3832:
3805:
3801:
3753:
3749:
3739:
3706:
3702:
3696:
3671:
3667:
3660:
3625:
3621:
3611:
3586:
3582:
3575:
3540:
3536:
3526:
3491:
3487:
3477:
3452:
3448:
3442:
3417:
3413:
3407:
3367:(1): 51–78.
3364:
3360:
3314:
3310:
3284:11572/214273
3264:
3260:
3214:
3210:
3204:
3179:
3175:
3169:
3126:
3122:
3112:
3077:
3073:
3063:
3028:
3024:
3014:
3005:
2972:
2968:
2962:
2937:
2933:
2927:
2915:. Retrieved
2911:
2902:
2890:. Retrieved
2881:
2856:. Retrieved
2852:the original
2842:
2807:
2803:
2793:
2758:
2754:
2744:
2709:
2705:
2655:
2651:
2641:
2616:
2612:
2606:
2595:. Retrieved
2593:. 2023-12-12
2590:
2581:
2546:
2542:
2532:
2499:
2495:
2489:
2456:
2452:
2446:
2401:
2397:
2387:
2362:
2358:
2352:
2319:
2315:
2309:
2268:
2264:
2258:
2217:
2213:
2207:
2174:
2170:
2164:
2147:
2141:
2114:
2110:
2086:
2082:
2072:
2047:
2043:
2037:
2000:
1996:
1986:
1953:
1949:
1943:
1910:
1906:
1862:
1858:
1848:
1840:the original
1835:
1831:
1821:
1700:
1693:
1686:
1682:
1666:
1648:
1642:
1623:
1604:
1592:
1544:
1535:
1532:
1523:
1301:
1294:
1272:
1267:< 1000 Da
1259:
1240:
1213:
1202:
1188:
1170:
1158:
1129:
1101:
1074:
1064:
1060:
1057:
1054:Metabonomics
1030:
994:lipoproteins
980:
881:Metabolomics
880:
864:Metagenomics
750:Hologenomics
574:
438:spermosphere
409:Phyllosphere
347:
338:
312:
296:
270:
262:
255:
249:
239:
227:
223:Roy Goodacre
204:
180:Gary Siuzdak
173:
146:
129:
119:
57:Metabolomics
56:
55:
36:The central
29:
5506:Epigenomics
5438:Pangenomics
5116:The Analyst
4897:: 1105113.
3537:The Analyst
3455:(1): 1–40.
3129:(7131): 8.
2316:The Analyst
2003:(7): 3867.
1806:XCMS Online
1756:Epigenomics
1608:assessment/
1559:data mining
1365:0.1-0.2 mL
1351:Advantages
1323:Technology
1048:hypercycles
1014:antibiotics
982:Metabolites
834:Gnotobiosis
563:Phycosphere
423:laimosphere
418:Rhizosphere
378:Microbiomes
365:Metabolites
61:metabolites
18:Metabolomic
5752:Metabolism
5746:Categories
5591:Proteomics
5528:Lipidomics
5523:Immunomics
5295:Metabolism
4944:: 113472.
4556:2022-11-08
4438:(9): 106.
4166:(6): 816.
3972:2018-09-26
2975:(1): 1–5.
2597:2023-12-14
1950:Proteomics
1913:: 110789.
1813:References
1796:Proteomics
1771:Lipidomics
1610:toxicology
1278:>500 Da
1160:analysis,
1033:metabolome
1022:endogenous
1010:ecological
986:metabolism
886:Pan-genome
839:Phytobiome
800:Virosphere
695:Holobionts
591:Microbiota
575:Drosophila
538:Necrobiome
503:Human milk
404:Endosphere
308:proteomics
299:metabolome
289:Metabolome
287:See also:
275:Metabolome
209:database,
79:data, and
69:metabolome
65:metabolism
5518:Glycomics
5344:Metabolon
5280:493824826
4551:Metabolon
4519:: 10–23.
4340:(2): 17.
4271:205463871
3628:(1): 52.
2133:122419960
1935:230533604
1761:Fluxomics
1688:Fluxomics
1675:hydrolase
1663:mammalian
1630:phenotype
1041:enzymatic
1037:metabolic
1026:exogenous
825:Dysbiosis
739:rhodolith
604:endophyte
558:Mycobiome
522:Placental
244:, led by
81:proteomic
46:phenotype
38:principle
5730:Category
5456:genomics
5380:Genomics
5319:LCMStats
5230:23109552
5187:22212615
5144:17028718
5107:17924839
5074:17502626
5041:22179989
5027:: 3–21.
5001:16949327
4966:36270412
4923:36816179
4872:37163464
4863:10171673
4840:PLOS ONE
4821:29251691
4772:16155259
4731:27163034
4682:17052608
4641:15533037
4606:32390414
4533:26002472
4497:15712363
4462:28890673
4413:22438836
4364:30830424
4307:29167936
4263:18007604
4227:14580571
4192:27231903
4141:20620548
4133:18714302
4086:17960240
4043:14582645
4008:17625818
3949:23157406
3914:28654323
3865:25855407
3824:15689416
3772:18775301
3731:17881327
3723:12120097
3688:10598751
3652:17016518
3603:18700783
3567:32393929
3518:16689529
3469:10230052
3399:16921475
3339:23743031
3331:12904817
3293:30176196
3231:15229480
3196:10740298
3153:17330009
3104:23161693
3055:34986597
2989:18413782
2912:phys.org
2886:Archived
2834:18055588
2785:18953024
2736:17202168
2682:22533540
2633:16448051
2573:29381867
2524:14774455
2516:16404815
2379:17269702
2344:12537357
2293:18948945
2199:12237358
2191:19551169
2089:(11): 8.
2029:35409231
1978:14631544
1970:16888765
1927:33487364
1889:19333056
1766:Genomics
1706:See also
1678:KIAA1363
1641:such as
1633:unknown
1606:Toxicity
1536:a priori
1290:orbitrap
1061:μεταβολή
1018:pigments
942:Category
909:Projects
789:Mangrove
729:seagrass
609:epiphyte
527:Salivary
472:Cetacean
462:Seagrass
359:proteome
317:and the
200:oleamide
102:lipidome
98:proteome
75:(mRNA),
50:genomics
5479:Biochip
5221:3531110
5178:3471671
5124:Bibcode
4946:Bibcode
4914:9932911
4812:5826639
4722:4827660
4597:8715951
4453:5550549
4404:3299976
4355:6342856
4315:3769892
4183:4926350
4094:4404703
4066:Bibcode
3905:5734093
3780:6677621
3643:1682018
3545:Bibcode
3509:3705959
3434:7762816
3390:1904337
3369:Bibcode
3161:2235062
3131:Bibcode
3095:3531200
3046:8728138
2997:9416755
2954:9744112
2917:May 20,
2892:28 June
2858:May 20,
2825:2245840
2776:2686599
2727:1899095
2673:3703953
2564:5933435
2481:7770779
2461:Bibcode
2453:Science
2438:7937797
2406:Bibcode
2324:Bibcode
2301:4411723
2273:Bibcode
2250:4291661
2242:4431445
2222:Bibcode
2156:2603028
2020:8998886
1880:2720561
1657:at the
1417:0.5 nM
1362:0.5 μM
1143:and/or
1104:biofuel
998:albumin
813:Related
795:Viriome
771:Viromes
649:vaginal
533:Uterine
230:Siuzdak
215:Siuzdak
116:History
44:to the
5387:Fields
5309:METLIN
5299:Curlie
5278:
5268:
5245:
5228:
5218:
5185:
5175:
5142:
5105:
5072:
5039:
4999:
4964:
4921:
4911:
4870:
4860:
4819:
4809:
4770:
4729:
4719:
4680:
4639:
4604:
4594:
4531:
4495:
4460:
4450:
4411:
4401:
4362:
4352:
4313:
4305:
4269:
4261:
4225:
4190:
4180:
4139:
4131:
4092:
4084:
4058:Nature
4041:
4006:
3947:
3912:
3902:
3863:
3822:
3778:
3770:
3729:
3721:
3686:
3650:
3640:
3601:
3565:
3516:
3506:
3467:
3432:
3397:
3387:
3337:
3329:
3291:
3239:527894
3237:
3229:
3194:
3159:
3151:
3123:Nature
3102:
3092:
3053:
3043:
2995:
2987:
2952:
2832:
2822:
2783:
2773:
2734:
2724:
2680:
2670:
2631:
2571:
2561:
2522:
2514:
2479:
2436:
2426:
2377:
2342:
2299:
2291:
2265:Nature
2248:
2240:
2214:Nature
2197:
2189:
2154:
2131:
2064:359193
2062:
2027:
2017:
1976:
1968:
1933:
1925:
1887:
1877:
1653:. The
1414:LC-MS
1359:GC-MS
940:
734:sponge
660:Marine
351:Genome
234:sepsis
211:METLIN
190:) and
130:et al.
110:-omics
100:, and
90:genome
5762:Omics
5694:(USA)
5454:Socio
5037:S2CID
4493:S2CID
4311:S2CID
4267:S2CID
4137:S2CID
4090:S2CID
3776:S2CID
3727:S2CID
3335:S2CID
3235:S2CID
3157:S2CID
2993:S2CID
2520:S2CID
2429:44841
2297:S2CID
2246:S2CID
2195:S2CID
2129:S2CID
1974:S2CID
1931:S2CID
1635:genes
1575:ANOVA
1469:5 μM
1263:MALDI
1231:GC-MS
1225:, or
1184:GCxGC
1176:GC-MS
1065:nomos
869:viral
784:Human
719:coral
624:Human
599:Plant
467:Coral
5718:List
5700:(UK)
5688:(EU)
5682:(JP)
5314:XCMS
5276:OCLC
5266:ISBN
5243:ISBN
5226:PMID
5183:PMID
5140:PMID
5103:PMID
5070:PMID
4997:PMID
4962:PMID
4919:PMID
4868:PMID
4817:PMID
4768:PMID
4727:PMID
4678:PMID
4637:PMID
4602:PMID
4529:PMID
4458:PMID
4409:PMID
4360:PMID
4303:PMID
4259:PMID
4223:PMID
4188:PMID
4129:PMID
4082:PMID
4039:PMID
4004:PMID
3945:PMID
3910:PMID
3861:PMID
3820:PMID
3768:PMID
3750:Cell
3719:PMID
3684:PMID
3648:PMID
3599:PMID
3563:PMID
3514:PMID
3465:PMID
3430:PMID
3395:PMID
3327:PMID
3289:PMID
3227:PMID
3192:PMID
3149:PMID
3100:PMID
3051:PMID
2985:PMID
2950:PMID
2919:2020
2894:2010
2860:2020
2830:PMID
2781:PMID
2732:PMID
2678:PMID
2629:PMID
2569:PMID
2512:PMID
2477:PMID
2434:PMID
2375:PMID
2340:PMID
2289:PMID
2238:PMID
2187:PMID
2152:PMID
2060:PMID
2025:PMID
1966:PMID
1923:PMID
1885:PMID
1647:and
1624:For
1615:drug
1487:Yes
1481:Yes
1478:Yes
1435:Yes
1429:Yes
1426:Yes
1371:Yes
1368:Yes
1223:HPLC
1031:The
1016:and
996:and
724:crab
644:skin
639:oral
634:lung
342:HMDB
306:and
297:The
291:and
254:and
140:and
5297:at
5216:PMC
5208:doi
5173:PMC
5165:doi
5161:133
5132:doi
5120:131
5095:doi
5062:doi
5029:doi
4989:doi
4954:doi
4942:205
4909:PMC
4899:doi
4858:PMC
4848:doi
4807:PMC
4799:doi
4758:doi
4717:PMC
4709:doi
4668:doi
4629:doi
4592:PMC
4584:doi
4521:doi
4517:879
4485:doi
4448:PMC
4440:doi
4399:PMC
4391:doi
4350:PMC
4342:doi
4295:doi
4291:410
4251:doi
4215:doi
4178:PMC
4168:doi
4121:doi
4074:doi
4062:449
4031:doi
3996:doi
3937:doi
3933:124
3900:PMC
3892:doi
3851:doi
3810:doi
3758:doi
3754:134
3711:doi
3676:doi
3638:PMC
3630:doi
3591:doi
3553:doi
3541:145
3504:PMC
3496:doi
3457:doi
3422:doi
3385:PMC
3377:doi
3319:doi
3279:hdl
3269:doi
3219:doi
3184:doi
3139:doi
3127:446
3090:PMC
3082:doi
3041:PMC
3033:doi
2977:doi
2942:doi
2820:PMC
2812:doi
2808:146
2771:PMC
2763:doi
2722:PMC
2714:doi
2668:PMC
2660:doi
2621:doi
2559:PMC
2551:doi
2504:doi
2469:doi
2457:268
2424:PMC
2414:doi
2367:doi
2332:doi
2320:127
2281:doi
2269:455
2230:doi
2218:252
2179:doi
2119:doi
2052:doi
2015:PMC
2005:doi
1958:doi
1915:doi
1911:303
1875:PMC
1867:doi
1475:No
1423:No
1380:No
1374:No
1328:LOD
1145:NMR
1135:or
1002:NMR
990:kDa
779:Bat
629:gut
577:gut
163:at
153:ATP
42:DNA
5748::
5274:.
5224:.
5214:.
5204:41
5202:.
5198:.
5181:.
5171:.
5159:.
5155:.
5138:.
5130:.
5118:.
5101:.
5089:.
5068:.
5058:25
5056:.
5052:.
5035:.
5023:.
4995:.
4985:11
4983:.
4960:.
4952:.
4940:.
4917:.
4907:.
4895:10
4893:.
4889:.
4866:.
4856:.
4844:18
4842:.
4838:.
4815:.
4805:.
4795:21
4793:.
4789:.
4766:.
4754:82
4752:.
4748:.
4725:.
4715:.
4703:.
4699:.
4676:.
4664:13
4662:.
4658:.
4635:.
4625:43
4623:.
4600:.
4590:.
4580:92
4578:.
4574:.
4549:.
4527:.
4515:.
4491:.
4481:11
4479:.
4456:.
4446:.
4436:13
4434:.
4430:.
4407:.
4397:.
4385:.
4381:.
4358:.
4348:.
4338:15
4336:.
4332:.
4309:.
4301:.
4289:.
4265:.
4257:.
4245:.
4221:.
4209:.
4186:.
4176:.
4164:17
4162:.
4158:.
4135:.
4127:.
4115:.
4111:.
4088:.
4080:.
4072:.
4060:.
4037:.
4025:.
4002:.
3990:.
3965:.
3943:.
3931:.
3908:.
3898:.
3888:86
3886:.
3882:.
3859:.
3847:34
3845:.
3841:.
3818:.
3806:85
3804:.
3800:.
3788:^
3774:.
3766:.
3752:.
3748:.
3725:.
3717:.
3705:.
3682:.
3672:29
3670:.
3646:.
3636:.
3624:.
3620:.
3597:.
3587:80
3585:.
3561:.
3551:.
3539:.
3535:.
3512:.
3502:.
3492:78
3490:.
3486:.
3463:.
3453:19
3451:.
3428:.
3418:67
3416:.
3393:.
3383:.
3375:.
3365:26
3363:.
3359:.
3347:^
3333:.
3325:.
3313:.
3301:^
3287:.
3277:.
3265:63
3263:.
3259:.
3247:^
3233:.
3225:.
3213:.
3190:.
3178:.
3155:.
3147:.
3137:.
3125:.
3121:.
3098:.
3088:.
3078:41
3076:.
3072:.
3049:.
3039:.
3029:50
3027:.
3023:.
2991:.
2983:.
2973:34
2971:.
2948:.
2938:16
2936:.
2910:.
2880:.
2868:^
2828:.
2818:.
2806:.
2802:.
2779:.
2769:.
2759:37
2757:.
2753:.
2730:.
2720:.
2710:35
2708:.
2704:.
2690:^
2676:.
2666:.
2656:84
2654:.
2650:.
2627:.
2617:78
2615:.
2589:.
2567:.
2557:.
2547:90
2545:.
2541:.
2518:.
2510:.
2500:27
2498:.
2475:.
2467:.
2455:.
2432:.
2422:.
2412:.
2402:91
2400:.
2396:.
2373:.
2361:.
2338:.
2330:.
2318:.
2295:.
2287:.
2279:.
2267:.
2244:.
2236:.
2228:.
2216:.
2193:.
2185:.
2175:38
2173:.
2127:.
2115:19
2113:.
2109:.
2095:^
2087:19
2085:.
2081:.
2058:.
2048:24
2046:.
2023:.
2013:.
2001:23
1999:.
1995:.
1972:.
1964:.
1952:.
1929:.
1921:.
1909:.
1897:^
1883:.
1873:.
1863:52
1861:.
1857:.
1836:19
1834:.
1830:.
1680:.
1573:,
1565:,
1330:)
1306:,
1257:.
1229:.
1227:CE
1221:,
1219:GC
1198:GC
1168:.
1141:MS
1050:.
225:.
96:,
92:,
5372:e
5365:t
5358:v
5282:.
5251:.
5232:.
5210::
5189:.
5167::
5146:.
5134::
5126::
5109:.
5097::
5091:8
5064::
5043:.
5031::
5025:5
5003:.
4991::
4968:.
4956::
4948::
4925:.
4901::
4874:.
4850::
4823:.
4801::
4774:.
4760::
4733:.
4711::
4705:2
4684:.
4670::
4643:.
4631::
4608:.
4586::
4559:.
4535:.
4523::
4499:.
4487::
4464:.
4442::
4415:.
4393::
4387:7
4366:.
4344::
4317:.
4297::
4273:.
4253::
4247:2
4229:.
4217::
4211:7
4194:.
4170::
4143:.
4123::
4117:3
4096:.
4076::
4068::
4045:.
4033::
4027:2
4010:.
3998::
3992:6
3975:.
3951:.
3939::
3916:.
3894::
3867:.
3853::
3826:.
3812::
3782:.
3760::
3733:.
3713::
3707:1
3690:.
3678::
3654:.
3632::
3626:2
3605:.
3593::
3569:.
3555::
3547::
3520:.
3498::
3471:.
3459::
3436:.
3424::
3401:.
3379::
3371::
3341:.
3321::
3315:2
3295:.
3281::
3271::
3241:.
3221::
3215:4
3198:.
3186::
3180:5
3163:.
3141::
3133::
3106:.
3084::
3057:.
3035::
2999:.
2979::
2956:.
2944::
2921:.
2896:.
2862:.
2836:.
2814::
2787:.
2765::
2738:.
2716::
2684:.
2662::
2635:.
2623::
2600:.
2575:.
2553::
2526:.
2506::
2483:.
2471::
2463::
2440:.
2416::
2408::
2381:.
2369::
2363:6
2346:.
2334::
2326::
2303:.
2283::
2275::
2252:.
2232::
2224::
2201:.
2181::
2158:.
2135:.
2121::
2066:.
2054::
2031:.
2007::
1980:.
1960::
1954:6
1937:.
1917::
1891:.
1869::
1667:N
1131:(
970:e
963:t
956:v
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.