Multiomics

Combined multiomic data collection approaches have evolved to address the limitations of traditional multiomics research, which typically requires separate sample processing for different molecular classes then subsequent computational integration, introducing variability and increasing costs. Early advances in this field include sequential extraction,{{Cite journal |last1=Shibko |first1=S. |last2=Koivistoinen |first2=P. |last3=Tratnyek |first3=C. A. |last4=Newhall |first4=A. R. |last5=Friedman |first5=L. |date=June 1967 |title=A method for sequential quantitative separation and determination of protein, RNA, DNA, lipid, and glycogen from a single rat liver homogenate or from a subcellular fraction |url=https://pubmed.ncbi.nlm.nih.gov/4292701 |journal=Analytical Biochemistry |volume=19 |issue=3 |pages=514–528 |doi=10.1016/0003-2697(67)90242-4 |issn=0003-2697 |pmid=4292701}} TRIzol-based sequential isolation methods, which demonstrated that a reagent traditionally used for RNA isolation could simultaneously extract DNA, RNA, proteins, metabolites, and lipids from a single sample. Similar approaches like the Metabolite, Protein, and Lipid extraction (MPLEx){{Cite journal |last1=Nakayasu |first1=Ernesto S. |last2=Nicora |first2=Carrie D. |last3=Sims |first3=Amy C. |last4=Burnum-Johnson |first4=Kristin E. |last5=Kim |first5=Young-Mo |last6=Kyle |first6=Jennifer E. |last7=Matzke |first7=Melissa M. |last8=Shukla |first8=Anil K. |last9=Chu |first9=Rosalie K. |last10=Schepmoes |first10=Athena A. |last11=Jacobs |first11=Jon M. |last12=Baric |first12=Ralph S. |last13=Webb-Robertson |first13=Bobbie-Jo |last14=Smith |first14=Richard D. |last15=Metz |first15=Thomas O. |date=2016 |title=MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses |journal=mSystems |volume=1 |issue=3 |pages=e00043–16 |doi=10.1128/mSystems.00043-16 |issn=2379-5077 |pmc=5069757 |pmid=27822525}} and the "Three-in-One"{{Cite journal |last1=Kang |first1=Jianing |last2=David |first2=Lisa |last3=Li |first3=Yangyang |last4=Cang |first4=Jing |last5=Chen |first5=Sixue |date=2021 |title=Three-in-One Simultaneous Extraction of Proteins, Metabolites and Lipids for Multi-Omics |journal=Frontiers in Genetics |volume=12 |pages=635971 |doi=10.3389/fgene.2021.635971 |doi-access=free |issn=1664-8021 |pmc=8082496 |pmid=33936167}} method adapted biphasic fractionation to extract proteins, metabolites, and lipids for LC-MS/MS analysis. More recent technological developments include the Multi-Omic Single-Shot Technology (MOST),{{Cite journal |last1=He |first1=Yuchen |last2=Rashan |first2=Edrees H. |last3=Linke |first3=Vanessa |last4=Shishkova |first4=Evgenia |last5=Hebert |first5=Alexander S. |last6=Jochem |first6=Adam |last7=Westphall |first7=Michael S. |last8=Pagliarini |first8=David J. |last9=Overmyer |first9=Katherine A. |last10=Coon |first10=Joshua J. |date=2021-03-09 |title=Multi-Omic Single-Shot Technology for Integrated Proteome and Lipidome Analysis |journal=Analytical Chemistry |volume=93 |issue=9 |pages=4217–4222 |doi=10.1021/acs.analchem.0c04764 |issn=1520-6882 |pmc=8028036 |pmid=33617230}} which integrates proteome and lipidome analysis in a single LC-MS run using one reverse-phase column and a binary mobile phase system, and the Bead-enabled Accelerated Monophasic Multi-omics (BAMM){{Cite journal |last1=Muehlbauer |first1=Laura K. |last2=Jen |first2=Annie |last3=Zhu |first3=Yunyun |last4=He |first4=Yuchen |last5=Shishkova |first5=Evgenia |last6=Overmyer |first6=Katherine A. |last7=Coon |first7=Joshua J. |date=2023-01-17 |title=Rapid Multi-Omics Sample Preparation for Mass Spectrometry |journal=Analytical Chemistry |volume=95 |issue=2 |pages=659–667 |doi=10.1021/acs.analchem.2c02042 |issn=1520-6882 |pmc=10026941 |pmid=36594155}} method that combines n-butanol-based monophasic extraction with magnetic beads and accelerated protein digestion for the separate analysis of metabolites, lipids, and proteins. One of the most comprehensive technologies in this space is Dalton Bioanalytics Inc.'s Omni-MS®, a multiomic assay that uses its proprietary method to simultaneously profile proteins, lipids, electrolytes, metabolites, and other small molecules in a single preparation and single LC-MS analysis. This platform has been applied to biomarker discovery, identifying potential biomarkers across multiple molecular classes and across various conditions and diseases{{Cite journal |last1=Wagle |first1=Basanta R. |last2=Quach |first2=Austin |last3=Yeo |first3=Seungjun |last4=Assumpcao |first4=Anna L. F. V. |last5=Arsi |first5=Komala |last6=Donoghue |first6=Annie M. |last7=Jesudhasan |first7=Palmy R. R. |date=2023-02-05 |title=A Multiomic Analysis of Chicken Serum Revealed the Modulation of Host Factors Due to Campylobacter jejuni Colonization and In-Water Supplementation of Eugenol Nanoemulsion |journal=Animals |volume=13 |issue=4 |pages=559 |doi=10.3390/ani13040559 |doi-access=free |issn=2076-2615 |pmc=9951679 |pmid=36830346}}{{Cite journal |last1=Choi |first1=Janghan |last2=Shakeri |first2=Majid |last3=Bowker |first3=Brian |last4=Zhuang |first4=Hong |last5=Kong |first5=Byungwhi |date=2025-04-14 |title=Differentially abundant proteins, metabolites, and lipid molecules in spaghetti meat compared to normal chicken breast meat: Multiomics analysis1 |journal=Poultry Science |volume=104 |issue=7 |pages=105165 |doi=10.1016/j.psj.2025.105165 |issn=0032-5791|doi-access=free |pmid=40286572 }} including COVID severity during pregnancy,{{Cite journal |last1=Altendahl |first1=Marie |last2=Mok |first2=Thalia |last3=Jang |first3=Christine |last4=Yeo |first4=Seungjun |last5=Quach |first5=Austin |last6=Afshar |first6=Yalda |date=2022 |title=Severe COVID-19 in pregnancy has a distinct serum profile, including greater complement activation and dysregulation of serum lipids |journal=PLOS ONE |volume=17 |issue=11 |pages=e0276766 |doi=10.1371/journal.pone.0276766 |doi-access=free |issn=1932-6203 |pmc=9668183 |pmid=36383608|bibcode=2022PLoSO..1776766A }} 22q11.2 deletion syndrome,{{Cite journal |last1=Zafarullah |first1=Marwa |last2=Angkustsiri |first2=Kathleen |last3=Quach |first3=Austin |last4=Yeo |first4=Seungjun |last5=Durbin-Johnson |first5=Blythe P. |last6=Bowling |first6=Heather |last7=Tassone |first7=Flora |date=2024-02-28 |title=Untargeted metabolomic, and proteomic analysis identifies metabolic biomarkers and pathway alterations in individuals with 22q11.2 deletion syndrome |journal=Metabolomics |volume=20 |issue=2 |pages=31 |doi=10.1007/s11306-024-02088-0 |issn=1573-3890 |pmc=10901937 |pmid=38418685}} and hereditary angioedema.{{Cite journal |last1=Mahajan |first1=Supriya D. |last2=Aalinkeel |first2=Ravikumar |last3=Reynolds |first3=Jessica L. |last4=Machhar |first4=Janvhi S. |last5=Ghebrehiwet |first5=Berhane |last6=Schwartz |first6=Stanley A. |date=February 2025 |title=Omics analysis reveals galectin-3 to be a potential key regulator of allergic inflammation in hereditary angioedema |journal=The Journal of Allergy and Clinical Immunology. Global |volume=4 |issue=1 |pages=100353 |doi=10.1016/j.jacig.2024.100353 |issn=2772-8293 |pmc=11583700 |pmid=39583036}} These integrated approaches significantly reduce sample requirements, processing time, and technical variation while improving correlation analysis across different molecular classes, making them increasingly valuable for precision medicine and systems biology research.

A branch of the field of multiomics is the analysis of multilevel single-cell data, called single-cell multiomics.{{cite journal | doi=10.1016/j.tig.2016.12.003 | title=Single-Cell Multiomics: Multiple Measurements from Single Cells | year=2017 | last1=MacAulay | first1=Iain C. | last2=Ponting | first2=Chris P. | last3=Voet | first3=Thierry | journal=Trends in Genetics | volume=33 | issue=2 | pages=155–168 | pmid=28089370 | pmc=5303816 }}{{Cite journal|last1=Hu|first1=Youjin|last2=An|first2=Qin|last3=Sheu|first3=Katherine|last4=Trejo|first4=Brandon|last5=Fan|first5=Shuxin|last6=Guo|first6=Ying|date=2018-04-20|title=Single Cell Multi-Omics Technology: Methodology and Application|journal=Frontiers in Cell and Developmental Biology|volume=6|pages=28|doi=10.3389/fcell.2018.00028|pmid=29732369|pmc=5919954|issn=2296-634X|doi-access=free}} This approach gives us an unprecedented resolution to look at multilevel transitions in health and disease at the single cell level. An advantage in relation to bulk analysis is to mitigate confounding factors derived from cell to cell variation, allowing the uncovering of heterogeneous tissue architectures.

Methods for parallel single-cell genomic and transcriptomic analysis can be based on simultaneous amplification{{Cite journal|title=Integrated genome and transcriptome sequencing of the same cell|last=Kester, Lennart Spanjaard, Bastiaan Bienko, Magda van Oudenaarden, Alexander Dey, Siddharth S|journal=Nature Biotechnology|date=2015|volume=33|issue=3|pages=285–289|doi=10.1038/nbt.3129|pmid=25599178|pmc=4374170|oclc=931063996}} or physical separation of RNA and genomic DNA.{{Cite journal|last1=Macaulay|first1=Iain C|last2=Teng|first2=Mabel J|last3=Haerty|first3=Wilfried|last4=Kumar|first4=Parveen|last5=Ponting|first5=Chris P|last6=Voet|first6=Thierry|date=2016-09-29|title=Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq|journal=Nature Protocols|volume=11|issue=11|pages=2081–2103|doi=10.1038/nprot.2016.138|pmid=27685099|issn=1754-2189|hdl=20.500.11820/015ce29d-7e2d-42c8-82fa-cb1290b761c0|s2cid=24351548|url=https://www.research.ed.ac.uk/portal/en/publications/separation-and-parallel-sequencing-of-the-genomes-and-transcriptomes-of-single-cells-using-gtseq(015ce29d-7e2d-42c8-82fa-cb1290b761c0).html|hdl-access=free}} They allow insights that cannot be gathered solely from transcriptomic analysis, as RNA data do not contain non-coding genomic regions and information regarding copy-number variation, for example. An extension of this methodology is the integration of single-cell transcriptomes to single-cell methylomes, combining single-cell bisulfite sequencing{{Cite journal|last1=Tang|first1=Fuchou|last2=Wen|first2=Lu|last3=Li|first3=Xianlong|last4=Wu|first4=Xinglong|last5=Zhu|first5=Ping|last6=Guo|first6=Hongshan|date=2013-12-01|title=Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing|journal=Genome Research|volume=23|issue=12|pages=2126–2135|doi=10.1101/gr.161679.113|issn=1088-9051|pmid=24179143|pmc=3847781}}{{Cite journal|last1=Kelsey|first1=Gavin|last2=Reik|first2=Wolf|last3=Stegle|first3=Oliver|last4=Andrews|first4=Simon R.|last5=Julian Peat|last6=Saadeh|first6=Heba|last7=Krueger|first7=Felix|last8=Angermueller|first8=Christof|last9=Lee|first9=Heather J.|date=August 2014|title=Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity|journal=Nature Methods|volume=11|issue=8|pages=817–820|doi=10.1038/nmeth.3035|pmid=25042786|pmc=4117646|issn=1548-7105}} to single cell RNA-Seq.{{Cite journal|last1=Angermueller|first1=Christof|last2=Clark|first2=Stephen J|last3=Lee|first3=Heather J|last4=Macaulay|first4=Iain C|last5=Teng|first5=Mabel J|last6=Hu|first6=Tim Xiaoming|last7=Krueger|first7=Felix|last8=Smallwood|first8=Sébastien A|last9=Ponting|first9=Chris P|date=2016-01-11|title=Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity|journal=Nature Methods|volume=13|issue=3|pages=229–232|doi=10.1038/nmeth.3728|pmid=26752769|pmc=4770512|issn=1548-7091}} Other techniques to query the epigenome, as single-cell ATAC-Seq{{Cite journal|last1=Greenleaf|first1=William J.|last2=Chang|first2=Howard Y.|last3=Snyder|first3=Michael P.|last4=Michael L. Gonzales|last5=Ruff|first5=Dave|last6=Litzenburger|first6=Ulrike M.|last7=Wu|first7=Beijing|last8=Buenrostro|first8=Jason D.|date=July 2015|title=Single-cell chromatin accessibility reveals principles of regulatory variation|journal=Nature|volume=523|issue=7561|pages=486–490|doi=10.1038/nature14590|pmid=26083756|pmc=4685948|issn=1476-4687|bibcode=2015Natur.523..486B}} and single-cell Hi-C{{Cite journal|last1=Fraser|first1=Peter|last2=Tanay|first2=Amos|last3=Laue|first3=Ernest D.|last4=Dean|first4=Wendy|last5=Yaffe|first5=Eitan|last6=Schoenfelder|first6=Stefan|last7=Stevens|first7=Tim J.|last8=Lubling|first8=Yaniv|last9=Nagano|first9=Takashi|date=October 2013|title=Single-cell Hi-C reveals cell-to-cell variability in chromosome structure|journal=Nature|volume=502|issue=7469|pages=59–64|doi=10.1038/nature12593|pmid=24067610|pmc=3869051|issn=1476-4687|bibcode=2013Natur.502...59N}} also exist.

A different, but related, challenge is the integration of proteomic and transcriptomic data.{{Cite journal|last1=Darmanis|first1=Spyros|last2=Gallant|first2=Caroline Julie|last3=Marinescu|first3=Voichita Dana|last4=Niklasson|first4=Mia|last5=Segerman|first5=Anna|last6=Flamourakis|first6=Georgios|last7=Fredriksson|first7=Simon|last8=Assarsson|first8=Erika|last9=Lundberg|first9=Martin|date=2016-01-12|title=Simultaneous Multiplexed Measurement of RNA and Proteins in Single Cells|journal=Cell Reports|volume=14|issue=2|pages=380–389|doi=10.1016/j.celrep.2015.12.021|pmid=26748716|pmc=4713867|issn=2211-1247}}{{Cite journal|last1=Gherardini|first1=Pier Federico|last2=Nolan|first2=Garry P.|last3=Chen|first3=Shih-Yu|last4=Hsieh|first4=Elena W. Y.|last5=Zunder|first5=Eli R.|last6=Bava|first6=Felice-Alessio|last7=Frei|first7=Andreas P.|date=March 2016|title=Highly multiplexed simultaneous detection of RNAs and proteins in single cells|journal=Nature Methods|volume=13|issue=3|pages=269–275|doi=10.1038/nmeth.3742|pmid=26808670|pmc=4767631|issn=1548-7105}} One approach to perform such measurement is to physically separate single-cell lysates in two, processing half for RNA, and half for proteins. The protein content of lysates can be measured by proximity extension assays (PEA), for example, which use DNA-barcoded antibodies.{{Cite journal|last1=Assarsson|first1=Erika|last2=Lundberg|first2=Martin|last3=Holmquist|first3=Göran|last4=Björkesten|first4=Johan|last5=Bucht Thorsen|first5=Stine|last6=Ekman|first6=Daniel|last7=Eriksson|first7=Anna|last8=Rennel Dickens|first8=Emma|last9=Ohlsson|first9=Sandra|date=2014-04-22|title=Homogenous 96-Plex PEA Immunoassay Exhibiting High Sensitivity, Specificity, and Excellent Scalability|journal=PLOS ONE|volume=9|issue=4|pages=e95192|doi=10.1371/journal.pone.0095192|pmid=24755770|pmc=3995906|issn=1932-6203|bibcode=2014PLoSO...995192A|doi-access=free}} A different approach uses a combination of heavy-metal RNA probes and protein antibodies to adapt mass cytometry for multiomic analysis.

Related to Single-cell multiomics is the field of Spatial Omics which assays tissues through omics readouts that preserve the relative spatial orientation of the cells in the tissue. The number of Spatial Omics methods published still lags behind the number of methods published for Single-Cell multiomics, but the numbers are catching up ([https://lookerstudio.google.com/reporting/c317ebcc-0bcc-40e9-845e-580afc8c6965/page/Fix4B Single-cell and Spatial methods]).

In parallel to the advances in high-throughput biology, machine learning applications to biomedical data analysis are flourishing. The integration of multi-omics data analysis and machine learning has led to the discovery of new biomarkers.{{Cite journal|last1=Garmire|first1=Lana X.|last2=Chaudhary|first2=Kumardeep|last3=Huang|first3=Sijia|date=2017|title=More Is Better: Recent Progress in Multi-Omics Data Integration Methods|journal=Frontiers in Genetics|language=English|volume=8|pages=84|doi=10.3389/fgene.2017.00084|pmid=28670325|issn=1664-8021|pmc=5472696|doi-access=free}}{{Cite journal|last1=Tagkopoulos|first1=Ilias|last2=Kim|first2=Minseung|date=2018|title=Data integration and predictive modeling methods for multi-omics datasets|journal=Molecular Omics|volume=14|issue=1|pages=8–25|doi=10.1039/C7MO00051K|pmid=29725673}}{{Cite journal|last1=Lin|first1=Eugene|last2=Lane|first2=Hsien-Yuan|date=2017-01-20|title=Machine learning and systems genomics approaches for multi-omics data|journal=Biomarker Research|volume=5|issue=1|pages=2|doi=10.1186/s40364-017-0082-y|pmid=28127429|issn=2050-7771|pmc=5251341 |doi-access=free }} For example, one of the methods of the [http://mixomics.org/ mixOmics] project implements a method based on sparse Partial Least Squares regression for selection of features (putative biomarkers).{{Cite journal|title=mixOmics: an R package for 'omics feature selection and multiple data integration|last1=Rohart|first1=Florian|last2=Gautier|first2=Benoît|date=2017-02-14|last3=Singh|first3=Amrit|last4=Lê Cao|first4=Kim-Anh|author4-link=Kim-Anh Lê Cao|journal=PLOS Computational Biology|volume=13|issue=11|pages=e1005752|doi = 10.1371/journal.pcbi.1005752|biorxiv=10.1101/108597|pmid=29099853|pmc=5687754|bibcode=2017PLSCB..13E5752R |doi-access=free }} A unified and flexible statistical framewok for heterogeneous data integration called "Regularized Generalized Canonical Correlation Analysis" (RGCCA {{Cite journal |last1=Tenenhaus |first1=Arthur |last2=Tenenhaus |first2=Michel |date=2011-03-17 |title=Regularized Generalized Canonical Correlation Analysis |url=http://dx.doi.org/10.1007/s11336-011-9206-8 |journal=Psychometrika |volume=76 |issue=2 |pages=257–284 |doi=10.1007/s11336-011-9206-8 |issn=0033-3123}}{{Cite journal |last1=Tenenhaus |first1=A. |last2=Philippe |first2=C. |last3=Guillemot |first3=V. |last4=Le Cao |first4=K.-A. |last5=Grill |first5=J. |last6=Frouin |first6=V. |date=2014-02-17 |title=Variable selection for generalized canonical correlation analysis |journal=Biostatistics |volume=15 |issue=3 |pages=569–583 |doi=10.1093/biostatistics/kxu001 |issn=1465-4644|doi-access=free |pmid=24550197 }}{{Cite journal |last1=Tenenhaus |first1=Arthur |last2=Philippe |first2=Cathy |last3=Frouin |first3=Vincent |date=October 2015 |title=Kernel Generalized Canonical Correlation Analysis |url=http://dx.doi.org/10.1016/j.csda.2015.04.004 |journal=Computational Statistics & Data Analysis |volume=90 |pages=114–131 |doi=10.1016/j.csda.2015.04.004 |issn=0167-9473}}{{Cite journal |last1=Tenenhaus |first1=Michel |last2=Tenenhaus |first2=Arthur |last3=Groenen |first3=Patrick J. F. |date=2017-05-23 |title=Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods |url=http://dx.doi.org/10.1007/s11336-017-9573-x |journal=Psychometrika |volume=82 |issue=3 |pages=737–777 |doi=10.1007/s11336-017-9573-x |pmid=28536930 |issn=0033-3123}}) enables identifying such putative biomarkers. This framework is implemented and made freely available within the [https://cran.r-project.org/web/packages/RGCCA/ RGCCA R package] .

File:The first and second phases of the NIH Human Microbiome Project.png

Multiomics currently holds a promise to fill gaps in the understanding of human health and disease, and many researchers are working on ways to generate and analyze disease-related data.{{Cite journal|last1=Hasin|first1=Yehudit|last2=Seldin|first2=Marcus|last3=Lusis|first3=Aldons|date=2017-05-05|title=Multi-omics approaches to disease|journal=Genome Biology|volume=18|issue=1|pages=83|doi=10.1186/s13059-017-1215-1|pmid=28476144|pmc=5418815|issn=1474-760X |doi-access=free }} The applications range from understanding host-pathogen interactions and infectious diseases,{{Cite journal|last1=Khan|first1=Mohd M.|last2=Ernst|first2=Orna|last3=Manes|first3=Nathan P.|last4=Oyler|first4=Benjamin L.|last5=Fraser|first5=Iain D. C.|last6=Goodlett|first6=David R.|last7=Nita-Lazar|first7=Aleksandra|date=2019-03-11|title=Multi-Omics Strategies Uncover Host–Pathogen Interactions|journal=ACS Infectious Diseases|volume=5|issue=4|pages=493–505|doi=10.1021/acsinfecdis.9b00080|pmid=30857388|s2cid=75137107|issn=2373-8227}}{{Cite journal|last1=Aderem|first1=Alan|last2=Adkins|first2=Joshua N.|last3=Ansong|first3=Charles|last4=Galagan|first4=James|last5=Kaiser|first5=Shari|last6=Korth|first6=Marcus J.|last7=Law|first7=G. Lynn|last8=McDermott|first8=Jason G.|last9=Proll|first9=Sean C.|date=2011-02-01|title=A Systems Biology Approach to Infectious Disease Research: Innovating the Pathogen-Host Research Paradigm|journal=mBio|volume=2|issue=1|pages=e00325-10|doi=10.1128/mbio.00325-10|pmid=21285433|pmc=3034460|issn=2150-7511}} cancer,{{cite journal |last1=Mouchtouris |first1=N |last2=Smit |first2=RD |last3=Piper |first3=K |last4=Prashant |first4=G |last5=Evans |first5=JJ |last6=Karsy |first6=M |title=A review of multiomics platforms in pituitary adenoma pathogenesis. |journal=Frontiers in Bioscience (Landmark Edition) |date=4 March 2022 |volume=27 |issue=3 |pages=77 |doi=10.31083/j.fbl2703077 |pmid=35345309|s2cid=247560386 |doi-access=free }} to understanding better chronic and complex non-communicable diseases{{Cite journal|last1=Yan|first1=Jingwen|last2=Risacher|first2=Shannon L|last3=Shen|first3=Li|last4=Saykin|first4=Andrew J.|date=2017-06-30|title=Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data|journal=Briefings in Bioinformatics|volume=19|issue=6|pages=1370–1381|doi=10.1093/bib/bbx066|pmid=28679163|pmc=6454489|issn=1467-5463}} and improving personalized medicine.{{Cite journal|last1=He|first1=Feng Q.|last2=Ollert|first2=Markus|last3=Balling|first3=Rudi|last4=Bode|first4=Sebastian F. N.|last5=Delhalle|first5=Sylvie|date=2018-02-06|title=A roadmap towards personalized immunology|journal=npj Systems Biology and Applications|volume=4|issue=1|pages=9|doi=10.1038/s41540-017-0045-9|pmid=29423275|pmc=5802799|issn=2056-7189}}

The second phase of the $170 million Human Microbiome Project was focused on integrating patient data to different omic datasets, considering host genetics, clinical information and microbiome composition.{{Cite journal|last1=Proctor|first1=Lita M.|last2=Creasy|first2=Heather H.|last3=Fettweis|first3=Jennifer M.|last4=Lloyd-Price|first4=Jason|last5=Mahurkar|first5=Anup|last6=Zhou|first6=Wenyu|last7=Buck|first7=Gregory A.|last8=Snyder|first8=Michael P.|last9=Strauss|first9=Jerome F.|date=May 2019|title=The Integrative Human Microbiome Project|journal=Nature|volume=569|issue=7758|pages=641–648|doi=10.1038/s41586-019-1238-8|pmid=31142853|pmc=6784865|issn=1476-4687|bibcode=2019Natur.569..641I}}{{Cite journal|date=2019-05-29|title=After the Integrative Human Microbiome Project, what's next for the microbiome community?|journal=Nature|volume=569|issue=7758|pages=599|doi=10.1038/d41586-019-01674-w|pmid=31142868|bibcode=2019Natur.569Q.599.|s2cid=169035865|doi-access=free}} The phase one focused on characterization of communities in different body sites. Phase 2 focused in the integration of multiomic data from host & microbiome to human diseases. Specifically, the project used multiomics to improve the understanding of the interplay of gut and nasal microbiomes with type 2 diabetes,{{Cite journal|last1=Snyder|first1=Michael|last2=Weinstock|first2=George M.|last3=Sodergren|first3=Erica|last4=McLaughlin|first4=Tracey|last5=Tse|first5=David|last6=Rost|first6=Hannes|last7=Piening|first7=Brian|last8=Kukurba|first8=Kim|last9=Rose|first9=Sophia Miryam Schüssler-Fiorenza|date=May 2019|title=Longitudinal multi-omics of host–microbe dynamics in prediabetes|journal=Nature|volume=569|issue=7758|pages=663–671|doi=10.1038/s41586-019-1236-x|pmid=31142858|pmc=6666404|issn=1476-4687|bibcode=2019Natur.569..663Z}} gut microbiomes and inflammatory bowel disease{{Cite journal|last1=Huttenhower|first1=Curtis|last2=Xavier|first2=Ramnik J.|last3=Vlamakis|first3=Hera|last4=Franzosa|first4=Eric A.|last5=Clish|first5=Clary B.|last6=Winter|first6=Harland S.|last7=Stappenbeck|first7=Thaddeus S.|last8=Petrosino|first8=Joseph F.|last9=McGovern|first9=Dermot P. B.|date=May 2019|title=Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases|journal=Nature|volume=569|issue=7758|pages=655–662|doi=10.1038/s41586-019-1237-9|pmid=31142855|pmc=6650278|issn=1476-4687|bibcode=2019Natur.569..655L}} and vaginal microbiomes and pre-term birth.{{Cite journal|last1=Buck|first1=Gregory A.|last2=Strauss|first2=Jerome F.|last3=Jefferson|first3=Kimberly K.|last4=Hendricks-Muñoz|first4=Karen D.|last5=Wijesooriya|first5=N. Romesh|last6=Rubens|first6=Craig E.|last7=Gravett|first7=Michael G.|last8=Sexton|first8=Amber L.|last9=Chaffin|first9=Donald O.|date=June 2019|title=The vaginal microbiome and preterm birth|journal=Nature Medicine|volume=25|issue=6|pages=1012–1021|doi=10.1038/s41591-019-0450-2|pmid=31142849|pmc=6750801|issn=1546-170X}}

The complexity of interactions in the human immune system has prompted the generation of a wealth of immunology-related multi-scale omic data.{{Cite journal|last1=Kidd|first1=Brian A|last2=Peters|first2=Lauren A|last3=Schadt|first3=Eric E|last4=Dudley|first4=Joel T|date=2014-01-21|title=Unifying immunology with informatics and multiscale biology|journal=Nature Immunology|volume=15|issue=2|pages=118–127|doi=10.1038/ni.2787|pmid=24448569|pmc=4345400|issn=1529-2908}} Multi-omic data analysis has been employed to gather novel insights about the immune response to infectious diseases, such as pediatric chikungunya,{{Cite journal|last1=Harris|first1=Eva|last2=Kasarskis|first2=Andrew|last3=Wolinsky|first3=Steven M.|last4=Suaréz-Fariñas|first4=Mayte|last5=Zhu|first5=Jun|last6=Wang|first6=Li|last7=Balmaseda|first7=Angel|last8=Thomas|first8=Guajira P.|last9=Stewart|first9=Michael G.|date=2018-08-01|title=Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases|journal=Molecular Systems Biology|volume=14|issue=8|pages=e7862|doi=10.15252/msb.20177862|pmid=30150281|pmc=6110311|issn=1744-4292}} as well as noncommunicable autoimmune diseases.{{Cite journal|last1=Firestein|first1=Gary S.|last2=Wang|first2=Wei|last3=Gay|first3=Steffen|last4=Ball|first4=Scott T.|last5=Bartok|first5=Beatrix|last6=Boyle|first6=David L.|last7=Whitaker|first7=John W.|date=2015-04-22|title=Integrative Omics Analysis of Rheumatoid Arthritis Identifies Non-Obvious Therapeutic Targets|journal=PLOS ONE|volume=10|issue=4|pages=e0124254|doi=10.1371/journal.pone.0124254|pmid=25901943|pmc=4406750|issn=1932-6203|bibcode=2015PLoSO..1024254W|doi-access=free}} Integrative omics has also been employed strongly to understand effectiveness and side effects of vaccines, a field called systems vaccinology.{{Cite journal|last1=Pulendran|first1=Bali|last2=Li|first2=Shuzhao|last3=Nakaya|first3=Helder I.|date=2010-10-29|title=Systems Vaccinology|journal=Immunity|volume=33|issue=4|pages=516–529|doi=10.1016/j.immuni.2010.10.006|pmid=21029962|pmc=3001343|issn=1074-7613}} For example, multiomics was essential to uncover the association of changes in plasma metabolites and immune system transcriptome on response to vaccination against herpes zoster.{{Cite journal|last1=Li|first1=Shuzhao|last2=Sullivan|first2=Nicole L.|last3=Rouphael|first3=Nadine|last4=Yu|first4=Tianwei|last5=Banton|first5=Sophia|last6=Maddur|first6=Mohan S.|last7=McCausland|first7=Megan|last8=Chiu|first8=Christopher|last9=Canniff|first9=Jennifer|date=2017-05-18|title=Metabolic Phenotypes of Response to Vaccination in Humans|journal=Cell|volume=169|issue=5|pages=862–877.e17|doi=10.1016/j.cell.2017.04.026|pmid=28502771|pmc=5711477|issn=0092-8674}}

The Bioconductor project curates a variety of R packages aimed at integrating omic data:

• [http://bioconductor.org/packages/release/bioc/html/omicade4.html omicade4], for multiple co-inertia analysis of multi omic datasets{{Cite journal|last1=Meng|first1=Chen|last2=Kuster|first2=Bernhard|last3=Culhane|first3=Aedín C|last4=Gholami|first4=Amin|date=2014|title=A multivariate approach to the integration of multi-omics datasets|journal=BMC Bioinformatics|volume=15|issue=1|pages=162|doi=10.1186/1471-2105-15-162|pmid=24884486|pmc=4053266|issn=1471-2105 |doi-access=free }}
• [http://bioconductor.org/packages/release/bioc/html/MultiAssayExperiment.html MultiAssayExperiment], offering a bioconductor interface for overlapping samples{{cite journal |last1=Ramos |first1=Marcel |last2=Schiffer |first2=Lucas |last3=Re |first3=Angela |last4=Azhar |first4=Rimsha |last5=Basunia |first5=Azfar |last6=Rodriguez |first6=Carmen |last7=Chan |first7=Tiffany |last8=Chapman |first8=Phil |last9=Davis |first9=Sean R. |last10=Gomez-Cabrero |first10=David |last11=Culhane |first11=Aedin C. |last12=Haibe-Kains |first12=Benjamin |last13=Hansen |first13=Kasper D. |last14=Kodali |first14=Hanish |last15=Louis |first15=Marie S. |last16=Mer |first16=Arvind S. |last17=Riester |first17=Markus |last18=Morgan |first18=Martin |last19=Carey |first19=Vince |last20=Waldron |first20=Levi |title=Software for the Integration of Multiomics Experiments in Bioconductor |journal=Cancer Research |date=1 November 2017 |volume=77 |issue=21 |pages=e39–e42 |doi=10.1158/0008-5472.CAN-17-0344|pmid=29092936 |pmc=5679241 }}
• [http://bioconductor.org/packages/release/bioc/html/IMAS.html IMAS], a package focused on using multi omic data for evaluating alternative splicing{{Citation|last=Seonggyun Han|first=Younghee Lee|title=IMAS|date=2017|url=https://bioconductor.org/packages/IMAS|publisher=Bioconductor|doi=10.18129/b9.bioc.imas|access-date=2019-06-28}}
• [http://bioconductor.org/packages/release/bioc/html/bioCancer.html bioCancer], a package for visualization of multiomic cancer data{{Citation|last=Karim Mezhoud [Aut, Cre]|title=bioCancer|date=2017|url=https://bioconductor.org/packages/bioCancer|publisher=Bioconductor|doi=10.18129/b9.bioc.biocancer|access-date=2019-06-28}}
• [http://bioconductor.org/packages/release/bioc/html/mixOmics.html mixOmics], a suite of multivariate methods for data integration
• [http://bioconductor.org/packages/release/bioc/html/MultiDataSet.html MultiDataSet], a package for encapsulating multiple data sets{{Cite journal|last1=Hernandez-Ferrer|first1=Carles|last2=Ruiz-Arenas|first2=Carlos|last3=Beltran-Gomila|first3=Alba|last4=González|first4=Juan R.|date=2017-01-17|title=MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration|journal=BMC Bioinformatics|volume=18|issue=1|pages=36|doi=10.1186/s12859-016-1455-1|pmid=28095799|pmc=5240259|issn=1471-2105 |doi-access=free }}

The [https://cran.r-project.org/web/packages/RGCCA/ RGCCA package] implements a versatile framework for data integration. This package is freely available on the [https://cran.r-project.org/ Comprehensive R Archive Network (CRAN)].

The OmicTools{{Cite web|title=Reap the rewards of a biological insight engine|url=https://omictools.com/|access-date=2019-06-26|website=omicX}} database further highlights R packages and othertools for multi omic data analysis:

• [http://www.paintomics.org/ PaintOmics], a web resource for visualization of multi-omics datasets{{Cite journal|last1=Conesa|first1=Ana|last2=Dopazo|first2=Joaquín|last3=García-López|first3=Federico|last4=García-Alcalde|first4=Fernando|date=2011-01-01|title=Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data|journal=Bioinformatics|volume=27|issue=1|pages=137–139|doi=10.1093/bioinformatics/btq594|pmid=21098431|pmc=3008637|issn=1367-4803}}{{Cite journal|last1=Conesa|first1=Ana|last2=Pappas|first2=Georgios J.|last3=Furió-Tarí|first3=Pedro|last4=Balzano-Nogueira|first4=Leandro|last5=Martínez-Mira|first5=Carlos|last6=Tarazona|first6=Sonia|last7=Hernández-de-Diego|first7=Rafael|date=2018-07-02|title=PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data|journal=Nucleic Acids Research|volume=46|issue=W1|pages=W503–W509|doi=10.1093/nar/gky466|pmid=29800320|pmc=6030972|issn=0305-1048}}
• SIGMA, a Java program focused on integrated analysis of cancer datasets{{Cite journal|last1=Chari|first1=Raj|last2=Coe|first2=Bradley P.|last3=Wedseltoft|first3=Craig|last4=Benetti|first4=Marie|last5=Wilson|first5=Ian M.|last6=Vucic|first6=Emily A.|last7=MacAulay|first7=Calum|last8=Ng|first8=Raymond T.|last9=Lam|first9=Wan L.|date=2008-10-07|title=SIGMA2: A system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes|journal=BMC Bioinformatics|volume=9|issue=1|pages=422|doi=10.1186/1471-2105-9-422|pmid=18840289|pmc=2571113|issn=1471-2105 |doi-access=free }}
• iOmicsPASS, a tool in C++ for multiomic-based phenotype prediction{{Cite journal|last1=Choi|first1=Hyungwon|last2=Ewing|first2=Rob|last3=Choi|first3=Kwok Pui|last4=Fermin|first4=Damian|last5=Koh|first5=Hiromi W. L.|date=2018-07-23|title=iOmicsPASS: a novel method for integration of multi-omics data over biological networks and discovery of predictive subnetworks|url=https://www.biorxiv.org/content/10.1101/374520v1|journal=bioRxiv|pages=374520|doi=10.1101/374520|s2cid=92157115}}
• [https://github.com/mkanai/grimon Grimon], an R graphical interface for visualization of multiomic data{{Cite journal|last1=Kanai|first1=Masahiro|last2=Maeda|first2=Yuichi|last3=Okada|first3=Yukinori|date=2018-06-19|title=Grimon: graphical interface to visualize multi-omics networks|journal=Bioinformatics|volume=34|issue=22|pages=3934–3936|doi=10.1093/bioinformatics/bty488|pmid=29931190|pmc=6223372|issn=1367-4803}}
• [https://pypi.org/project/omics_pipe/ Omics Pipe], a framework in Python for reproducibly automating multiomic data analysis{{Cite journal|last1=Su|first1=Andrew I.|last2=Loguercio|first2=Salvatore|last3=Carland|first3=Tristan M.|last4=Ducom|first4=Jean-Christophe|last5=Gioia|first5=Louis|last6=Meißner|first6=Tobias|last7=Fisch|first7=Kathleen M.|date=2015-06-01|title=Omics Pipe: a community-based framework for reproducible multi-omics data analysis|journal=Bioinformatics|volume=31|issue=11|pages=1724–1728|doi=10.1093/bioinformatics/btv061|pmid=25637560|pmc=4443682|issn=1367-4803}}

A major limitation of classical omic studies is the isolation of only one level of biological complexity. For example, transcriptomic studies may provide information at the transcript level, but many different entities contribute to the biological state of the sample (genomic variants, post-translational modifications, metabolic products, interacting organisms, among others). With the advent of high-throughput biology, it is becoming increasingly affordable to make multiple measurements, allowing transdomain (e.g. RNA and protein levels) correlations and inferences. These correlations aid the construction or more complete biological networks, filling gaps in our knowledge.

Integration of data, however, is not an easy task. To facilitate the process, groups have curated database and pipelines to systematically explore multiomic data:

• Multi-Omics Profiling Expression Database (MOPED),{{Cite journal|last1=Montague|first1=Elizabeth|last2=Stanberry|first2=Larissa|last3=Higdon|first3=Roger|last4=Janko|first4=Imre|last5=Lee|first5=Elaine|last6=Anderson|first6=Nathaniel|last7=Choiniere|first7=John|last8=Stewart|first8=Elizabeth|last9=Yandl|first9=Gregory|date=June 2014|title=MOPED 2.5—An Integrated Multi-Omics Resource: Multi-Omics Profiling Expression Database Now Includes Transcriptomics Data|journal=OMICS: A Journal of Integrative Biology|volume=18|issue=6|pages=335–343|doi=10.1089/omi.2014.0061|pmid=24910945|pmc=4048574|issn=1536-2310}} integrating diverse animal models,
• The Pancreatic Expression Database, integrating data related to pancreatic tissue,
• [http://www.linkedomics.org/ LinkedOmics],{{Cite journal|last1=Zhang|first1=Bing|last2=Wang|first2=Jing|last3=Straub|first3=Peter|last4=Vasaikar|first4=Suhas V.|date=2018-01-04|title=LinkedOmics: analyzing multi-omics data within and across 32 cancer types|journal=Nucleic Acids Research|volume=46|issue=D1|pages=D956–D963|doi=10.1093/nar/gkx1090|pmid=29136207|pmc=5753188|issn=0305-1048}}{{Cite web|url=http://www.linkedomics.org|title=LinkedOmics :: Login|website=www.linkedomics.org|access-date=2019-06-26}} connecting data from TCGA cancer datasets,
• OASIS,{{Cite journal|last1=Kan|first1=Zhengyan|last2=Rejto|first2=Paul A.|last3=Roberts|first3=Peter|last4=Ding|first4=Ying|last5=AChing|first5=Keith|last6=Wang|first6=Kai|last7=Deng|first7=Shibing|last8=Schefzick|first8=Sabine|last9=Estrella|first9=Heather|date=January 2016|title=OASIS: web-based platform for exploring cancer multi-omics data|journal=Nature Methods|volume=13|issue=1|pages=9–10|doi=10.1038/nmeth.3692|pmid=26716558|s2cid=38621277|issn=1548-7105}} a web-based resource for general cancer studies,
• BCIP,{{Cite journal|last1=Wu|first1=Jiaqi|last2=Hu|first2=Shuofeng|last3=Chen|first3=Yaowen|last4=Li|first4=Zongcheng|last5=Zhang|first5=Jian|last6=Yuan|first6=Hanyu|last7=Shi|first7=Qiang|last8=Shao|first8=Ningsheng|last9=Ying|first9=Xiaomin|date=May 2017|title=BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer|journal=Scientific Reports|volume=7|issue=1|pages=45235|doi=10.1038/srep45235|pmid=28327601|pmc=5361122|issn=2045-2322|bibcode=2017NatSR...745235W}} a platform for breast cancer studies,
• C/VDdb,{{Cite journal|last1=Husi|first1=Holger|last2=Patel|first2=Alisha|last3=Fernandes|first3=Marco|date=2018-11-12|title=C/VDdb: A multi-omics expression profiling database for a knowledge-driven approach in cardiovascular disease (CVD)|journal=PLOS ONE|volume=13|issue=11|pages=e0207371|doi=10.1371/journal.pone.0207371|pmid=30419069|pmc=6231654|issn=1932-6203|bibcode=2018PLoSO..1307371F|doi-access=free}} connecting data from several cardiovascular disease studies,
• ZikaVR,{{Cite journal|last1=Gupta|first1=Amit Kumar|last2=Kaur|first2=Karambir|last3=Rajput|first3=Akanksha|last4=Dhanda|first4=Sandeep Kumar|last5=Sehgal|first5=Manika|last6=Khan|first6=Md. Shoaib|last7=Monga|first7=Isha|last8=Dar|first8=Showkat Ahmad|last9=Singh|first9=Sandeep|date=2016-09-16|title=ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis|journal=Scientific Reports|volume=6|issue=1|pages=32713|doi=10.1038/srep32713|pmid=27633273|pmc=5025660|issn=2045-2322|bibcode=2016NatSR...632713G}} a multiomic resource for Zika virus data
• Ecomics,{{Cite journal|last1=Tagkopoulos|first1=Ilias|last2=Violeta Zorraquino|last3=Rai|first3=Navneet|last4=Kim|first4=Minseung|date=2016-10-07|title=Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli|journal=Nature Communications|volume=7|pages=13090|doi=10.1038/ncomms13090|pmid=27713404|pmc=5059772|issn=2041-1723|bibcode=2016NatCo...713090K}} a normalized multi-omic database for Escherichia coli data,
• GourdBase,{{Cite journal|last1=Li|first1=Guojing|last2=Lu|first2=Zhongfu|last3=Lin|first3=Jiandong|last4=Hu|first4=Yaowen|last5=Yunping Huang|last6=Wang|first6=Baogen|last7=Wu|first7=Xinyi|last8=Wu|first8=Xiaohua|last9=Xu|first9=Pei|date=2018-02-26|title=GourdBase: a genome-centered multi-omics database for the bottle gourd ( Lagenaria siceraria ), an economically important cucurbit crop|journal=Scientific Reports|volume=8|issue=1|pages=3604|doi=10.1038/s41598-018-22007-3|pmid=29483591|pmc=5827520|issn=2045-2322|bibcode=2018NatSR...8.3604W}} integrating data from studies with gourd,
• MODEM,{{Cite journal|last1=Liu|first1=Haijun|last2=Wang|first2=Fan|last3=Xiao|first3=Yingjie|last4=Tian|first4=Zonglin|last5=Wen|first5=Weiwei|last6=Zhang|first6=Xuehai|last7=Chen|first7=Xi|last8=Liu|first8=Nannan|last9=Li|first9=Wenqiang|date=2016|title=MODEM: multi-omics data envelopment and mining in maize|journal=Database|volume=2016|pages=baw117|doi=10.1093/database/baw117|pmid=27504011|pmc=4976297|issn=1758-0463}} a database for multilevel maize data,
• SoyKB,{{Cite journal|last1=Xu|first1=Dong|last2=Nguyen|first2=Henry T.|last3=Stacey|first3=Gary|last4=Gaudiello|first4=Eric C.|last5=Endacott|first5=Ryan Z.|last6=Zhang|first6=Hongxin|last7=Liu|first7=Yang|last8=Chen|first8=Shiyuan|last9=Fitzpatrick|first9=Michael R.|date=2014-01-01|title=Soybean knowledge base (SoyKB): a web resource for integration of soybean translational genomics and molecular breeding|journal=Nucleic Acids Research|volume=42|issue=D1|pages=D1245–D1252|doi=10.1093/nar/gkt905|pmid=24136998|pmc=3965117|issn=0305-1048}} a database for multilevel soybean data,
• [https://www.proteomicsdb.org/ ProteomicsDB],{{Cite journal|last1=Samaras|first1=Patroklos|last2=Schmidt|first2=Tobias|last3=Frejno|first3=Martin|last4=Gessulat|first4=Siegfried|last5=Reinecke|first5=Maria|last6=Jarzab|first6=Anna|last7=Zecha|first7=Jana|last8=Mergner|first8=Julia|last9=Giansanti|first9=Piero|last10=Ehrlich|first10=Hans-Christian|last11=Aiche|first11=Stephan|date=2020-01-08|title=ProteomicsDB: a multi-omics and multi-organism resource for life science research|url= |journal=Nucleic Acids Research|language=en|volume=48|issue=D1|pages=D1153–D1163|doi=10.1093/nar/gkz974|pmid=31665479|pmc=7145565|issn=0305-1048}} a multi-omics and multi-organism resource for life science research

• DisGeNET
• Pangenomics
• Hologenomics
• Omics
• List of omics topics in biology
• Systems Biology
• Network Medicine

{{reflist}}

Category:Biology theories

Category:Molecular biology