Multiomics

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Number of citations of the terms "Multiomics" and "Multi-omics" in PubMed until the 31st December 2021. Multiomics PubMed 2022.png
Number of citations of the terms "Multiomics" and "Multi-omics" in PubMed until the 31st December 2021.

Multiomics, multi-omics, integrative omics, "panomics" or "pan-omics" is a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome (i.e., a meta-genome and/or meta-transcriptome, depending upon how it is sequenced); [1] [2] [3] in other words, the use of multiple omics technologies to study life in a concerted way. By combining these "omes", scientists can analyze complex biological big data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association. [4] The OmicTools service lists more than 99 softwares related to multiomic data analysis, as well as more than 99 databases on the topic.

Contents

Systems biology approaches are often based upon the use of panomic analysis data. [5] [6] The American Society of Clinical Oncology (ASCO) defines panomics as referring to "the interaction of all biological functions within a cell and with other body functions, combining data collected by targeted tests ... and global assays (such as genome sequencing) with other patient-specific information." [7]

Single-cell multiomics

A branch of the field of multiomics is the analysis of multilevel single-cell data, called single-cell multiomics. [8] [9] This approach gives us an unprecedent 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. [8]

Methods for parallel single-cell genomic and transcriptomic analysis can be based on simultaneous amplification [10] or physical separation of RNA and genomic DNA. [11] 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 [12] [13] to single cell RNA-Seq. [14] Other techniques to query the epigenome, as single-cell ATAC-Seq [15] and single-cell Hi-C [16] also exist.

A different, but related, challenge is the integration of proteomic and transcriptomic data. [17] [18] One approach to perform such measurement is to physically separate single-cell lysates in two, processing half for RNA, and half for proteins. [17] The protein content of lysates can be measured by proximity extension assays (PEA), for example, which use DNA-barcoded antibodies. [19] A different approach uses a combination of heavy-metal RNA probes and protein antibodies to adapt mass cytometry for multiomic analysis. [18]

Multiomics and machine learning

In parallel to the advances in highthroughput 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. [20] [21] [22] For example, one of the methods of the mixOmics project implements a method based on sparse Partial Least Squares regression for selection of features (putative biomarkers). [23] A unified and flexible statistical framewok for heterogeneous data integration called "Regularized Generalized Canonical Correlation Analysis" (RGCCA [24] [25] [26] [27] ) enables identifying such putative biomarkers. This framework is implemented and made freely avalaible within the RGCCA R package .

Multiomics in health and disease

Overview of phases 1 and 2 of the human microbiome project. The first and second phases of the NIH Human Microbiome Project.png
Overview of phases 1 and 2 of the human microbiome project.

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. [28] The applications range from understanding host-pathogen interactions and infectious diseases, [29] [30] cancer, [31] to understanding better chronic and complex non-communicable diseases [32] and improving personalized medicine. [33]

Integrated Human Microbiome Project

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. [34] [35] 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, [36] gut microbiomes and inflammatory bowel disease [37] and vaginal microbiomes and pre-term birth. [38]

Systems Immunology

The complexity of interactions in the human immune system has prompted the generation of a wealth of immunology-related multi-scale omic data. [39] Multi-omic data analysis has been employed to gather novel insights about the immune response to infectious diseases, such as pediatric chikungunya, [40] as well as noncommunicable autoimmune diseases. [41] Integrative omics has also been employed strongly to understand effectiveness and side effects of vaccines, a field called systems vaccinology. [42] 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. [43]

List of softwares for multi-omic analysis

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

The RGCCA package implements a versatile framework for data integration. This package is freely available on the Comprehensive R Archive Network (CRAN).

The OmicTools [49] database further highlights R packages and othertools for multi omic data analysis:

Multiomic Databases

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:

See also

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">Systems biology</span> Computational and mathematical modeling of complex biological systems

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.

<span class="mw-page-title-main">Omics</span> Suffix in biology

The branches of science known informally as omics are various disciplines in biology whose names end in the suffix -omics, such as genomics, proteomics, metabolomics, metagenomics, phenomics and transcriptomics. Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

<span class="mw-page-title-main">Fusion gene</span>

A fusion gene is a hybrid gene formed from two previously independent genes. It can occur as a result of translocation, interstitial deletion, or chromosomal inversion. Fusion genes have been found to be prevalent in all main types of human neoplasia. The identification of these fusion genes play a prominent role in being a diagnostic and prognostic marker.

Fluxomics describes the various approaches that seek to determine the rates of metabolic reactions within a biological entity. While metabolomics can provide instantaneous information on the metabolites in a biological sample, metabolism is a dynamic process. The significance of fluxomics is that metabolic fluxes determine the cellular phenotype. It has the added advantage of being based on the metabolome which has fewer components than the genome or proteome.

<span class="mw-page-title-main">RNA-Seq</span> Lab technique in cellular biology

RNA-Seq is a sequencing technique that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample, representing an aggregated snapshot of the cells' dynamic pool of RNAs, also known as transcriptome.

<span class="mw-page-title-main">Single-cell analysis</span> Testbg biochemical processes and reactions in an individual cell

In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.

Cancer systems biology encompasses the application of systems biology approaches to cancer research, in order to study the disease as a complex adaptive system with emerging properties at multiple biological scales. Cancer systems biology represents the application of systems biology approaches to the analysis of how the intracellular networks of normal cells are perturbed during carcinogenesis to develop effective predictive models that can assist scientists and clinicians in the validations of new therapies and drugs. Tumours are characterized by genomic and epigenetic instability that alters the functions of many different molecules and networks in a single cell as well as altering the interactions with the local environment. Cancer systems biology approaches, therefore, are based on the use of computational and mathematical methods to decipher the complexity in tumorigenesis as well as cancer heterogeneity.

Pan-cancer analysis aims to examine the similarities and differences among the genomic and cellular alterations found across diverse tumor types. International efforts have performed pan-cancer analysis on exomes and the whole genomes of cancers, the latter including their non-coding regions. In 2018, The Cancer Genome Atlas (TCGA) Research Network used exome, transcriptome, and DNA methylome data to develop an integrated picture of commonalities, differences, and emergent themes across tumor types.

Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.

Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome.

<span class="mw-page-title-main">Pathway analysis</span>

Pathway is the term from molecular biology for a curated schematic representation of a well characterized segment of the molecular physiological machinery, such as a metabolic pathway describing an enzymatic process within a cell or tissue or a signaling pathway model representing a regulatory process that might, in its turn, enable a metabolic or another regulatory process downstream. A typical pathway model starts with an extracellular signaling molecule that activates a specific receptor, thus triggering a chain of molecular interactions. A pathway is most often represented as a relatively small graph with gene, protein, and/or small molecule nodes connected by edges of known functional relations. While a simpler pathway might appear as a chain, complex pathway topologies with loops and alternative routes are much more common. Computational analyses employ special formats of pathway representation. In the simplest form, however, a pathway might be represented as a list of member molecules with order and relations unspecified. Such a representation, generally called Functional Gene Set (FGS), can also refer to other functionally characterised groups such as protein families, Gene Ontology (GO) and Disease Ontology (DO) terms etc. In bioinformatics, methods of pathway analysis might be used to identify key genes/ proteins within a previously known pathway in relation to a particular experiment / pathological condition or building a pathway de novo from proteins that have been identified as key affected elements. By examining changes in e.g. gene expression in a pathway, its biological activity can be explored. However most frequently, pathway analysis refers to a method of initial characterization and interpretation of an experimental condition that was studied with omics tools or genome-wide association study. Such studies might identify long lists of altered genes. A visual inspection is then challenging and the information is hard to summarize, since the altered genes map to a broad range of pathways, processes, and molecular functions. In such situations, the most productive way of exploring the list is to identify enrichment of specific FGSs in it. The general approach of enrichment analyses is to identify FGSs, members of which were most frequently or most strongly altered in the given condition, in comparison to a gene set sampled by chance. In other words, enrichment can map canonical prior knowledge structured in the form of FGSs to the condition represented by altered genes.

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.

CITE-Seq is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. So far, the method has been demonstrated to work with only a few proteins per cell. As such, it provides an additional layer of information for the same cell by combining both proteomics and transcriptomics data. For phenotyping, this method has been shown to be as accurate as flow cytometry by the groups that developed it. It is currently one of the main methods, along with REAP-Seq, to evaluate both gene expression and protein levels simultaneously in different species.

<span class="mw-page-title-main">Cellular deconvolution</span> Set of computational techniques

Cellular deconvolution refers to computational techniques aiming at estimating the proportions of different cell types in samples collected from a tissue. For example, samples collected from the human brain are a mixture of various neuronal and glial cell types in different proportions, where each cell type has a diverse gene expression profile. Since most high-throughput technologies use bulk samples and measure the aggregated levels of molecular information for all cells in a sample, the measured values would be an aggregate of the values pertaining to the expression landscape of different cell types. Therefore, many downstream analyses such as differential gene expression might be confounded by the variations in cell type proportions when using the output of high-throughput technologies applied to bulk samples. The development of statistical methods to identify cell type proportions in large-scale bulk samples is an important step for better understanding of the relationship between cell type composition and diseases.

Single-cell genome and epigenome by transposases sequencing (scGET-seq) is a DNA sequencing method for profiling open and closed chromatin. In contrast to single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), which only targets active euchromatin. scGET-seq is also capable of probing inactive heterochromatin.

Precision diagnostics is a branch of precision medicine that involves precisely managing a patient's healthcare model and diagnosing specific diseases based on customized omics data analytics.

References

  1. ↑ Bersanelli, Matteo; Mosca, Ettore; Remondini, Daniel; Giampieri, Enrico; Sala, Claudia; Castellani, Gastone; Milanesi, Luciano (1 January 2016). "Methods for the integration of multi-omics data: mathematical aspects". BMC Bioinformatics. 17 (2): S15. doi: 10.1186/s12859-015-0857-9 . ISSN   1471-2105. PMC   4959355 . PMID   26821531.
  2. ↑ Bock, Christoph; Farlik, Matthias; Sheffield, Nathan C. (August 2016). "Multi-Omics of Single Cells: Strategies and Applications". Trends in Biotechnology. 34 (8): 605–608. doi:10.1016/j.tibtech.2016.04.004. PMC   4959511 . PMID   27212022.
  3. ↑ Vilanova, Cristina; Porcar, Manuel (26 July 2016). "Are multi-omics enough?". Nature Microbiology. 1 (8): 16101. doi:10.1038/nmicrobiol.2016.101. PMID   27573112. S2CID   3835720.
  4. ↑ Tarazona, S., Balzano-Nogueira, L., & Conesa, A. (2018). Multiomics Data Integration in Time Series Experiments. doi : 10.1016/bs.coac.2018.06.005
  5. ↑ PSB'14 Cancer Panomics Session Archived 2013-09-23 at the Wayback Machine
  6. ↑ The Molecular Landscape of Cancer: Using Panomics to Drive Change Archived 2013-11-09 at the Wayback Machine
  7. ↑ "Glossary". Accelerating Progress Against Cancer: ASCO's blueprint for transforming clinical and translational cancer research (PDF). American Society of Clinical Oncology. 2011. p. 28. Retrieved 13 September 2013.
  8. 1 2 MacAulay, Iain C.; Ponting, Chris P.; Voet, Thierry (2017). "Single-Cell Multiomics: Multiple Measurements from Single Cells". Trends in Genetics. 33 (2): 155–168. doi:10.1016/j.tig.2016.12.003. PMC   5303816 . PMID   28089370.
  9. ↑ Hu, Youjin; An, Qin; Sheu, Katherine; Trejo, Brandon; Fan, Shuxin; Guo, Ying (2018-04-20). "Single Cell Multi-Omics Technology: Methodology and Application". Frontiers in Cell and Developmental Biology. 6: 28. doi: 10.3389/fcell.2018.00028 . ISSN   2296-634X. PMC   5919954 . PMID   29732369.
  10. ↑ Kester, Lennart Spanjaard, Bastiaan Bienko, Magda van Oudenaarden, Alexander Dey, Siddharth S (2015). "Integrated genome and transcriptome sequencing of the same cell". Nature Biotechnology. 33 (3): 285–289. doi:10.1038/nbt.3129. OCLC   931063996. PMC   4374170 . PMID   25599178.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  11. ↑ Macaulay, Iain C; Teng, Mabel J; Haerty, Wilfried; Kumar, Parveen; Ponting, Chris P; Voet, Thierry (2016-09-29). "Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq". Nature Protocols. 11 (11): 2081–2103. doi:10.1038/nprot.2016.138. hdl:20.500.11820/015ce29d-7e2d-42c8-82fa-cb1290b761c0. ISSN   1754-2189. PMID   27685099. S2CID   24351548.
  12. ↑ Tang, Fuchou; Wen, Lu; Li, Xianlong; Wu, Xinglong; Zhu, Ping; Guo, Hongshan (2013-12-01). "Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing". Genome Research. 23 (12): 2126–2135. doi:10.1101/gr.161679.113. ISSN   1088-9051. PMC   3847781 . PMID   24179143.
  13. ↑ Kelsey, Gavin; Reik, Wolf; Stegle, Oliver; Andrews, Simon R.; Julian Peat; Saadeh, Heba; Krueger, Felix; Angermueller, Christof; Lee, Heather J. (August 2014). "Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity". Nature Methods. 11 (8): 817–820. doi:10.1038/nmeth.3035. ISSN   1548-7105. PMC   4117646 . PMID   25042786.
  14. ↑ Angermueller, Christof; Clark, Stephen J; Lee, Heather J; Macaulay, Iain C; Teng, Mabel J; Hu, Tim Xiaoming; Krueger, Felix; Smallwood, Sébastien A; Ponting, Chris P (2016-01-11). "Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity". Nature Methods. 13 (3): 229–232. doi:10.1038/nmeth.3728. ISSN   1548-7091. PMC   4770512 . PMID   26752769.
  15. ↑ Greenleaf, William J.; Chang, Howard Y.; Snyder, Michael P.; Michael L. Gonzales; Ruff, Dave; Litzenburger, Ulrike M.; Wu, Beijing; Buenrostro, Jason D. (July 2015). "Single-cell chromatin accessibility reveals principles of regulatory variation". Nature. 523 (7561): 486–490. Bibcode:2015Natur.523..486B. doi:10.1038/nature14590. ISSN   1476-4687. PMC   4685948 . PMID   26083756.
  16. ↑ Fraser, Peter; Tanay, Amos; Laue, Ernest D.; Dean, Wendy; Yaffe, Eitan; Schoenfelder, Stefan; Stevens, Tim J.; Lubling, Yaniv; Nagano, Takashi (October 2013). "Single-cell Hi-C reveals cell-to-cell variability in chromosome structure". Nature. 502 (7469): 59–64. Bibcode:2013Natur.502...59N. doi:10.1038/nature12593. ISSN   1476-4687. PMC   3869051 . PMID   24067610.
  17. 1 2 Darmanis, Spyros; Gallant, Caroline Julie; Marinescu, Voichita Dana; Niklasson, Mia; Segerman, Anna; Flamourakis, Georgios; Fredriksson, Simon; Assarsson, Erika; Lundberg, Martin (2016-01-12). "Simultaneous Multiplexed Measurement of RNA and Proteins in Single Cells". Cell Reports. 14 (2): 380–389. doi:10.1016/j.celrep.2015.12.021. ISSN   2211-1247. PMC   4713867 . PMID   26748716.
  18. 1 2 Gherardini, Pier Federico; Nolan, Garry P.; Chen, Shih-Yu; Hsieh, Elena W. Y.; Zunder, Eli R.; Bava, Felice-Alessio; Frei, Andreas P. (March 2016). "Highly multiplexed simultaneous detection of RNAs and proteins in single cells". Nature Methods. 13 (3): 269–275. doi:10.1038/nmeth.3742. ISSN   1548-7105. PMC   4767631 . PMID   26808670.
  19. ↑ Assarsson, Erika; Lundberg, Martin; Holmquist, Göran; Björkesten, Johan; Bucht Thorsen, Stine; Ekman, Daniel; Eriksson, Anna; Rennel Dickens, Emma; Ohlsson, Sandra (2014-04-22). "Homogenous 96-Plex PEA Immunoassay Exhibiting High Sensitivity, Specificity, and Excellent Scalability". PLOS ONE. 9 (4): e95192. Bibcode:2014PLoSO...995192A. doi: 10.1371/journal.pone.0095192 . ISSN   1932-6203. PMC   3995906 . PMID   24755770.
  20. ↑ Garmire, Lana X.; Chaudhary, Kumardeep; Huang, Sijia (2017). "More Is Better: Recent Progress in Multi-Omics Data Integration Methods". Frontiers in Genetics. 8: 84. doi: 10.3389/fgene.2017.00084 . ISSN   1664-8021. PMC   5472696 . PMID   28670325.
  21. ↑ Tagkopoulos, Ilias; Kim, Minseung (2018). "Data integration and predictive modeling methods for multi-omics datasets". Molecular Omics. 14 (1): 8–25. doi:10.1039/C7MO00051K. PMID   29725673.
  22. ↑ Lin, Eugene; Lane, Hsien-Yuan (2017-01-20). "Machine learning and systems genomics approaches for multi-omics data". Biomarker Research. 5 (1): 2. doi: 10.1186/s40364-017-0082-y . ISSN   2050-7771. PMC   5251341 . PMID   28127429.
  23. 1 2 Rohart, Florian; Gautier, Benoît; Singh, Amrit; Lê Cao, Kim-Anh (2017-02-14). "mixOmics: an R package for 'omics feature selection and multiple data integration". PLOS Computational Biology. 13 (11): e1005752. Bibcode:2017PLSCB..13E5752R. bioRxiv   10.1101/108597 . doi: 10.1371/journal.pcbi.1005752 . PMC   5687754 . PMID   29099853.
  24. ↑ Tenenhaus, Arthur; Tenenhaus, Michel (2011-03-17). "Regularized Generalized Canonical Correlation Analysis". Psychometrika. 76 (2): 257–284. doi:10.1007/s11336-011-9206-8. ISSN   0033-3123.
  25. ↑ Tenenhaus, A.; Philippe, C.; Guillemot, V.; Le Cao, K.-A.; Grill, J.; Frouin, V. (2014-02-17). "Variable selection for generalized canonical correlation analysis". Biostatistics. 15 (3): 569–583. doi: 10.1093/biostatistics/kxu001 . ISSN   1465-4644.
  26. ↑ Tenenhaus, Arthur; Philippe, Cathy; Frouin, Vincent (October 2015). "Kernel Generalized Canonical Correlation Analysis". Computational Statistics & Data Analysis. 90: 114–131. doi:10.1016/j.csda.2015.04.004. ISSN   0167-9473.
  27. ↑ Tenenhaus, Michel; Tenenhaus, Arthur; Groenen, Patrick J. F. (2017-05-23). "Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods". Psychometrika. 82 (3): 737–777. doi:10.1007/s11336-017-9573-x. ISSN   0033-3123.
  28. ↑ Hasin, Yehudit; Seldin, Marcus; Lusis, Aldons (2017-05-05). "Multi-omics approaches to disease". Genome Biology. 18 (1): 83. doi: 10.1186/s13059-017-1215-1 . ISSN   1474-760X. PMC   5418815 . PMID   28476144.
  29. ↑ Khan, Mohd M.; Ernst, Orna; Manes, Nathan P.; Oyler, Benjamin L.; Fraser, Iain D. C.; Goodlett, David R.; Nita-Lazar, Aleksandra (2019-03-11). "Multi-Omics Strategies Uncover Host–Pathogen Interactions". ACS Infectious Diseases. 5 (4): 493–505. doi:10.1021/acsinfecdis.9b00080. ISSN   2373-8227. PMID   30857388. S2CID   75137107.
  30. ↑ Aderem, Alan; Adkins, Joshua N.; Ansong, Charles; Galagan, James; Kaiser, Shari; Korth, Marcus J.; Law, G. Lynn; McDermott, Jason G.; Proll, Sean C. (2011-02-01). "A Systems Biology Approach to Infectious Disease Research: Innovating the Pathogen-Host Research Paradigm". mBio. 2 (1): e00325-10. doi:10.1128/mbio.00325-10. ISSN   2150-7511. PMC   3034460 . PMID   21285433.
  31. ↑ Mouchtouris, N; Smit, RD; Piper, K; Prashant, G; Evans, JJ; Karsy, M (4 March 2022). "A review of multiomics platforms in pituitary adenoma pathogenesis". Frontiers in Bioscience (Landmark Edition). 27 (3): 77. doi: 10.31083/j.fbl2703077 . PMID   35345309. S2CID   247560386.
  32. ↑ Yan, Jingwen; Risacher, Shannon L; Shen, Li; Saykin, Andrew J. (2017-06-30). "Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data". Briefings in Bioinformatics. 19 (6): 1370–1381. doi:10.1093/bib/bbx066. ISSN   1467-5463. PMC   6454489 . PMID   28679163.
  33. ↑ He, Feng Q.; Ollert, Markus; Balling, Rudi; Bode, Sebastian F. N.; Delhalle, Sylvie (2018-02-06). "A roadmap towards personalized immunology". npj Systems Biology and Applications. 4 (1): 9. doi:10.1038/s41540-017-0045-9. ISSN   2056-7189. PMC   5802799 . PMID   29423275.
  34. ↑ Proctor, Lita M.; Creasy, Heather H.; Fettweis, Jennifer M.; Lloyd-Price, Jason; Mahurkar, Anup; Zhou, Wenyu; Buck, Gregory A.; Snyder, Michael P.; Strauss, Jerome F. (May 2019). "The Integrative Human Microbiome Project". Nature. 569 (7758): 641–648. Bibcode:2019Natur.569..641I. doi:10.1038/s41586-019-1238-8. ISSN   1476-4687. PMC   6784865 . PMID   31142853.
  35. ↑ "After the Integrative Human Microbiome Project, what's next for the microbiome community?". Nature. 569 (7758): 599. 2019-05-29. Bibcode:2019Natur.569Q.599.. doi: 10.1038/d41586-019-01674-w . PMID   31142868. S2CID   169035865.
  36. ↑ Snyder, Michael; Weinstock, George M.; Sodergren, Erica; McLaughlin, Tracey; Tse, David; Rost, Hannes; Piening, Brian; Kukurba, Kim; Rose, Sophia Miryam Schüssler-Fiorenza (May 2019). "Longitudinal multi-omics of host–microbe dynamics in prediabetes". Nature. 569 (7758): 663–671. Bibcode:2019Natur.569..663Z. doi:10.1038/s41586-019-1236-x. ISSN   1476-4687. PMC   6666404 . PMID   31142858.
  37. ↑ Huttenhower, Curtis; Xavier, Ramnik J.; Vlamakis, Hera; Franzosa, Eric A.; Clish, Clary B.; Winter, Harland S.; Stappenbeck, Thaddeus S.; Petrosino, Joseph F.; McGovern, Dermot P. B. (May 2019). "Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases". Nature. 569 (7758): 655–662. Bibcode:2019Natur.569..655L. doi:10.1038/s41586-019-1237-9. ISSN   1476-4687. PMC   6650278 . PMID   31142855.
  38. ↑ Buck, Gregory A.; Strauss, Jerome F.; Jefferson, Kimberly K.; Hendricks-Muñoz, Karen D.; Wijesooriya, N. Romesh; Rubens, Craig E.; Gravett, Michael G.; Sexton, Amber L.; Chaffin, Donald O. (June 2019). "The vaginal microbiome and preterm birth". Nature Medicine. 25 (6): 1012–1021. doi:10.1038/s41591-019-0450-2. ISSN   1546-170X. PMC   6750801 . PMID   31142849.
  39. ↑ Kidd, Brian A; Peters, Lauren A; Schadt, Eric E; Dudley, Joel T (2014-01-21). "Unifying immunology with informatics and multiscale biology". Nature Immunology. 15 (2): 118–127. doi:10.1038/ni.2787. ISSN   1529-2908. PMC   4345400 . PMID   24448569.
  40. ↑ Harris, Eva; Kasarskis, Andrew; Wolinsky, Steven M.; Suaréz‐Fariñas, Mayte; Zhu, Jun; Wang, Li; Balmaseda, Angel; Thomas, Guajira P.; Stewart, Michael G. (2018-08-01). "Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases". Molecular Systems Biology. 14 (8): e7862. doi:10.15252/msb.20177862. ISSN   1744-4292. PMC   6110311 . PMID   30150281.
  41. ↑ Firestein, Gary S.; Wang, Wei; Gay, Steffen; Ball, Scott T.; Bartok, Beatrix; Boyle, David L.; Whitaker, John W. (2015-04-22). "Integrative Omics Analysis of Rheumatoid Arthritis Identifies Non-Obvious Therapeutic Targets". PLOS ONE. 10 (4): e0124254. Bibcode:2015PLoSO..1024254W. doi: 10.1371/journal.pone.0124254 . ISSN   1932-6203. PMC   4406750 . PMID   25901943.
  42. ↑ Pulendran, Bali; Li, Shuzhao; Nakaya, Helder I. (2010-10-29). "Systems Vaccinology". Immunity. 33 (4): 516–529. doi:10.1016/j.immuni.2010.10.006. ISSN   1074-7613. PMC   3001343 . PMID   21029962.
  43. ↑ Li, Shuzhao; Sullivan, Nicole L.; Rouphael, Nadine; Yu, Tianwei; Banton, Sophia; Maddur, Mohan S.; McCausland, Megan; Chiu, Christopher; Canniff, Jennifer (2017-05-18). "Metabolic Phenotypes of Response to Vaccination in Humans". Cell. 169 (5): 862–877.e17. doi:10.1016/j.cell.2017.04.026. ISSN   0092-8674. PMC   5711477 . PMID   28502771.
  44. ↑ Meng, Chen; Kuster, Bernhard; Culhane, Aedín C; Gholami, Amin (2014). "A multivariate approach to the integration of multi-omics datasets". BMC Bioinformatics. 15 (1): 162. doi: 10.1186/1471-2105-15-162 . ISSN   1471-2105. PMC   4053266 . PMID   24884486.
  45. ↑ Ramos, Marcel; Schiffer, Lucas; Re, Angela; Azhar, Rimsha; Basunia, Azfar; Rodriguez, Carmen; Chan, Tiffany; Chapman, Phil; Davis, Sean R.; Gomez-Cabrero, David; Culhane, Aedin C.; Haibe-Kains, Benjamin; Hansen, Kasper D.; Kodali, Hanish; Louis, Marie S.; Mer, Arvind S.; Riester, Markus; Morgan, Martin; Carey, Vince; Waldron, Levi (1 November 2017). "Software for the Integration of Multiomics Experiments in Bioconductor". Cancer Research. 77 (21): e39–e42. doi:10.1158/0008-5472.CAN-17-0344. PMC   5679241 . PMID   29092936.
  46. ↑ Seonggyun Han, Younghee Lee (2017), IMAS, Bioconductor, doi:10.18129/b9.bioc.imas , retrieved 2019-06-28
  47. ↑ Karim Mezhoud [Aut, Cre] (2017), bioCancer, Bioconductor, doi:10.18129/b9.bioc.biocancer , retrieved 2019-06-28
  48. ↑ Hernandez-Ferrer, Carles; Ruiz-Arenas, Carlos; Beltran-Gomila, Alba; González, Juan R. (2017-01-17). "MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration". BMC Bioinformatics. 18 (1): 36. doi: 10.1186/s12859-016-1455-1 . ISSN   1471-2105. PMC   5240259 . PMID   28095799.
  49. ↑ "Reap the rewards of a biological insight engine". omicX. Retrieved 2019-06-26.
  50. ↑ Conesa, Ana; Dopazo, Joaquín; García-López, Federico; García-Alcalde, Fernando (2011-01-01). "Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data". Bioinformatics. 27 (1): 137–139. doi:10.1093/bioinformatics/btq594. ISSN   1367-4803. PMC   3008637 . PMID   21098431.
  51. ↑ Conesa, Ana; Pappas, Georgios J.; Furió-Tarí, Pedro; Balzano-Nogueira, Leandro; Martínez-Mira, Carlos; Tarazona, Sonia; Hernández-de-Diego, Rafael (2018-07-02). "PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data". Nucleic Acids Research. 46 (W1): W503–W509. doi:10.1093/nar/gky466. ISSN   0305-1048. PMC   6030972 . PMID   29800320.
  52. ↑ Chari, Raj; Coe, Bradley P.; Wedseltoft, Craig; Benetti, Marie; Wilson, Ian M.; Vucic, Emily A.; MacAulay, Calum; Ng, Raymond T.; Lam, Wan L. (2008-10-07). "SIGMA2: A system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes". BMC Bioinformatics. 9 (1): 422. doi: 10.1186/1471-2105-9-422 . ISSN   1471-2105. PMC   2571113 . PMID   18840289.
  53. ↑ Choi, Hyungwon; Ewing, Rob; Choi, Kwok Pui; Fermin, Damian; Koh, Hiromi W. L. (2018-07-23). "iOmicsPASS: a novel method for integration of multi-omics data over biological networks and discovery of predictive subnetworks". bioRxiv: 374520. doi:10.1101/374520. S2CID   92157115.
  54. ↑ Kanai, Masahiro; Maeda, Yuichi; Okada, Yukinori (2018-06-19). "Grimon: graphical interface to visualize multi-omics networks". Bioinformatics. 34 (22): 3934–3936. doi:10.1093/bioinformatics/bty488. ISSN   1367-4803. PMC   6223372 . PMID   29931190.
  55. ↑ Su, Andrew I.; Loguercio, Salvatore; Carland, Tristan M.; Ducom, Jean-Christophe; Gioia, Louis; Meißner, Tobias; Fisch, Kathleen M. (2015-06-01). "Omics Pipe: a community-based framework for reproducible multi-omics data analysis". Bioinformatics. 31 (11): 1724–1728. doi:10.1093/bioinformatics/btv061. ISSN   1367-4803. PMC   4443682 . PMID   25637560.
  56. ↑ Montague, Elizabeth; Stanberry, Larissa; Higdon, Roger; Janko, Imre; Lee, Elaine; Anderson, Nathaniel; Choiniere, John; Stewart, Elizabeth; Yandl, Gregory (June 2014). "MOPED 2.5—An Integrated Multi-Omics Resource: Multi-Omics Profiling Expression Database Now Includes Transcriptomics Data". OMICS: A Journal of Integrative Biology. 18 (6): 335–343. doi:10.1089/omi.2014.0061. ISSN   1536-2310. PMC   4048574 . PMID   24910945.
  57. ↑ Zhang, Bing; Wang, Jing; Straub, Peter; Vasaikar, Suhas V. (2018-01-04). "LinkedOmics: analyzing multi-omics data within and across 32 cancer types". Nucleic Acids Research. 46 (D1): D956–D963. doi:10.1093/nar/gkx1090. ISSN   0305-1048. PMC   5753188 . PMID   29136207.
  58. ↑ "LinkedOmics :: Login". www.linkedomics.org. Retrieved 2019-06-26.
  59. ↑ Kan, Zhengyan; Rejto, Paul A.; Roberts, Peter; Ding, Ying; AChing, Keith; Wang, Kai; Deng, Shibing; Schefzick, Sabine; Estrella, Heather (January 2016). "OASIS: web-based platform for exploring cancer multi-omics data". Nature Methods. 13 (1): 9–10. doi:10.1038/nmeth.3692. ISSN   1548-7105. PMID   26716558. S2CID   38621277.
  60. ↑ Wu, Jiaqi; Hu, Shuofeng; Chen, Yaowen; Li, Zongcheng; Zhang, Jian; Yuan, Hanyu; Shi, Qiang; Shao, Ningsheng; Ying, Xiaomin (May 2017). "BCIP: a gene-centered platform for identifying potential regulatory genes in breast cancer". Scientific Reports. 7 (1): 45235. Bibcode:2017NatSR...745235W. doi:10.1038/srep45235. ISSN   2045-2322. PMC   5361122 . PMID   28327601.
  61. ↑ Husi, Holger; Patel, Alisha; Fernandes, Marco (2018-11-12). "C/VDdb: A multi-omics expression profiling database for a knowledge-driven approach in cardiovascular disease (CVD)". PLOS ONE. 13 (11): e0207371. Bibcode:2018PLoSO..1307371F. doi: 10.1371/journal.pone.0207371 . ISSN   1932-6203. PMC   6231654 . PMID   30419069.
  62. ↑ Gupta, Amit Kumar; Kaur, Karambir; Rajput, Akanksha; Dhanda, Sandeep Kumar; Sehgal, Manika; Khan, Md. Shoaib; Monga, Isha; Dar, Showkat Ahmad; Singh, Sandeep (2016-09-16). "ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis". Scientific Reports. 6 (1): 32713. Bibcode:2016NatSR...632713G. doi:10.1038/srep32713. ISSN   2045-2322. PMC   5025660 . PMID   27633273.
  63. ↑ Tagkopoulos, Ilias; Violeta Zorraquino; Rai, Navneet; Kim, Minseung (2016-10-07). "Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli". Nature Communications. 7: 13090. Bibcode:2016NatCo...713090K. doi:10.1038/ncomms13090. ISSN   2041-1723. PMC   5059772 . PMID   27713404.
  64. ↑ Li, Guojing; Lu, Zhongfu; Lin, Jiandong; Hu, Yaowen; Yunping Huang; Wang, Baogen; Wu, Xinyi; Wu, Xiaohua; Xu, Pei (2018-02-26). "GourdBase: a genome-centered multi-omics database for the bottle gourd ( Lagenaria siceraria ), an economically important cucurbit crop". Scientific Reports. 8 (1): 3604. Bibcode:2018NatSR...8.3604W. doi:10.1038/s41598-018-22007-3. ISSN   2045-2322. PMC   5827520 . PMID   29483591.
  65. ↑ Liu, Haijun; Wang, Fan; Xiao, Yingjie; Tian, Zonglin; Wen, Weiwei; Zhang, Xuehai; Chen, Xi; Liu, Nannan; Li, Wenqiang (2016). "MODEM: multi-omics data envelopment and mining in maize". Database. 2016: baw117. doi:10.1093/database/baw117. ISSN   1758-0463. PMC   4976297 . PMID   27504011.
  66. ↑ Xu, Dong; Nguyen, Henry T.; Stacey, Gary; Gaudiello, Eric C.; Endacott, Ryan Z.; Zhang, Hongxin; Liu, Yang; Chen, Shiyuan; Fitzpatrick, Michael R. (2014-01-01). "Soybean knowledge base (SoyKB): a web resource for integration of soybean translational genomics and molecular breeding". Nucleic Acids Research. 42 (D1): D1245–D1252. doi:10.1093/nar/gkt905. ISSN   0305-1048. PMC   3965117 . PMID   24136998.
  67. ↑ Samaras, Patroklos; Schmidt, Tobias; Frejno, Martin; Gessulat, Siegfried; Reinecke, Maria; Jarzab, Anna; Zecha, Jana; Mergner, Julia; Giansanti, Piero; Ehrlich, Hans-Christian; Aiche, Stephan (2020-01-08). "ProteomicsDB: a multi-omics and multi-organism resource for life science research". Nucleic Acids Research. 48 (D1): D1153–D1163. doi:10.1093/nar/gkz974. ISSN   0305-1048. PMC   7145565 . PMID   31665479.
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