-omics

-omics

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Cross-sectional analysis of the various hierarchical levels in which the expression of biological information is organized in the cell.

 

Introductory reading

The Encode project is a current paradigm for the integration of multiple -omics datasets for comprehensive annotation of the human genome.

ENCODE Project Consortium (2011) A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol 9:e1001046. (pmid: 21526222)

[ PubMed ] [ DOI ] Abstract The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.

 

Contents

See also:

• -omics.
• List of omics topics in biology

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• Genome, Epigenome, Variome
• Transcriptome, Exome, Regulome, miRNA networks
• Proteome, Complexome, Secretome, Kinome (cf. Kinases)
• Glycome
• Lipidome
• Metabolome, Reactome

Other -omes

 

Phenome, Physiome

 

Microbiome

Levy & Borenstein (2013) Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc Natl Acad Sci U.S.A 110:12804-9. (pmid: 23858463)

[ PubMed ] [ DOI ] Abstract The human microbiome plays a key role in human health and is associated with numerous diseases. Metagenomic-based studies are now generating valuable information about the composition of the microbiome in health and in disease, demonstrating nonneutral assembly processes and complex co-occurrence patterns. However, the underlying ecological forces that structure the microbiome are still unclear. Specifically, compositional studies alone with no information about mechanisms of interaction, potential competition, or syntrophy, cannot clearly distinguish habitat-filtering and species assortment assembly processes. To address this challenge, we introduce a computational framework, integrating metagenomic-based compositional data with genome-scale metabolic modeling of species interaction. We use in silico metabolic network models to predict levels of competition and complementarity among 154 microbiome species and compare predicted interaction measures to species co-occurrence. Applying this approach to two large-scale datasets describing the composition of the gut microbiome, we find that species tend to co-occur across individuals more frequently with species with which they strongly compete, suggesting that microbiome assembly is dominated by habitat filtering. Moreover, species' partners and excluders exhibit distinct metabolic interaction levels. Importantly, we show that these trends cannot be explained by phylogeny alone and hold across multiple taxonomic levels. Interestingly, controlling for host health does not change the observed patterns, indicating that the axes along which species are filtered are not fully defined by macroecological host states. The approach presented here lays the foundation for a reverse-ecology framework for addressing key questions concerning the assembly of host-associated communities and for informing clinical efforts to manipulate the microbiome.

Greenblum et al. (2013) Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities. Curr Opin Biotechnol 24:810-20. (pmid: 23623295)

[ PubMed ] [ DOI ] Abstract The human microbiome represents a vastly complex ecosystem that is tightly linked to our development, physiology, and health. Our increased capacity to generate multiple channels of omic data from this system, brought about by recent advances in high throughput molecular technologies, calls for the development of systems-level methods and models that take into account not only the composition of genes and species in a microbiome but also the interactions between these components. Such models should aim to study the microbiome as a community of species whose metabolisms are tightly intertwined with each other and with that of the host, and should be developed with a view towards an integrated, comprehensive, and predictive modeling framework. Here, we review recent work specifically in metabolic modeling of the human microbiome, highlighting both novel methodologies and pressing challenges. We discuss various modeling approaches that lay the foundation for a full-scale predictive model, focusing on models of interactions between microbial species, metagenome-scale models of community-level metabolism, and models of the interaction between the microbiome and the host. Continued development of such models and of their integration into a multi-scale model of the microbiome will lead to a deeper mechanistic understanding of how variation in the microbiome impacts the host, and will promote the discovery of clinically relevant and ecologically relevant insights from the rich trove of data now available.

   

Further reading and resources

Hiller et al. (2013) Computational methods to detect conserved non-genic elements in phylogenetically isolated genomes: application to zebrafish. Nucleic Acids Res 41:e151. (pmid: 23814184)

[ PubMed ] [ DOI ] Abstract Many important model organisms for biomedical and evolutionary research have sequenced genomes, but occupy a phylogenetically isolated position, evolutionarily distant from other sequenced genomes. This phylogenetic isolation is exemplified for zebrafish, a vertebrate model for cis-regulation, development and human disease, whose evolutionary distance to all other currently sequenced fish exceeds the distance between human and chicken. Such large distances make it difficult to align genomes and use them for comparative analysis beyond gene-focused questions. In particular, detecting conserved non-genic elements (CNEs) as promising cis-regulatory elements with biological importance is challenging. Here, we develop a general comparative genomics framework to align isolated genomes and to comprehensively detect CNEs. Our approach integrates highly sensitive and quality-controlled local alignments and uses alignment transitivity and ancestral reconstruction to bridge large evolutionary distances. We apply our framework to zebrafish and demonstrate substantially improved CNE detection and quality compared with previous sets. Our zebrafish CNE set comprises 54 533 CNEs, of which 11 792 (22%) are conserved to human or mouse. Our zebrafish CNEs (http://zebrafish.stanford.edu) are highly enriched in known enhancers and extend existing experimental (ChIP-Seq) sets. The same framework can now be applied to the isolated genomes of frog, amphioxus, Caenorhabditis elegans and many others.

Rosenbloom et al. (2013) ENCODE data in the UCSC Genome Browser: year 5 update. Nucleic Acids Res 41:D56-63. (pmid: 23193274)

[ PubMed ] [ DOI ] Abstract The Encyclopedia of DNA Elements (ENCODE), http://encodeproject.org, has completed its fifth year of scientific collaboration to create a comprehensive catalog of functional elements in the human genome, and its third year of investigations in the mouse genome. Since the last report in this journal, the ENCODE human data repertoire has grown by 898 new experiments (totaling 2886), accompanied by a major integrative analysis. In the mouse genome, results from 404 new experiments became available this year, increasing the total to 583, collected during the course of the project. The University of California, Santa Cruz, makes this data available on the public Genome Browser http://genome.ucsc.edu for visual browsing and data mining. Download of raw and processed data files are all supported. The ENCODE portal provides specialized tools and information about the ENCODE data sets.

ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57-74. (pmid: 22955616)

[ PubMed ] [ DOI ] Abstract The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

Samuels & Rouleau (2011) The case for locus-specific databases. Nat Rev Genet 12:378-9. (pmid: 21540879)

[ PubMed ] [ DOI ] Abstract Locus-specific databases are the most useful repositories of the sequence information underlying medical genetic conditions and, for this reason, they need our continued support.

McLean et al. (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28:495-501. (pmid: 20436461)

[ PubMed ] [ DOI ] Abstract We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.