The integration of transcriptomic, genetic, genomic, epigenetic and network interaction data is crucial for a unified view of biological processes and to advance our understanding of human disease and biology.
The genome sequence is a scaffold on which known annotations and experimental data can be assembled. It is useful to view these different levels of information together on a genome browser.
Data integration can be used to identify functional elements in the genome, explore the function of genetic variation and improve understanding of gene regulation.
Given large multidimensional data sets with minimal parameters, unsupervised learning techniques can be used to identify frequently occurring patterns in the data and therefore to suggest hypotheses.
Carefully designed computational experiments for supervised integration can be used to test hypotheses on a global scale. Other supervised approaches, such as Bayesian networks, can also generate hypotheses of function.
There are a range of online and stand-alone tools available to bench scientists for tackling large-scale data sets.
Several analytical hurdles remain, which are being addressed by bioinformaticians.
Integrating results from diverse experiments is an essential process in our effort to understand the logic of complex systems, such as development, homeostasis and responses to the environment. With the advent of high-throughput methods — including genome-wide association (GWA) studies, chromatin immunoprecipitation followed by sequencing (ChIP–seq) and RNA sequencing (RNA–seq) — acquisition of genome-scale data has never been easier. Epigenomics, transcriptomics, proteomics and genomics each provide an insightful, and yet one-dimensional, view of genome function; integrative analysis promises a unified, global view. However, the large amount of information and diverse technology platforms pose multiple challenges for data access and processing. This Review discusses emerging issues and strategies related to data integration in the era of next-generation genomics.
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We apologize to those authors whose work we were unable to reference owing to limitations of space. R.D.H is supported by a postdoctoral fellowship from the American Cancer Society. We acknowledge generous funding from the Ludwig Institute for Cancer Research, the US National Institutes of Health, the California Institute of Regenerative Medicine and the Juvenile Diabetes Research Foundation. We thank the anonymous reviewers for their valuable comments on earlier versions of this Review.
The authors declare no competing financial interests.
- Next-generation sequencing
Here, we define this as the use of established sequencing platforms, including the Illumina/Solexa Genome Analyzer, Roche/454 Genome Sequencer and Applied Biosystems SOLiD platforms, as well as newer platforms, such as the Helicos and Pacific Biosciences platforms.
- Reduced representation bisulphite sequencing
This technique cuts genomic DNA with restriction enzymes to enrich for CG-rich regions, which are then converted through bisulphite treatment and sequenced with next-generation sequencing. Bisulphite treatment converts unmethylated C to uracil — which appears as T in sequencing reads — while leaving methylated C intact.
Methylated DNA is immunoprecipitated with an antibody against methylated cytosine and then sequenced by next-generation sequencing.
(Also known as bisulphite conversion followed by sequencing (BS–seq).) Methylated DNA is identified by shotgun sequencing of bisulphite-converted DNA.
- Sequence capture
This uses oligonucleotide microarrays or oligonucleotide-coupled beads to select for regions of the genome, such as all exons (exome sequencing) for targeted sequencing.
- RNA sequencing
(RNA–seq.) RNA isolated from cells are sequenced by next-generation sequencing after conversion to cDNA.
- Nuclear run-on
An assay that directly measures the transcriptional activity of a gene by incorporation of labelled UTP into its mRNA.
Small, highly conserved basic proteins, found in the chromatin of all eukaryotic cells, which associate with DNA to form a nucleosome. The amino-terminal tails of histones are subject to various post-translational modifications.
- Chromatin immunoprecipitation
A technique used to identify potential regulatory sequences by isolating soluble DNA chromatin extracts (complexes of DNA and protein) using antibodies that recognize specific DNA-binding proteins.
- DNase I hypersensitivity site footprinting
An assay that identifies regions of the genome that lack nucleosome structure and are therefore readily degraded by the enzyme DNase I. Such regions tend to be associated with transcriptional activity. When coupled with sequencing, the ends of DNA fragments generated by treatment of chromatin with DNase I are sequenced.
A technique similar to ChIP–seq in which proteins bound to RNA — such as splicing factors — are immunoprecipitated and the RNA fragments are sequenced.
An assay system in which one protein is fused to an activation domain and the other to a DNA-binding domain, and both fusion proteins are expressed in cells. Expression of a reporter gene indicates that the two proteins physically interact.
- Epistatic miniarray profiles
These are created by screening the fitness of double mutants in a high-throughput manner. The results, when analysed as a whole, can reveal both positive and negative genetic interactions between genes and provide insights into biological pathways and protein–protein complexes in the cell.
- Single-nucleotide variant
Sequence variations that include insertions and deletions in addition to base substitutions (which are known as SNPs).
- Genomic imprinting
The epigenetic marking of a gene on the basis of parental origin, which results in monoallelic expression.
- Cap analysis of gene expression
(CAGE.) The high-throughput sequencing of concatamers of DNA tags that are derived from the initial nucleotides of 5′ mRNA.
- Formaldehyde-assisted isolation of regulatory elements followed by sequencing
(FAIRE–seq.) This technique isolates nucleosome-free regions of DNA from chromatin during phenol:chloroform extraction.
The conversion of a continuous signal to a discrete signal.
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Hawkins, R., Hon, G. & Ren, B. Next-generation genomics: an integrative approach. Nat Rev Genet 11, 476–486 (2010). https://doi.org/10.1038/nrg2795
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