Next-Generation Genomics: an Integrative Approach

By R. Hawkins, Gary Chung Hon, Bing Ren. Nature reviews genetics, 2010

626 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a1ef4a4c6d6493860920986b6ab483e6981c0d7e

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In Molecular Biology

Citations in the corpus

Charting histone modifications and the functional organization of mammalian genomes (2011, background): “These technologies and their integration have been extensively reviewed elsewher”
A Comparative Encyclopedia of DNA Elements in the Mouse Genome (2014, methodology): “Advances in DNA sequencing technologies have led to the development of RNA-seq, DNase-seq, ChIP-seq, and other methods that allow rapid and genome-wide analysis of transcription, replication, chromatin accessibility, chromatin modifications, and transcription factor binding in cells 11 .”
ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases (2014, background): “The bottleneck is now in the processing and interpretation of the massive amount of data that are being generated [70,71].”
From RNA-seq reads to differential expression results (2010, background): “There is wide scope for integrating the results of RNAseq data with other sources of biological data to establish a more complete picture of gene regulation [69].”
Quake: quality-aware detection and correction of sequencing errors (2010, methodology): “Massively parallel DNA sequencing has become a prominent tool in biological research [1,2].”