Dataset Information

Next-generation genomics: an integrative approach.

ABSTRACT: 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.

SUBMITTER: Hawkins RD

PROVIDER: S-EPMC3321268 | biostudies-other | 2010 Jul

REPOSITORIES: biostudies-other

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Next-generation genomics: an integrative approach.

Hawkins R David RD Hon Gary C GC Ren Bing B

Nature reviews. Genetics 20100701 7

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 prov ...[more]

PMID: 20531367

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Project description:Next-generation sequencing (NGS) informs many biological questions with unprecedented depth and nucleotide resolution. These assays have created a need for analytical tools that enable users to manipulate data nucleotide-by-nucleotide robustly and easily. Furthermore, because many NGS assays encode information jointly within multiple properties of read alignments - for example, in ribosome profiling, the locations of ribosomes are jointly encoded in alignment coordinates and length - analytical tools are often required to extract the biological meaning from the alignments before analysis. Many assay-specific pipelines exist for this purpose, but there remains a need for user-friendly, generalized, nucleotide-resolution tools that are not limited to specific experimental regimes or analytical workflows.Plastid is a Python library designed specifically for nucleotide-resolution analysis of genomics and NGS data. As such, Plastid is designed to extract assay-specific information from read alignments while retaining generality and extensibility to novel NGS assays. Plastid represents NGS and other biological data as arrays of values associated with genomic or transcriptomic positions, and contains configurable tools to convert data from a variety of sources to such arrays. Plastid also includes numerous tools to manipulate even discontinuous genomic features, such as spliced transcripts, with nucleotide precision. Plastid automatically handles conversion between genomic and feature-centric coordinates, accounting for splicing and strand, freeing users of burdensome accounting. Finally, Plastid's data models use consistent and familiar biological idioms, enabling even beginners to develop sophisticated analytical workflows with minimal effort.Plastid is a versatile toolkit that has been used to analyze data from multiple NGS assays, including RNA-seq, ribosome profiling, and DMS-seq. It forms the genomic engine of our ORF annotation tool, ORF-RATER, and is readily adapted to novel NGS assays. Examples, tutorials, and extensive documentation can be found at https://plastid.readthedocs.io .

Dataset Information

Next-generation genomics: an integrative approach.

Publications

Next-generation genomics: an integrative approach.

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