Format

Send to

Choose Destination
BMC Genomics. 2016 Nov 22;17(1):958.

Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data.

Dunn JG1,2,3,4, Weissman JS5,6,7,8.

Author information

1
California Institute of Quantitative Biosciences, San Francisco, USA. joshua.g.dunn@gmail.com.
2
Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA. joshua.g.dunn@gmail.com.
3
Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA. joshua.g.dunn@gmail.com.
4
Center for RNA Systems Biology, Berkeley, CA, USA. joshua.g.dunn@gmail.com.
5
California Institute of Quantitative Biosciences, San Francisco, USA.
6
Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA.
7
Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA.
8
Center for RNA Systems Biology, Berkeley, CA, USA.

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSIONS:

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 .

KEYWORDS:

Bioinformatics; Genomics; Python; Ribosome profiling; Sequencing

PMID:
27875984
PMCID:
PMC5120557
DOI:
10.1186/s12864-016-3278-x
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center