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

View Article: PubMed Central - PubMed

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.

Results: 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.

No MeSH data available.


Plastid streamlines analysis. a. The quality of a ribosome profiling dataset may be assayed by comparing the numbers of read counts in the first versus second half of each coding region. Plastid makes it possible to implement such analyses with few lines of easily readable code. b. Plastid readily integrates with the tools in the SciPy stack. Here, first- and second-half counts from (a) are plotted against each other using matplotlib, and a Pearson correlation coefficient calculated using SciPy
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Fig4: Plastid streamlines analysis. a. The quality of a ribosome profiling dataset may be assayed by comparing the numbers of read counts in the first versus second half of each coding region. Plastid makes it possible to implement such analyses with few lines of easily readable code. b. Plastid readily integrates with the tools in the SciPy stack. Here, first- and second-half counts from (a) are plotted against each other using matplotlib, and a Pearson correlation coefficient calculated using SciPy

Mentions: Finally, Plastid’s intended audience includes bench scientists and novices as well as seasoned bioinformaticians. For this reason, Plastid defines a minimal sets of data structures that, when possible, have human-readable names and are modeled on biological objects — such as spliced transcripts — rather than on more abstract notions. Users can thus leverage their biological knowledge when writing or reading code (Fig. 4).Fig. 4


Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data
Plastid streamlines analysis. a. The quality of a ribosome profiling dataset may be assayed by comparing the numbers of read counts in the first versus second half of each coding region. Plastid makes it possible to implement such analyses with few lines of easily readable code. b. Plastid readily integrates with the tools in the SciPy stack. Here, first- and second-half counts from (a) are plotted against each other using matplotlib, and a Pearson correlation coefficient calculated using SciPy
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5120557&req=5

Fig4: Plastid streamlines analysis. a. The quality of a ribosome profiling dataset may be assayed by comparing the numbers of read counts in the first versus second half of each coding region. Plastid makes it possible to implement such analyses with few lines of easily readable code. b. Plastid readily integrates with the tools in the SciPy stack. Here, first- and second-half counts from (a) are plotted against each other using matplotlib, and a Pearson correlation coefficient calculated using SciPy
Mentions: Finally, Plastid’s intended audience includes bench scientists and novices as well as seasoned bioinformaticians. For this reason, Plastid defines a minimal sets of data structures that, when possible, have human-readable names and are modeled on biological objects — such as spliced transcripts — rather than on more abstract notions. Users can thus leverage their biological knowledge when writing or reading code (Fig. 4).Fig. 4

View Article: PubMed Central - PubMed

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.

Results: 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.

No MeSH data available.