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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.


Metagene profiles reveal genomic signals. Schematic of metagene analysis. Normalized arrays of quantitative data (e.g. ribosomal P-sites; top) are taken at each position in the maximal spanning windows of multiple genes. These arrays are aligned at a landmark of interest (here, a start codon), and the median value of each column (nucleotide position), is taken to be the average (bottom)
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Fig6: Metagene profiles reveal genomic signals. Schematic of metagene analysis. Normalized arrays of quantitative data (e.g. ribosomal P-sites; top) are taken at each position in the maximal spanning windows of multiple genes. These arrays are aligned at a landmark of interest (here, a start codon), and the median value of each column (nucleotide position), is taken to be the average (bottom)

Mentions: Noise can obscure important biological signals within individual samples, but such signals frequently appear in population averages. For nucleotide-resolution analysis of NGS data, one particularly useful average is a metagene profile, in which arrays of quantitative data, corresponding to each position of a gene or region of interest, are aligned at some landmark — such as a start codon [1], or the beginning of a region encoding a signal peptide [22] — and a position-wise average is taken over the aligned arrays (Fig. 6). Metagene profiles have been used to reveal numerous biological signals, such as peaks of ribosome density at start or stop codons [1], ribosomal pauses over polybasic signals [23], and sites of engagement of hydrophobic nascent chains by the signal recognition particle [22].Fig. 6


Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data
Metagene profiles reveal genomic signals. Schematic of metagene analysis. Normalized arrays of quantitative data (e.g. ribosomal P-sites; top) are taken at each position in the maximal spanning windows of multiple genes. These arrays are aligned at a landmark of interest (here, a start codon), and the median value of each column (nucleotide position), is taken to be the average (bottom)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: Metagene profiles reveal genomic signals. Schematic of metagene analysis. Normalized arrays of quantitative data (e.g. ribosomal P-sites; top) are taken at each position in the maximal spanning windows of multiple genes. These arrays are aligned at a landmark of interest (here, a start codon), and the median value of each column (nucleotide position), is taken to be the average (bottom)
Mentions: Noise can obscure important biological signals within individual samples, but such signals frequently appear in population averages. For nucleotide-resolution analysis of NGS data, one particularly useful average is a metagene profile, in which arrays of quantitative data, corresponding to each position of a gene or region of interest, are aligned at some landmark — such as a start codon [1], or the beginning of a region encoding a signal peptide [22] — and a position-wise average is taken over the aligned arrays (Fig. 6). Metagene profiles have been used to reveal numerous biological signals, such as peaks of ribosome density at start or stop codons [1], ribosomal pauses over polybasic signals [23], and sites of engagement of hydrophobic nascent chains by the signal recognition particle [22].Fig. 6

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.