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

Overview of attention for article published in BMC Genomics, November 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
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10 X users
facebook
1 Facebook page

Citations

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141 Dimensions

Readers on

mendeley
133 Mendeley
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2 CiteULike
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Title
Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data
Published in
BMC Genomics, November 2016
DOI 10.1186/s12864-016-3278-x
Pubmed ID
Authors

Joshua G. Dunn, Jonathan S. Weissman

Abstract

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 .

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Japan 1 <1%
United Kingdom 1 <1%
Unknown 129 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 26%
Student > Ph. D. Student 32 24%
Student > Master 13 10%
Student > Bachelor 10 8%
Student > Doctoral Student 6 5%
Other 14 11%
Unknown 24 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 47 35%
Agricultural and Biological Sciences 34 26%
Computer Science 6 5%
Engineering 3 2%
Nursing and Health Professions 2 2%
Other 13 10%
Unknown 28 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 December 2016.
All research outputs
#2,292,515
of 22,903,988 outputs
Outputs from BMC Genomics
#702
of 10,674 outputs
Outputs of similar age
#47,942
of 415,136 outputs
Outputs of similar age from BMC Genomics
#21
of 243 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,674 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 415,136 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 243 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.