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Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data

Overview of attention for article published in BMC Bioinformatics, April 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
9 tweeters

Citations

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22 Dimensions

Readers on

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40 Mendeley
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Title
Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data
Published in
BMC Bioinformatics, April 2017
DOI 10.1186/s12859-017-1621-0
Pubmed ID
Authors

William W. Greenwald, He Li, Erin N. Smith, Paola Benaglio, Naoki Nariai, Kelly A. Frazer

Abstract

Genomic interaction studies use next-generation sequencing (NGS) to examine the interactions between two loci on the genome, with subsequent bioinformatics analyses typically including annotation, intersection, and merging of data from multiple experiments. While many file types and analysis tools exist for storing and manipulating single locus NGS data, there is currently no file standard or analysis tool suite for manipulating and storing paired-genomic-loci: the data type resulting from "genomic interaction" studies. As genomic interaction sequencing data are becoming prevalent, a standard file format and tools for working with these data conveniently and efficiently are needed. This article details a file standard and novel software tool suite for working with paired-genomic-loci data. We present the paired-genomic-loci (PGL) file standard for genomic-interactions data, and the accompanying analysis tool suite "pgltools": a cross platform, pypy compatible python package available both as an easy-to-use UNIX package, and as a python module, for integration into pipelines of paired-genomic-loci analyses. Pgltools is a freely available, open source tool suite for manipulating paired-genomic-loci data. Source code, an in-depth manual, and a tutorial are available publicly at www.github.com/billgreenwald/pgltools , and a python module of the operations can be installed from PyPI via the PyGLtools module.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
France 1 3%
Lithuania 1 3%
United Kingdom 1 3%
Spain 1 3%
Unknown 35 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 35%
Student > Ph. D. Student 8 20%
Student > Doctoral Student 3 8%
Student > Master 3 8%
Student > Bachelor 2 5%
Other 3 8%
Unknown 7 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 35%
Agricultural and Biological Sciences 8 20%
Computer Science 3 8%
Medicine and Dentistry 2 5%
Neuroscience 2 5%
Other 2 5%
Unknown 9 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 19 June 2019.
All research outputs
#4,488,769
of 15,473,495 outputs
Outputs from BMC Bioinformatics
#1,948
of 5,649 outputs
Outputs of similar age
#91,765
of 267,288 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 24 outputs
Altmetric has tracked 15,473,495 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 5,649 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 65% 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 267,288 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.