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  pyGenomeTracks: reproducible plots for multivariate genomic datasets

Lopez-Delisle, L., Rabbani, L., Wolff, J., Bhardwaj, V., Backofen, R., Grüning, B., et al. (2020). pyGenomeTracks: reproducible plots for multivariate genomic datasets. Bioinformatics, 692, 1-2. doi:org/10.1093/bioinformatics/btaa692.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-E6D1-9 Version Permalink: https://hdl.handle.net/21.11116/0000-0008-7442-B
Genre: Journal Article

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Lopez et al. 2020.pdf (Publisher version), 479KB
 
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Lopez et al. 2020.pdf
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Copyright Date:
2020
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The Author(s)
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 Creators:
Lopez-Delisle, Lucille1, Author
Rabbani, Leily2, Author
Wolff, Joachim2, Author
Bhardwaj, Vivek1, Author
Backofen, Rolf2, Author
Grüning, Björn2, Author
Ramírez, Fidel1, Author
Manke, Thomas1, Author           
Affiliations:
1Max Planck Institute of Immunobiology and Epigenetics, Max Planck Society, ou_2243644              
2External Organizations, ou_persistent22              

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 Abstract: Motivation: Generating publication ready plots to display multiple genomic tracks can pose a serious challenge. Making desirable and accurate figures requires considerable effort. This is usually done by hand or using a vector graphic software.

Results: pyGenomeTracks (PGT) is a modular plotting tool that easily combines multiple tracks. It enables a reproducible and standardized generation of highly customizable and publication ready images.

Availability and implementation: PGT is available through a graphical interface on https://usegalaxy.eu and through the command line. It is provided on conda via the bioconda channel, on pip and it is openly developed on github: https://github.com/deeptools/pyGenomeTracks.

Contact: fidel.ramirez@boehringer-ingelheim.com

Supplementary information: Supplementary data: are available at Bioinformatics online.

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Language(s): eng - English
 Dates: 2020
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: org/10.1093/bioinformatics/btaa692
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 692 Sequence Number: - Start / End Page: 1 - 2 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991