Svist4get : A simple visualization tool for genomic tracks from sequencing experiments
(2019) In BMC Bioinformatics 20. p.1-6- Abstract
Background: High-throughput sequencing often provides a foundation for experimental analyses in the life sciences. For many such methods, an intermediate layer of bioinformatics data analysis is the genomic signal track constructed by short read mapping to a particular genome assembly. There are many software tools to visualize genomic tracks in a web browser or with a stand-alone graphical user interface. However, there are only few command-line applications suitable for automated usage or production of publication-ready visualizations. Results: Here we present svist4get, a command-line tool for customizable generation of publication-quality figures based on data from genomic signal tracks. Similarly to generic genome browser software,... (More)
Background: High-throughput sequencing often provides a foundation for experimental analyses in the life sciences. For many such methods, an intermediate layer of bioinformatics data analysis is the genomic signal track constructed by short read mapping to a particular genome assembly. There are many software tools to visualize genomic tracks in a web browser or with a stand-alone graphical user interface. However, there are only few command-line applications suitable for automated usage or production of publication-ready visualizations. Results: Here we present svist4get, a command-line tool for customizable generation of publication-quality figures based on data from genomic signal tracks. Similarly to generic genome browser software, svist4get visualizes signal tracks at a given genomic location and is able to aggregate data from several tracks on a single plot along with the transcriptome annotation. The resulting plots can be saved as the vector or high-resolution bitmap images. We demonstrate practical use cases of svist4get for Ribo-Seq and RNA-Seq data. Conclusions: svist4get is implemented in Python 3 and runs on Linux. The command-line interface of svist4get allows for easy integration into bioinformatics pipelines in a console environment. Extra customization is possible through configuration files and Python API. For convenience, svist4get is provided as pypi package. The source code is available at https://bitbucket.org/artegorov/svist4get/
(Less)
- author
- Egorov, Artyom A.
LU
; Sakharova, Ekaterina A. ; Anisimova, Aleksandra S. ; Dmitriev, Sergey E. ; Gladyshev, Vadim N. and Kulakovskiy, Ivan V.
- publishing date
- 2019-03-06
- type
- Contribution to journal
- publication status
- published
- keywords
- Genome browser, Genomic tracks, High-throughput sequencing, Next-generation sequencing, Python, Ribo-Seq, RNA-Seq, Visualization
- in
- BMC Bioinformatics
- volume
- 20
- article number
- 113
- pages
- 1 - 6
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:30841857
- scopus:85062500730
- ISSN
- 1471-2105
- DOI
- 10.1186/s12859-019-2706-8
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2019 The Author(s).
- id
- 00437574-9a1e-4ef8-ad63-0a5a22980444
- date added to LUP
- 2022-08-24 23:16:29
- date last changed
- 2025-03-18 09:46:28
@article{00437574-9a1e-4ef8-ad63-0a5a22980444, abstract = {{<p>Background: High-throughput sequencing often provides a foundation for experimental analyses in the life sciences. For many such methods, an intermediate layer of bioinformatics data analysis is the genomic signal track constructed by short read mapping to a particular genome assembly. There are many software tools to visualize genomic tracks in a web browser or with a stand-alone graphical user interface. However, there are only few command-line applications suitable for automated usage or production of publication-ready visualizations. Results: Here we present svist4get, a command-line tool for customizable generation of publication-quality figures based on data from genomic signal tracks. Similarly to generic genome browser software, svist4get visualizes signal tracks at a given genomic location and is able to aggregate data from several tracks on a single plot along with the transcriptome annotation. The resulting plots can be saved as the vector or high-resolution bitmap images. We demonstrate practical use cases of svist4get for Ribo-Seq and RNA-Seq data. Conclusions: svist4get is implemented in Python 3 and runs on Linux. The command-line interface of svist4get allows for easy integration into bioinformatics pipelines in a console environment. Extra customization is possible through configuration files and Python API. For convenience, svist4get is provided as pypi package. The source code is available at https://bitbucket.org/artegorov/svist4get/</p>}}, author = {{Egorov, Artyom A. and Sakharova, Ekaterina A. and Anisimova, Aleksandra S. and Dmitriev, Sergey E. and Gladyshev, Vadim N. and Kulakovskiy, Ivan V.}}, issn = {{1471-2105}}, keywords = {{Genome browser; Genomic tracks; High-throughput sequencing; Next-generation sequencing; Python; Ribo-Seq; RNA-Seq; Visualization}}, language = {{eng}}, month = {{03}}, pages = {{1--6}}, publisher = {{BioMed Central (BMC)}}, series = {{BMC Bioinformatics}}, title = {{Svist4get : A simple visualization tool for genomic tracks from sequencing experiments}}, url = {{http://dx.doi.org/10.1186/s12859-019-2706-8}}, doi = {{10.1186/s12859-019-2706-8}}, volume = {{20}}, year = {{2019}}, }