Trackplot: A flexible toolkit for combinatorial analysis of genomic data

Yiming Zhang; Ran Zhou; Lunxu Liu; Lu Chen; Yuan Wang

doi:10.1371/journal.pcbi.1011477

Trackplot: A flexible toolkit for combinatorial analysis of genomic data

PLoS Comput Biol. 2023 Sep 5;19(9):e1011477. doi: 10.1371/journal.pcbi.1011477. eCollection 2023 Sep.

Authors

Yiming Zhang^{1

2}, Ran Zhou¹, Lunxu Liu², Lu Chen³, Yuan Wang¹

Affiliations

¹ Department of Neurosurgery and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
² Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
³ State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.

Abstract

Here, we introduce Trackplot, a Python package for generating publication-quality visualization by a programmable and interactive web-based approach. Compared to the existing versions of programs generating sashimi plots, Trackplot offers a versatile platform for visually interpreting genomic data from a wide variety of sources, including gene annotation with functional domain mapping, isoform expression, isoform structures identified by scRNA-seq and long-read sequencing, as well as chromatin accessibility and architecture without any preprocessing, and also offers a broad degree of flexibility for formats of output files that satisfy the requirements of major journals. The Trackplot package is an open-source software which is freely available on Bioconda (https://anaconda.org/bioconda/trackplot), Docker (https://hub.docker.com/r/ygidtu/trackplot), PyPI (https://pypi.org/project/trackplot/) and GitHub (https://github.com/ygidtu/trackplot), and a built-in web server for local deployment is also provided.

Copyright: © 2023 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Chromatin*
Genomics*
Molecular Sequence Annotation
Records
Software

Substances

Chromatin

Grants and funding

This work is supported by the National Natural Science Foundation of China (82303975 to R.Z. and 82273117 to Y.W), the National Key Research and Development Program of China, Stem Cell and Translational Research (2022YFA1105200 to Y.W.), the China Postdoctoral Science Foundation (2022TQ0226 to R.Z.), and Post-Doctor Research Project, West China Hospital, Sichuan University (2023HXBH100 to R.Z.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.