Sashimi.py: a flexible toolkit for combinatorial analysis of genomic data

Abstract

Simultaneously visualizing how isoform expression, protein-DNA/RNA interactions, accessibility, and architecture of chromatin differs across condition and cell types could inform our understanding on regulatory mechanisms and functional consequences of alternative splicing. However, the existing versions of tools generating sashimi plots remain inflexible, complicated, and user-unfriendly for integrating data sources from multiple bioinformatic formats or various genomics assays. Thus, a more scalable visualization tool is necessary to broaden the scope of sashimi plots. Here, we introduce sashimi.py, a Python package for generating publication-quality visualization by a programmable and interactive web-based approach. Sashimi.py is a platform to visually interpret genomic data from a large variety of data sources including single-cell RNA-seq, protein-DNA/RNA interactions, long-reads sequencing data, and Hi-C data without any preprocessing, and also offers a broad degree of flexibility for formats of output files that satisfy the requirements of major journals. The Sashimi.py package is an open-source software which is freely available on Bioconda (https://anaconda.org/bioconda/sashimi-py), Docker, PyPI (https://pypi.org/project/sashimi.py/) and GitHub (https://github.com/ygidtu/sashimi.py), and a built-in web server for local deployment is also provided.