Shiba (v0.5.1)

A versatile computational method for robust identification of differential RNA splicing. Shiba/scShiba can quantify and identify differential splicing events (DSEs) from short-read bulk RNA-seq data and single-cell RNA-seq data. Shiba and scShiba are also implemented as Snakemake workflows, SnakeShiba and SnakeScShiba, respectively.

See CHANGELOG.md for the latest updates.

Overview

Shiba comprises four main steps:

1. Transcript assembly: Assemble transcripts from RNA-seq reads using StringTie2
2. Splicing event identification: Identify alternative mRNA splicing events from assembled transcripts
3. Read counting: Count reads mapped to each splicing event using RegTools and featureCounts
4. Statistical analysis: Identify DSEs based on Fisher's exact test

Installation

A docker image is available at Docker Hub.

docker pull naotokubota/shiba

Usage

Manual for Shiba is available at https://sika-zheng-lab.github.io/Shiba/.

Shiba

python shiba.py -p 32 config.yaml

SnakeShiba, Snakemake-based workflow of Shiba

snakemake -s snakeshiba.smk --configfile config.yaml --cores 32 --use-singularity

scShiba, a single-cell RNA-seq version of Shiba

python scshiba.py -p 32 config.yaml

SnakeScShiba, Snakemake-based workflow of scShiba

snakemake -s snakescshiba.smk --configfile config.yaml --cores 32 --use-singularity

Citation

Kubota N, Chen L, Zheng S. (2024). Shiba: A unified computational method for robust identification of differential RNA splicing across platforms. bioRxiv 2024.05.30.596331

Authors

• Naoto Kubota (0000-0003-0612-2300)
• Liang Chen (0000-0001-6164-4553)
• Sika Zheng (0000-0002-0573-4981)