Software pipelines for RNA-Seq, ChIP-Seq and germline variant calling analyses in common workflow language (CWL).

Area of Science:

• Bioinformatics and Computational Biology
• Genomics and Molecular Biology
• Data Science and Machine Learning

Background:

• Reproducibility in data analysis is crucial, especially for large-scale High-throughput Sequencing (HTS) datasets.
• Automating data analysis pipelines addresses the need for consistent and reliable results in genomics research.
• Challenges in bioinformatics include software incompatibility and complex configuration, hindering reproducibility.

Purpose of the Study:

• To develop and evaluate automated Common Workflow Language (CWL) pipelines for HTS data analysis.
• To assess the performance of CWL workflows in reproducing published results and detecting genetic variants.
• To provide a flexible, reusable, and open-source resource for standard bioinformatics analyses.

Main Methods:

• Implementation of automated workflows using Common Workflow Language (CWL).
• Analysis of RNA-Seq, ChIP-Seq, and Germline variant calling experiments.
• Validation through reproduction of Chronic Lymphocytic Leukemia (CLL) study results and analysis of Genome in a Bottle (GIAB) Consortium data.

Main Results:

• CWL-implemented workflows demonstrated high accuracy in reproducing prior findings.
• Successful identification of significant biomarkers in Chronic Lymphocytic Leukemia (CLL) datasets.
• Accurate detection of germline Single Nucleotide Polymorphism (SNP) and small Insertion/Deletion (INDEL) variants in whole genome sequencing data.

Conclusions:

• CWL pipelines offer significant advantages in reproducibility and reusability for bioinformatics analyses.
• Containerization combined with CWL overcomes software compatibility and configuration challenges.
• The developed CWL workflows are flexible, adaptable, and serve as an open resource for short-read data analysis, promoting automation and cross-platform compatibility.