SnakeLines: integrated set of computational pipelines for sequencing reads

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Massively parallel sequencing generates large datasets requiring complex bioinformatics processing.
• Lack of computational expertise and inconsistent analysis environments hinder genomic data interpretation and reproducibility.
• There is a need for user-friendly, reproducible bioinformatics pipelines for genomic analyses.

Purpose of the Study:

• To develop an easily extensible set of computational pipelines for processing sequencing reads.
• To address challenges in bioinformatics processing for researchers without computational backgrounds.
• To ensure reproducibility of genomic analyses across different computational centers.

Main Methods:

• SnakeLines provides a set of computational pipelines for mapping, assembly, variant calling, viral identification, transcriptomics, and metagenomics.
• Analysis steps and parameters are configurable via a single file.
• Pipelines are embedded in virtual environments for resource isolation, rapid deployment, and cross-platform reproducibility on Unix-based systems.

Main Results:

• SnakeLines offers a powerful framework for automating bioinformatics analyses.
• The framework emphasizes simple setup, modification, extensibility, and reproducibility.
• It is routinely used in research projects, including SARS-CoV-2 surveillance.

Conclusions:

• SnakeLines simplifies complex bioinformatics workflows, making genomic data analysis more accessible.
• The framework enhances the reproducibility and efficiency of sequencing data processing.
• SnakeLines is a valuable tool for various research applications, particularly in public health surveillance.