Genome Assemblies, Analyses, and Comparisons

National Library Of Medicine

Investigators

Linked publications, trials & patents

Abstract

NGS data analysis has advanced the design, implementation, and execution of many complex computational biology pipelines. For computational biologists, pipelines are multi-step methods that should follow the FAIR (Find-ability, Accessibility, Interoperability, and Reusability) data principles and guarantee reproducibility, portability and scalability (3). Workflow languages and managers, docker containers, and scientific computational notebooks have been adopted by the scientific community with the intention to improve reproducibility, portability, maintainability, and share-ability of computational pipelines. Following these principles, our group has developed PM4NGS, a project management framework for NGS data analysis. This framework is composed of the automatic creation of a standard organizational structure of directories and files, bioinformatics tool management using Docker/Biocontainers or Conda/Bioconda, data analysis pipelines in the Common Workflow Language (CWL) format and pre-configured Jupyter notebooks with minimum Python code. The framework was designed as a fully interactive tool for data analysis on personal laptops or workstations. It can also be used as an educational tool to train new bioinformaticians on how to organize an NGS data analysis project showing a detailed view of the pipeline components. PM4NGS currently includes four NGS data analysis workflows as templates: differential gene expression and GO enrichment analysis from RNA-Seq data, differential binding analysis from ChIP-Seq data, DNA motif binding detection from ChIP-exo data and transcriptome assembly, including annotation and submission for unannotated organisms. These templates can be reused or modified to create new computational biology workflows. This framework aims to reduce the gap between researchers in experimental laboratories producing NGS data and the workflows for the data analysis. The complexity of working with multiple directories, data files and programs on the Linux command line interface is completely managed by PM4NGS allowing researchers to focus on result interpretation.