Seq2pathway: an R/Bioconductor package for pathway analysis of next-generation sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Advances in genome research necessitate sophisticated tools for analyzing genomic loci.
• Next-generation sequencing (NGS) data generates large volumes of information requiring effective summarization.
• Understanding functional gene-set enrichment is crucial for biological discovery.

Purpose of the Study:

• To introduce Seq2pathway, an R/Python wrapper for pathway analysis of genomic loci.
• To enable the association of biological significance of genomic loci with their target transcripts.
• To provide enhanced capabilities for discovering collective pathway effects from NGS data.

Main Methods:

• Seq2pathway quantifies gene-level values and summarizes them into pathway scores.
• It is designed to isolate systematic disturbances and common biological underpinnings.
• The tool integrates analysis of both coding genes and cis-regulation of non-coding elements.

Main Results:

• Seq2pathway facilitates the interpretation of genomic loci in the context of biological pathways.
• It enhances the discovery of collective pathway effects by considering diverse genomic elements.
• The package provides Bioconductor users with advanced functional gene-set analysis capabilities.

Conclusions:

• Seq2pathway offers a robust method for pathway analysis in modern genome research.
• The tool aids in identifying biological significance and disturbances from NGS data.
• It expands the scope of pathway analysis to include non-coding genomic elements.