Rnalib: a Python library for custom transcriptomics analyses

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput sequencing data analysis requires robust computational pipelines.
• Existing Python libraries lack specialized tools for transcriptomics analysis.
• Key transcriptomics tasks include associating gene features with genomic data and efficient querying.

Purpose of the Study:

• To develop a specialized Python library for transcriptomics data analysis.
• To provide efficient methods for accessing and annotating large-scale genomic datasets.
• To create a framework for building reproducible and robust bioinformatics tools.

Main Methods:

• Developed rnalib, a Python library built on pysam and pyBigWig.
• Implemented an object-oriented Transcriptome class for stepwise annotation.
• Designed an API separating genomic locations from associated data for efficient querying and export.

Main Results:

• rnalib offers random access support for efficient subregion access in large datasets.
• The library extends filtering and access capabilities, including error-checking for data integration.
• Provides methods for iterating, querying, and exporting collated transcriptomics datasets.

Conclusions:

• rnalib establishes a fast, readable, reproducible, and robust framework for novel transcriptomics analysis.
• The library addresses the need for specialized tools in transcriptomics data analysis.
• Facilitates the development of custom bioinformatics methods for transcriptomics research.