orfipy: a fast and flexible tool for extracting ORFs

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Identifying open reading frames (ORFs) is essential for annotating protein-coding regions in genomic and transcriptomic data.
• The increasing volume of sequencing data necessitates faster and more versatile ORF-finding tools.
• Existing tools may lack the flexibility required for fine-tuning search parameters and integrating with downstream analyses.

Purpose of the Study:

• To introduce orfipy, a novel Python-based tool for efficient and flexible open reading frame searching.
• To provide a customizable solution for analyzing large-scale genomic and transcriptomic datasets.
• To facilitate downstream bioinformatics analyses by offering versatile output formats.

Main Methods:

• Development of a Python-based software tool named orfipy.
• Implementation of flexible search criteria for identifying open reading frames.
• Inclusion of customizable output formats, including FASTA and BED.

Main Results:

• orfipy enables rapid searching of open reading frames in large datasets.
• The tool is fully customizable, allowing users to define specific search parameters.
• orfipy supports both FASTA and BED output formats for seamless integration with other tools.

Conclusions:

• orfipy offers a fast, flexible, and customizable solution for open reading frame identification.
• The tool addresses the need for efficient analysis of large genomic and transcriptomic datasets.
• orfipy enhances downstream analysis workflows in genomics and transcriptomics.