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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
Published on: February 18, 2022
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OrfM: a fast open reading frame predictor for metagenomic data
Ben J Woodcroft1, Joel A Boyd1, Gene W Tyson1
1Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia.
Bioinformatics (Oxford, England)
|May 7, 2016
Summary
OrfM is a new bioinformatics tool that rapidly identifies open reading frames (ORFs) in DNA sequences. It is four times faster than existing methods, addressing a key bottleneck in genomic data analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Identifying open reading frames (ORFs) is crucial for DNA sequence analysis.
- Current computational tools for ORF finding are slow, creating a bottleneck with increasing sequence data volumes.
- This bottleneck is particularly significant in metagenomics for analyzing unassembled reads and assembled contigs.
Purpose of the Study:
- To develop a faster computational tool for identifying open reading frames (ORFs).
- To address the performance limitations of existing ORF-finding software.
- To provide a solution for efficient gene discovery in large-scale sequencing datasets.
Main Methods:
- Developed OrfM, a novel tool for rapid ORF identification.
- Employed the Aho-Corasick algorithm to efficiently locate regions without stop codons.
- Benchmarked OrfM against established tools like GetOrf and Translate.
Main Results:
- OrfM identifies identical ORFs compared to existing tools.
- OrfM demonstrates a 4-5 times speed improvement over similar ORF-finding software.
- The tool is sequencing platform-agnostic, excelling with large, high-quality datasets (e.g., Illumina).
Conclusions:
- OrfM significantly accelerates the identification of open reading frames (ORFs).
- The tool effectively overcomes computational bottlenecks in sequence data analysis.
- OrfM is a valuable asset for genomic and metagenomic research, especially with high-throughput sequencing data.
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