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InParanoid-DIAMOND: faster orthology analysis with the InParanoid algorithm
Emma Persson1, Erik L L Sonnhammer1
1Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, 17121 Solna, Sweden.
Bioinformatics (Oxford, England)
|May 13, 2022
Summary
This study accelerates ortholog prediction by replacing BLAST with DIAMOND in the InParanoid algorithm. This update significantly reduces runtime by 94% without compromising prediction accuracy.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Ortholog prediction is crucial for comparative genomics and understanding gene function across species.
- Existing tools like InParanoid are accurate but computationally intensive, limiting large-scale analyses.
- The need for faster orthology prediction methods is critical for handling burgeoning genomic datasets.
Purpose of the Study:
- To enhance the InParanoid algorithm for faster ortholog prediction.
- To integrate the DIAMOND tool for accelerated homolog searching within InParanoid.
- To evaluate the performance and speed improvements of the updated InParanoid algorithm.
Main Methods:
- Modified the InParanoid algorithm to utilize DIAMOND for the initial homolog search.
- Compared the runtime and accuracy of the updated InParanoid against the original version using BLAST.
- Assessed performance using the Quest for Orthologs benchmark dataset.
Main Results:
- The updated InParanoid algorithm with DIAMOND achieved a 94% reduction in runtime.
- The accuracy of ortholog predictions remained comparable to the original InParanoid algorithm.
- The Quest for Orthologs benchmark confirmed similar performance metrics.
Conclusions:
- Replacing BLAST with DIAMOND in InParanoid offers a substantial speed improvement for ortholog prediction.
- The enhanced InParanoid remains a reliable tool for accurate orthology analysis on large datasets.
- This optimization makes large-scale comparative genomic studies more feasible and efficient.
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