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| Item Type: | Article |
|---|---|
| Title: | Benchmarking the next generation of homology inference tools |
| Creators Name: | Saripella, G.V., Sonnhammer, E.L.L. and Forslund, K. |
| Abstract: | Motivation: Over the last decades, vast numbers of sequences were deposited in public databases. Bioinformatics tools allow homology and consequently functional inference for these sequences. New profile-based homology search tools have been introduced, allowing reliable detection of remote homologs, but have not been systematically benchmarked. To provide such a comparison, which can guide bioinformatics workflows, we extend and apply our previously developed benchmark approach to evaluate the 'next generation' of profile-based approaches, including CS-BLAST, HHSEARCH and PHMMER, in comparison with the non-profile based search tools NCBI-BLAST, USEARCH, UBLAST and FASTA. Method: We generated challenging benchmark datasets based on protein domain architectures within either the PFAM + Clan, SCOP/Superfamily or CATH/Gene3D domain definition schemes. From each dataset, homologous and non-homologous protein pairs were aligned using each tool, and standard performance metrics calculated. We further measured congruence of domain architecture assignments in the three domain databases. Results: CSBLAST and PHMMER had overall highest accuracy. FASTA, UBLAST and USEARCH showed large trade-offs of accuracy for speed optimization. Conclusion: Profile methods are superior at inferring remote homologs but the difference in accuracy between methods is relatively small. PHMMER and CSBLAST stand out with the highest accuracy, yet still at a reasonable computational cost. Additionally, we show that less than 0.1% of Swiss-Prot protein pairs considered homologous by one database are considered non-homologous by another, implying that these classifications represent equivalent underlying biological phenomena, differing mostly in coverage and granularity. Availability and Implementation: Benchmark datasets and all scripts are placed at (http://sonnhammer.org/download/Homology_benchmark). Contact: forslund@embl.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. |
| Keywords: | Amino Acid Sequence Homology, Benchmarking, High-Throughput Nucleotide Sequencing, Protein Databases, Proteins, Sequence Homology |
| Source: | Bioinformatics |
| ISSN: | 1367-4803 |
| Volume: | 32 |
| Number: | 17 |
| Page Range: | 2636-2641 |
| Date: | 1 September 2016 |
| Official Publication: | https://doi.org/10.1093/bioinformatics/btw305 |
| PubMed: | View item in PubMed |
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