DeepNOG: Fast and accurate protein orthologous group assignment.
Ontology highlight
ABSTRACT: Motivation
Protein orthologous group databases are powerful tools for evolutionary analysis, functional annotation, or metabolic pathway modeling across lineages. Sequences are typically assigned to orthologous groups with alignment-based methods, such as profile hidden Markov models, which has become a computational bottleneck.Results
We present DeepNOG, an extremely fast and accurate, alignment-free orthology assignment method based on deep convolutional networks. We compare DeepNOG against state-of-the-art alignment-based (HMMER, DIAMOND) and alignment-free methods (DeepFam) on two orthology databases (COG, eggNOG 5). DeepNOG can be scaled to large orthology databases like eggNOG, for which it outperforms DeepFam in terms of precision and recall by large margins. While alignment-based methods still provide the most accurate assignments among the investigated methods, computing time of DeepNOG is an order of magnitude lower on CPUs. Optional GPU usage further increases throughput massively. A command-line tool enables rapid adoption by users.Availability
Source code and packages are freely available at https://github.com/univieCUBE/deepnog. Install the platform-independent Python program with $pip install deepnog.Supplementary information
Supplementary material is available at Bioinformatics online.
SUBMITTER: Feldbauer R
PROVIDER: S-EPMC8016488 | biostudies-literature |
REPOSITORIES: biostudies-literature
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