Software Open Access
Snekmer is a software package designed to reduce the representation of protein sequences by combining amino acid reduction (AAR) with the kmer approach. Based on the AAR-kmer representations, Snekmer subsequently (1) clusters sequences using various unsupervised clustering algorithms, (2) generates supervised machine learning models, or (3) searches sequences against pre-trained models to determine probabilistic annotations.
There are three operation modes for Snekmer:
This research was supported in part by the U.S. Department of Energy (DOE), Office of Biological and Environmental Research (BER), as part of the Genomic Science Program (GSP) as a contribution of the Pacific Northwest National Laboratory (PNNL) Secure Biosystems Design Science Focus Area: Persistence Control of Engineered Functions in Complex Soil Microbiomes (PerCon SFA). Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory managed by the Battelle Memorial Institute Battelle Memorial Institute, operating under the U.S. Department of Energy, Contract DE-AC05-76RL01830.
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Christine H Chang, William C Nelson, Abby Jerger, Aaron T Wright, Robert G Egbert, Jason E McDermott, Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding, Bioinformatics Advances, Volume 3, Issue 1, 2023, vbad005. https://doi.org/10.1093/bioadv/vbad005