Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering

Publication: ResearchInternet publication – Annual report year: 2017

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Large collections of protein sequences with divergent sequences are tedious to analyze for understanding their phylogenetic or structure-function relation. Peptide Pattern Recognition is an algorithm that was developed to facilitate this task but the previous version does only allow a limited number of sequences as input. I implemented Peptide Pattern Recognition as a multithread software designed to handle large numbers of sequences and perform analysis in a reasonable time frame. Benchmarking showed that the new implementation of Peptide Pattern Recognition is twenty times faster than the previous implementation on a small protein collection with 673 MAP kinase sequences. In addition, the new implementation could analyze a large protein collection with 48,570 Glycosyl Transferase family 20 sequences without reaching its upper limit on a desktop computer. Peptide Pattern Recognition is a useful software for providing comprehensive groups of related sequences from large protein sequence collections.
Original languageEnglish
Publication date2017
TypeUnpublished preprint
Source/PublisherbioRxiv Cold Spring Harbor Laboratory
DOIs
StatePublished - 2017

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The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.

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