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Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering
- Publication Year :
- 2017
- Publisher :
- Cold Spring Harbor Laboratory, 2017.
-
Abstract
- 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.
- Subjects :
- chemistry.chemical_classification
Phylogenetic tree
biology
business.industry
Sequence analysis
Computer science
Pattern recognition
Peptide
ComputingMethodologies_PATTERNRECOGNITION
Protein sequencing
Recognition sequence
chemistry
Pattern recognition (psychology)
Glycosyltransferase
biology.protein
Artificial intelligence
Cluster analysis
business
Throughput (business)
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....05f36706647e08cb5bfc91d5ea82bea3