Optimizing Spaced k-mer Neighbors for Efficient Filtration in Protein Similarity Search.
Identifieur interne : 001264 ( Ncbi/Merge ); précédent : 001263; suivant : 001265Optimizing Spaced k-mer Neighbors for Efficient Filtration in Protein Similarity Search.
Auteurs : Weiming Li ; Bin Ma ; Kaizhong ZhangSource :
- IEEE/ACM transactions on computational biology and bioinformatics [ 1557-9964 ]
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Proteins.
- chemical , genetics : Proteins.
- methods : Computational Biology, Sequence Analysis, Protein.
- Algorithms, Animals, Drosophila, Humans, Mice, Sequence Homology, Amino Acid, Software.
Abstract
Large-scale comparison or similarity search of genomic DNA and protein sequence is of fundamental importance in modern molecular biology. To perform DNA and protein sequence similarity search efficiently, seeding (or filtration) method has been widely used where only sequences sharing a common pattern or "seed" are subject to detailed comparison. Therefore these methods trade search sensitivity with search speed. In this paper, we introduce a new seeding method, called spaced k-mer neighbors, which provides a better tradeoff between the sensitivity and speed in protein sequence similarity search. With the method of spaced k-mer neighbors, for each spaced k-mer, a set of spaced k-mers is selected as its neighbors. These pre-selected spaced k-mer neighbors are then used to detect hits between query sequence and database sequences. We propose an efficient heuristic algorithm for the spaced neighbor selection. Our computational experimental results demonstrate that the method of spaced k-mer neighbors can improve the overall tradeoff efficiency over existing seeding methods.
DOI: 10.1109/TCBB.2014.2306831
PubMed: 26355786
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To perform DNA and protein sequence similarity search efficiently, seeding (or filtration) method has been widely used where only sequences sharing a common pattern or "seed" are subject to detailed comparison. Therefore these methods trade search sensitivity with search speed. In this paper, we introduce a new seeding method, called spaced k-mer neighbors, which provides a better tradeoff between the sensitivity and speed in protein sequence similarity search. With the method of spaced k-mer neighbors, for each spaced k-mer, a set of spaced k-mers is selected as its neighbors. These pre-selected spaced k-mer neighbors are then used to detect hits between query sequence and database sequences. We propose an efficient heuristic algorithm for the spaced neighbor selection. Our computational experimental results demonstrate that the method of spaced k-mer neighbors can improve the overall tradeoff efficiency over existing seeding methods. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26355786</PMID><DateCompleted><Year>2016</Year><Month>03</Month><Day>14</Day></DateCompleted><DateRevised><Year>2016</Year><Month>10</Month><Day>20</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1557-9964</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><Issue>2</Issue><PubDate><MedlineDate>2014 Mar-Apr</MedlineDate></PubDate></JournalIssue><Title>IEEE/ACM transactions on computational biology and bioinformatics</Title><ISOAbbreviation>IEEE/ACM Trans Comput Biol Bioinform</ISOAbbreviation></Journal><ArticleTitle>Optimizing Spaced k-mer Neighbors for Efficient Filtration in Protein Similarity Search.</ArticleTitle><Pagination><MedlinePgn>398-406</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1109/TCBB.2014.2306831</ELocationID><Abstract><AbstractText>Large-scale comparison or similarity search of genomic DNA and protein sequence is of fundamental importance in modern molecular biology. 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