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• W2022653138 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." @default.
• W2022653138 created "2016-06-24" @default.
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• W2022653138 date "2014-03-01" @default.
• W2022653138 modified "2023-09-27" @default.
• W2022653138 title "Optimizing Spaced <formula formulatype=inline><tex Notation=TeX>$k$</tex></formula>-mer Neighbors for Efficient Filtration in Protein Similarity Search" @default.
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• W2022653138 doi "https://doi.org/10.1109/tcbb.2014.2306831" @default.
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