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Simple adjustment of the sequence weight algorithm remarkably enhances PSI-BLAST performance

Overview of attention for article published in BMC Bioinformatics, June 2017
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Title
Simple adjustment of the sequence weight algorithm remarkably enhances PSI-BLAST performance
Published in
BMC Bioinformatics, June 2017
DOI 10.1186/s12859-017-1686-9
Pubmed ID
Authors

Toshiyuki Oda, Kyungtaek Lim, Kentaro Tomii

Abstract

PSI-BLAST, an extremely popular tool for sequence similarity search, features the utilization of Position-Specific Scoring Matrix (PSSM) constructed from a multiple sequence alignment (MSA). PSSM allows the detection of more distant homologs than a general amino acid substitution matrix does. An accurate estimation of the weights for sequences in an MSA is crucially important for PSSM construction. PSI-BLAST divides a given MSA into multiple blocks, for which sequence weights are calculated. When the block width becomes very narrow, the sequence weight calculation can be odd. We demonstrate that PSI-BLAST indeed generates a significant fraction of blocks having width less than 5, thereby degrading the PSI-BLAST performance. We revised the code of PSI-BLAST to prevent the blocks from being narrower than a given minimum block width (MBW). We designate the modified application of PSI-BLAST as PSI-BLASTexB. When MBW is 25, PSI-BLASTexB notably outperforms PSI-BLAST consistently for three independent benchmark sets. The performance boost is even more drastic when an MSA, instead of a sequence, is used as a query. Our results demonstrate that the generation of narrow-width blocks during the sequence weight calculation is a critically important factor that restricts the PSI-BLAST search performance. By preventing narrow blocks, PSI-BLASTexB upgrades the PSI-BLAST performance remarkably. Binaries and source codes of PSI-BLASTexB (MBW = 25) are available at https://github.com/kyungtaekLIM/PSI-BLASTexB .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 27%
Researcher 9 20%
Student > Ph. D. Student 6 13%
Student > Master 3 7%
Lecturer 2 4%
Other 4 9%
Unknown 9 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 29%
Agricultural and Biological Sciences 7 16%
Computer Science 4 9%
Immunology and Microbiology 2 4%
Engineering 2 4%
Other 2 4%
Unknown 15 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 November 2017.
All research outputs
#14,717,488
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#4,823
of 7,418 outputs
Outputs of similar age
#178,949
of 318,607 outputs
Outputs of similar age from BMC Bioinformatics
#66
of 112 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
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