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DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment

Overview of attention for article published in BMC Bioinformatics, October 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

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16 tweeters
wikipedia
2 Wikipedia pages

Citations

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140 Dimensions

Readers on

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145 Mendeley
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Title
DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0749-z
Pubmed ID
Authors

Erik S. Wright

Abstract

Alignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments. Two predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets. Predicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.

Twitter Demographics

The data shown below were collected from the profiles of 16 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
France 1 <1%
Korea, Republic of 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 139 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 32%
Researcher 27 19%
Student > Doctoral Student 13 9%
Student > Master 11 8%
Student > Bachelor 9 6%
Other 20 14%
Unknown 19 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 36%
Biochemistry, Genetics and Molecular Biology 29 20%
Environmental Science 10 7%
Immunology and Microbiology 7 5%
Computer Science 7 5%
Other 17 12%
Unknown 23 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 24 May 2021.
All research outputs
#2,039,636
of 18,017,546 outputs
Outputs from BMC Bioinformatics
#707
of 6,336 outputs
Outputs of similar age
#34,843
of 260,423 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 18,017,546 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 260,423 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them