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MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions

Overview of attention for article published in BMC Bioinformatics, November 2015
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
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Title
MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions
Published in
BMC Bioinformatics, November 2015
DOI 10.1186/s12859-015-0826-3
Pubmed ID
Authors

Mufleh Al-Shatnawi, M. Omair Ahmad, M. N. S. Swamy

Abstract

The alignment of multiple protein sequences is one of the most commonly performed tasks in bioinformatics. In spite of considerable research and efforts that have been recently deployed for improving the performance of multiple sequence alignment (MSA) algorithms, finding a highly accurate alignment between multiple protein sequences is still a challenging problem. We propose a novel and efficient algorithm called, MSAIndelFR, for multiple sequence alignment using the information on the predicted locations of IndelFRs and the computed average log-loss values obtained from IndelFR predictors, each of which is designed for a different protein fold. We demonstrate that the introduction of a new variable gap penalty function based on the predicted locations of the IndelFRs and the computed average log-loss values into the proposed algorithm substantially improves the protein alignment accuracy. This is illustrated by evaluating the performance of the algorithm in aligning sequences belonging to the protein folds for which the IndelFR predictors already exist and by using the reference alignments of the four popular benchmarks, BAliBASE 3.0, OXBENCH, PREFAB 4.0, and SABRE (SABmark 1.65). We have proposed a novel and efficient algorithm, the MSAIndelFR algorithm, for multiple protein sequence alignment incorporating a new variable gap penalty function. It is shown that the performance of the proposed algorithm is superior to that of the most-widely used alignment algorithms, Clustal W2, Clustal Omega, Kalign2, MSAProbs, MAFFT, MUSCLE, ProbCons and Probalign, in terms of both the sum-of-pairs and total column metrics.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 5%
Germany 1 5%
Switzerland 1 5%
Unknown 17 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 25%
Student > Ph. D. Student 5 25%
Student > Master 3 15%
Other 1 5%
Librarian 1 5%
Other 2 10%
Unknown 3 15%
Readers by discipline Count As %
Computer Science 6 30%
Agricultural and Biological Sciences 5 25%
Biochemistry, Genetics and Molecular Biology 4 20%
Engineering 2 10%
Social Sciences 1 5%
Other 0 0%
Unknown 2 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 11 December 2015.
All research outputs
#13,216,846
of 22,833,393 outputs
Outputs from BMC Bioinformatics
#4,006
of 7,288 outputs
Outputs of similar age
#181,111
of 386,225 outputs
Outputs of similar age from BMC Bioinformatics
#70
of 132 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,288 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 386,225 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.