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A non-local gap-penalty for profile alignment

Overview of attention for article published in Bulletin of Mathematical Biology, January 1996
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Title
A non-local gap-penalty for profile alignment
Published in
Bulletin of Mathematical Biology, January 1996
DOI 10.1007/bf02458279
Pubmed ID
Authors

William R. Taylor

Abstract

The length of an alignment of biological sequences is typically longer than the mean length of its component sequences. (This arises from the insertion of gaps in the alignment.) When such an alignment is used as a profile for the alignment of further sequences (or profiles), it will have a bias toward additional sequences that match the length of the profile, rather than the mean length of sequences in the profile, as the alignment of these will entail fewer (or smaller) insertions (so avoiding gap-penalties). An algorithm is described to correct this bias that entails monitoring the correspondence, for every pair of positions, of the mean separations in both profiles as they are aligned. The correction was incorporated into a standard dynamic programming algorithm through a modification of the gap-penalty, but, unlike other approaches, this modification is not local and takes into consideration the overall alignment of the sequences. This implies that the algorithm cannot guarantee to find the optimal alignment, but tests suggest that close approximations are obtained. The method was tested on protein families by measuring the area in the parameter space of the phase containing the correct multiple alignment. No improvement (increase in phase area) was found with a family that required few gaps to be aligned correctly. However, for highly gapped alignments, a 50% increase in area was obtained with one family and the correct alignment was found for another that could not be aligned with the unbiased method.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 20%
Student > Bachelor 1 20%
Researcher 1 20%
Student > Doctoral Student 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 1 20%
Agricultural and Biological Sciences 1 20%
Chemistry 1 20%
Engineering 1 20%
Unknown 1 20%
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 10 November 2011.
All research outputs
#7,453,126
of 22,785,242 outputs
Outputs from Bulletin of Mathematical Biology
#299
of 1,094 outputs
Outputs of similar age
#16,759
of 79,172 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#1
of 2 outputs
Altmetric has tracked 22,785,242 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,094 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 59% of its peers.
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