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CombAlign: a code for generating a one-to-many sequence alignment from a set of pairwise structure-based sequence alignments

Overview of attention for article published in Source Code for Biology and Medicine, August 2015
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Title
CombAlign: a code for generating a one-to-many sequence alignment from a set of pairwise structure-based sequence alignments
Published in
Source Code for Biology and Medicine, August 2015
DOI 10.1186/s13029-015-0039-1
Pubmed ID
Authors

Carol L. Ecale Zhou

Abstract

In order to better define regions of similarity among related protein structures, it is useful to identify the residue-residue correspondences among proteins. Few codes exist for constructing a one-to-many multiple sequence alignment derived from a set of structure or sequence alignments, and a need was evident for creating such a tool for combining pairwise structure alignments that would allow for insertion of gaps in the reference structure. This report describes a new Python code, CombAlign, which takes as input a set of pairwise sequence alignments (which may be structure based) and generates a one-to-many, gapped, multiple structure- or sequence-based sequence alignment (MSSA). The use and utility of CombAlign was demonstrated by generating gapped MSSAs using sets of pairwise structure-based sequence alignments between structure models of the matrix protein (VP40) and pre-small/secreted glycoprotein (sGP) of Reston Ebolavirus and the corresponding proteins of several other filoviruses. The gapped MSSAs revealed structure-based residue-residue correspondences, which enabled identification of structurally similar versus differing regions in the Reston proteins compared to each of the other corresponding proteins. CombAlign is a new Python code that generates a one-to-many, gapped, multiple structure- or sequence-based sequence alignment (MSSA) given a set of pairwise sequence alignments (which may be structure based). CombAlign has utility in assisting the user in distinguishing structurally conserved versus divergent regions on a reference protein structure relative to other closely related proteins. CombAlign was developed in Python 2.6, and the source code is available for download from the GitHub code repository.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Other 4 24%
Researcher 4 24%
Librarian 3 18%
Student > Bachelor 1 6%
Lecturer 1 6%
Other 2 12%
Unknown 2 12%
Readers by discipline Count As %
Nursing and Health Professions 3 18%
Agricultural and Biological Sciences 3 18%
Computer Science 2 12%
Medicine and Dentistry 2 12%
Veterinary Science and Veterinary Medicine 2 12%
Other 3 18%
Unknown 2 12%
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 18 November 2015.
All research outputs
#14,717,488
of 23,577,761 outputs
Outputs from Source Code for Biology and Medicine
#76
of 127 outputs
Outputs of similar age
#138,150
of 265,611 outputs
Outputs of similar age from Source Code for Biology and Medicine
#1
of 1 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.
So far Altmetric has tracked 127 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 37th percentile – i.e., 37% 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 265,611 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
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