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PnpProbs: a better multiple sequence alignment tool by better handling of guide trees

Overview of attention for article published in BMC Bioinformatics, August 2016
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2 tweeters

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Title
PnpProbs: a better multiple sequence alignment tool by better handling of guide trees
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1121-7
Pubmed ID
Authors

Yongtao Ye, Tak-Wah Lam, Hing-Fung Ting

Abstract

This paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment; it first estimates the input's discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment. To evaluate PnpProbs, we have compared it with thirteen other popular MSA tools, and PnpProbs has the best alignment scores in all but one test. We have also used it for phylogenetic analysis, and found that the phylogenetic trees constructed from PnpProbs' alignments are closest to the model trees. By combining the strength of the progressive and non-progressive alignment methods, we have developed an MSA tool called PnpProbs. We have compared PnpProbs with thirteen other popular MSA tools and our results showed that our tool usually constructed the best alignments.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 33%
Student > Bachelor 4 27%
Professor 2 13%
Student > Ph. D. Student 2 13%
Other 1 7%
Other 0 0%
Unknown 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 33%
Biochemistry, Genetics and Molecular Biology 4 27%
Computer Science 3 20%
Chemistry 1 7%
Medicine and Dentistry 1 7%
Other 0 0%
Unknown 1 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 September 2016.
All research outputs
#6,313,699
of 8,320,311 outputs
Outputs from BMC Bioinformatics
#3,026
of 3,631 outputs
Outputs of similar age
#178,463
of 251,996 outputs
Outputs of similar age from BMC Bioinformatics
#106
of 134 outputs
Altmetric has tracked 8,320,311 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,631 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
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We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.