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Integrated pipeline for inferring the evolutionary history of a gene family embedded in the species tree: a case study on the STIMATE gene family

Overview of attention for article published in BMC Bioinformatics, October 2017
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Title
Integrated pipeline for inferring the evolutionary history of a gene family embedded in the species tree: a case study on the STIMATE gene family
Published in
BMC Bioinformatics, October 2017
DOI 10.1186/s12859-017-1850-2
Pubmed ID
Authors

Jia Song, Sisi Zheng, Nhung Nguyen, Youjun Wang, Yubin Zhou, Kui Lin

Abstract

Because phylogenetic inference is an important basis for answering many evolutionary problems, a large number of algorithms have been developed. Some of these algorithms have been improved by integrating gene evolution models with the expectation of accommodating the hierarchy of evolutionary processes. To the best of our knowledge, however, there still is no single unifying model or algorithm that can take all evolutionary processes into account through a stepwise or simultaneous method. On the basis of three existing phylogenetic inference algorithms, we built an integrated pipeline for inferring the evolutionary history of a given gene family; this pipeline can model gene sequence evolution, gene duplication-loss, gene transfer and multispecies coalescent processes. As a case study, we applied this pipeline to the STIMATE (TMEM110) gene family, which has recently been reported to play an important role in store-operated Ca(2+) entry (SOCE) mediated by ORAI and STIM proteins. We inferred their phylogenetic trees in 69 sequenced chordate genomes. By integrating three tree reconstruction algorithms with diverse evolutionary models, a pipeline for inferring the evolutionary history of a gene family was developed, and its application was demonstrated.

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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 %
Student > Master 3 18%
Researcher 3 18%
Student > Ph. D. Student 3 18%
Student > Bachelor 2 12%
Student > Doctoral Student 1 6%
Other 2 12%
Unknown 3 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 47%
Biochemistry, Genetics and Molecular Biology 4 24%
Environmental Science 1 6%
Immunology and Microbiology 1 6%
Unknown 3 18%
Attention Score in Context

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 October 2017.
All research outputs
#20,449,496
of 23,005,189 outputs
Outputs from BMC Bioinformatics
#6,887
of 7,312 outputs
Outputs of similar age
#281,823
of 323,064 outputs
Outputs of similar age from BMC Bioinformatics
#94
of 105 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,312 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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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