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An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes

Overview of attention for article published in BMC Genomics, August 2016
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Title
An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2982-x
Pubmed ID
Authors

Bingqiang Liu, Hanyuan Zhang, Chuan Zhou, Guojun Li, Anne Fennell, Guanghui Wang, Yu Kang, Qi Liu, Qin Ma

Abstract

Phylogenetic footprinting is an important computational technique for identifying cis-regulatory motifs in orthologous regulatory regions from multiple genomes, as motifs tend to evolve slower than their surrounding non-functional sequences. Its application, however, has several difficulties for optimizing the selection of orthologous data and reducing the false positives in motif prediction. Here we present an integrative phylogenetic footprinting framework for accurate motif predictions in prokaryotic genomes (MP(3)). The framework includes a new orthologous data preparation procedure, an additional promoter scoring and pruning method and an integration of six existing motif finding algorithms as basic motif search engines. Specifically, we collected orthologous genes from available prokaryotic genomes and built the orthologous regulatory regions based on sequence similarity of promoter regions. This procedure made full use of the large-scale genomic data and taxonomy information and filtered out the promoters with limited contribution to produce a high quality orthologous promoter set. The promoter scoring and pruning is implemented through motif voting by a set of complementary predicting tools that mine as many motif candidates as possible and simultaneously eliminate the effect of random noise. We have applied the framework to Escherichia coli k12 genome and evaluated the prediction performance through comparison with seven existing programs. This evaluation was systematically carried out at the nucleotide and binding site level, and the results showed that MP(3) consistently outperformed other popular motif finding tools. We have integrated MP(3) into our motif identification and analysis server DMINDA, allowing users to efficiently identify and analyze motifs in 2,072 completely sequenced prokaryotic genomes. The performance evaluation indicated that MP(3) is effective for predicting regulatory motifs in prokaryotic genomes. Its application may enhance progress in elucidating transcription regulation mechanism, thus provide benefit to the genomic research community and prokaryotic genome researchers in particular.

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Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 27%
Student > Ph. D. Student 8 18%
Unspecified 5 11%
Researcher 5 11%
Student > Bachelor 5 11%
Other 3 7%
Unknown 6 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 30%
Agricultural and Biological Sciences 6 14%
Unspecified 5 11%
Computer Science 5 11%
Medicine and Dentistry 2 5%
Other 3 7%
Unknown 10 23%

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 11 August 2016.
All research outputs
#4,353,292
of 8,200,733 outputs
Outputs from BMC Genomics
#3,707
of 5,798 outputs
Outputs of similar age
#141,197
of 257,425 outputs
Outputs of similar age from BMC Genomics
#176
of 264 outputs
Altmetric has tracked 8,200,733 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,798 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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