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Logo2PWM: a tool to convert sequence logo to position weight matrix

Overview of attention for article published in BMC Genomics, October 2017
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Title
Logo2PWM: a tool to convert sequence logo to position weight matrix
Published in
BMC Genomics, October 2017
DOI 10.1186/s12864-017-4023-9
Pubmed ID
Authors

Zhen Gao, Lu Liu, Jianhua Ruan

Abstract

position weight matrix (PWM) and sequence logo are the most widely used representations of transcription factor binding site (TFBS) in biological sequences. Sequence logo - a graphical representation of PWM, has been widely used in scientific publications and reports, due to its easiness of human perception, rich information, and simple format. Different from sequence logo, PWM works great as a precise and compact digitalized form, which can be easily used by a variety of motif analysis software. There are a few available tools to generate sequence logos from PWM; however, no tool does the reverse. Such tool to convert sequence logo back to PWM is needed to scan a TFBS represented in logo format in a publication where the PWM is not provided or hard to be acquired. A major difficulty in developing such tool to convert sequence logo to PWM is to deal with the diversity of sequence logo images. We propose logo2PWM for reconstructing PWM from a large variety of sequence logo images. Evaluation results on over one thousand logos from three sources of different logo format show that the correlation between the reconstructed PWMs and the original PWMs are constantly high, where median correlation is greater than 0.97. Because of the high recognition accuracy, the easiness of usage, and, the availability of both web-based service and stand-alone application, we believe that logo2PWM can readily benefit the study of transcription by filling the gap between sequence logo and PWM.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 37%
Researcher 5 9%
Student > Ph. D. Student 5 9%
Student > Bachelor 4 7%
Student > Doctoral Student 2 4%
Other 8 15%
Unknown 10 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 31%
Computer Science 8 15%
Agricultural and Biological Sciences 7 13%
Medicine and Dentistry 3 6%
Immunology and Microbiology 2 4%
Other 7 13%
Unknown 10 19%
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 05 October 2017.
All research outputs
#18,573,839
of 23,005,189 outputs
Outputs from BMC Genomics
#8,225
of 10,692 outputs
Outputs of similar age
#247,363
of 323,064 outputs
Outputs of similar age from BMC Genomics
#155
of 203 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,692 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 203 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.