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TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets

Overview of attention for article published in BMC Genomics, April 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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16 tweeters

Citations

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11 Dimensions

Readers on

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38 Mendeley
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Title
TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets
Published in
BMC Genomics, April 2018
DOI 10.1186/s12864-018-4630-0
Pubmed ID
Authors

Louis T. Dang, Markus Tondl, Man Ho H. Chiu, Jerico Revote, Benedict Paten, Vincent Tano, Alex Tokolyi, Florence Besse, Greg Quaife-Ryan, Helen Cumming, Mark J. Drvodelic, Michael P. Eichenlaub, Jeannette C. Hallab, Julian S. Stolper, Fernando J. Rossello, Marie A. Bogoyevitch, David A. Jans, Hieu T. Nim, Enzo R. Porrello, James E. Hudson, Mirana Ramialison

Abstract

A strong focus of the post-genomic era is mining of the non-coding regulatory genome in order to unravel the function of regulatory elements that coordinate gene expression (Nat 489:57-74, 2012; Nat 507:462-70, 2014; Nat 507:455-61, 2014; Nat 518:317-30, 2015). Whole-genome approaches based on next-generation sequencing (NGS) have provided insight into the genomic location of regulatory elements throughout different cell types, organs and organisms. These technologies are now widespread and commonly used in laboratories from various fields of research. This highlights the need for fast and user-friendly software tools dedicated to extracting cis-regulatory information contained in these regulatory regions; for instance transcription factor binding site (TFBS) composition. Ideally, such tools should not require prior programming knowledge to ensure they are accessible for all users. We present TrawlerWeb, a web-based version of the Trawler_standalone tool (Nat Methods 4:563-5, 2007; Nat Protoc 5:323-34, 2010), to allow for the identification of enriched motifs in DNA sequences obtained from next-generation sequencing experiments in order to predict their TFBS composition. TrawlerWeb is designed for online queries with standard options common to web-based motif discovery tools. In addition, TrawlerWeb provides three unique new features: 1) TrawlerWeb allows the input of BED files directly generated from NGS experiments, 2) it automatically generates an input-matched biologically relevant background, and 3) it displays resulting conservation scores for each instance of the motif found in the input sequences, which assists the researcher in prioritising the motifs to validate experimentally. Finally, to date, this web-based version of Trawler_standalone remains the fastest online de novo motif discovery tool compared to other popular web-based software, while generating predictions with high accuracy. TrawlerWeb provides users with a fast, simple and easy-to-use web interface for de novo motif discovery. This will assist in rapidly analysing NGS datasets that are now being routinely generated. TrawlerWeb is freely available and accessible at: http://trawler.erc.monash.edu.au .

Twitter Demographics

The data shown below were collected from the profiles of 16 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 24%
Student > Master 6 16%
Researcher 4 11%
Student > Bachelor 3 8%
Professor 2 5%
Other 6 16%
Unknown 8 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 29%
Biochemistry, Genetics and Molecular Biology 8 21%
Immunology and Microbiology 2 5%
Medicine and Dentistry 2 5%
Social Sciences 2 5%
Other 3 8%
Unknown 10 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 13 January 2021.
All research outputs
#2,390,985
of 18,209,496 outputs
Outputs from BMC Genomics
#988
of 9,547 outputs
Outputs of similar age
#57,055
of 289,817 outputs
Outputs of similar age from BMC Genomics
#3
of 22 outputs
Altmetric has tracked 18,209,496 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,547 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 89% of its peers.
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 289,817 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.