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ExUTR: a novel pipeline for large-scale prediction of 3′-UTR sequences from NGS data

Overview of attention for article published in BMC Genomics, November 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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Title
ExUTR: a novel pipeline for large-scale prediction of 3′-UTR sequences from NGS data
Published in
BMC Genomics, November 2017
DOI 10.1186/s12864-017-4241-1
Pubmed ID
Authors

Zixia Huang, Emma C. Teeling

Abstract

The three prime untranslated region (3'-UTR) is known to play a pivotal role in modulating gene expression by determining the fate of mRNA. Many crucial developmental events, such as mammalian spermatogenesis, tissue patterning, sex determination and neurogenesis, rely heavily on post-transcriptional regulation by the 3'-UTR. However, 3'-UTR biology seems to be a relatively untapped field, with only limited tools and 3'-UTR resources available. To elucidate the regulatory mechanisms of the 3'-UTR on gene expression, firstly the 3'-UTR sequences must be identified. Current 3'-UTR mining tools, such as GETUTR, 3USS and UTRscan, all depend on a well-annotated reference genome or curated 3'-UTR sequences, which hinders their application on a myriad of non-model organisms where the genomes are not available. To address these issues, the establishment of an NGS-based, automated pipeline is urgently needed for genome-wide 3'-UTR prediction in the absence of reference genomes. Here, we propose ExUTR, a novel NGS-based pipeline to predict and retrieve 3'-UTR sequences from RNA-Seq experiments, particularly designed for non-model species lacking well-annotated genomes. This pipeline integrates cutting-edge bioinformatics tools, databases (Uniprot and UTRdb) and novel in-house Perl scripts, implementing a fully automated workflow. By taking transcriptome assemblies as inputs, this pipeline identifies 3'-UTR signals based primarily on the intrinsic features of transcripts, and outputs predicted 3'-UTR candidates together with associated annotations. In addition, ExUTR only requires minimal computational resources, which facilitates its implementation on a standard desktop computer with reasonable runtime, making it affordable to use for most laboratories. We also demonstrate the functionality and extensibility of this pipeline using publically available RNA-Seq data from both model and non-model species, and further validate the accuracy of predicted 3'-UTR using both well-characterized 3'-UTR resources and 3P-Seq data. ExUTR is a practical and powerful workflow that enables rapid genome-wide 3'-UTR discovery from NGS data. The candidates predicted through this pipeline will further advance the study of miRNA target prediction, cis elements in 3'-UTR and the evolution and biology of 3'-UTRs. Being independent of a well-annotated reference genome will dramatically expand its application to much broader research area, encompassing all species for which RNA-Seq is available.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 90 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 23%
Student > Master 18 20%
Student > Ph. D. Student 13 14%
Student > Bachelor 8 9%
Professor > Associate Professor 5 6%
Other 11 12%
Unknown 14 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 39%
Agricultural and Biological Sciences 23 26%
Computer Science 5 6%
Immunology and Microbiology 3 3%
Engineering 2 2%
Other 5 6%
Unknown 17 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 01 December 2017.
All research outputs
#3,708,731
of 23,007,887 outputs
Outputs from BMC Genomics
#1,445
of 10,698 outputs
Outputs of similar age
#68,839
of 330,777 outputs
Outputs of similar age from BMC Genomics
#25
of 199 outputs
Altmetric has tracked 23,007,887 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,698 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 86% 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 330,777 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 79% of its contemporaries.
We're also able to compare this research output to 199 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.