Evidence map›Paper›PMID 29110697›Full record

ArticleBMC genomics2017

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

Zixia Huang, Emma C Teeling

Abstract read
In one paragraph

Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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  14. Computational Detection of MicroRNA Targets.Methods in molecular biology (Clifton, N.J.) · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Zixia HuangUCD School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
Emma C TeelingUCD School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland. emma.teeling@ucd.ie.ORCID http://orcid.org/0000-0002-3309-1346

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

resultsHere, 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.

conclusionsExUTR 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.

Indexed as

High-Throughput Nucleotide SequencingSequence Analysis, RNA3' Untranslated RegionsGenomics3' Untranslated Regions3′-UTR predictionIndependent of genomesNext generation sequencing

Identifiers

PMID29110697
PMCPMC5674806

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.