Evidence map›Paper›PMID 25406361›Full record

ArticleRNA (New York, N.Y.)2015

IsoSCM: improved and alternative 3' UTR annotation using multiple change-point inference.

Sol Shenker, Pedro Miura, Piero Sanfilippo, Eric C Lai

Open access · bronzeAbstract read
In one paragraph

Article in RNA (New York, N.Y.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
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

41 citing papers in PubMed, 68 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
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

4 authors at 2 institutions in 1 country.

Sol ShenkerDepartment of Developmental Biology, Sloan-Kettering Institute, New York, New York 10065, USA Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, New York 10065, USA.
Pedro MiuraDepartment of Developmental Biology, Sloan-Kettering Institute, New York, New York 10065, USA.
Piero SanfilippoDepartment of Developmental Biology, Sloan-Kettering Institute, New York, New York 10065, USA Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, New York 10065, USA.
Eric C LaiDepartment of Developmental Biology, Sloan-Kettering Institute, New York, New York 10065, USA laie@mskcc.org.
Cornell University · USKettering University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Mechanism and regulation of Hu family RNA binding proteins during neural alternative polyadenylationR01NS083833 · NINDS · SLOAN-KETTERING INST CAN RESEARCH · PI LAI, ERIC C · 2014 to 2023
$5.4M
BEN factors are conserved CSL co-repressors in Notch-mediated neural developmentR01NS074037 · NINDS · SLOAN-KETTERING INST CAN RESEARCH · PI LAI, ERIC C · 2011 to 2015
$2.1M
Canadian Institutes of Health ResearchNCI NIH HHS P30 CA008748NINDS NIH HHS R01 NS074037NINDS NIH HHS R01-NS074037NINDS NIH HHS R01 NS083833NINDS NIH HHS R01-NS083833
6 · The paper itself

Abstract

Major applications of RNA-seq data include studies of how the transcriptome is modulated at the levels of gene expression and RNA processing, and how these events are related to cellular identity, environmental condition, and/or disease status. While many excellent tools have been developed to analyze RNA-seq data, these generally have limited efficacy for annotating 3' UTRs. Existing assembly strategies often fragment long 3' UTRs, and importantly, none of the algorithms in popular use can apportion data into tandem 3' UTR isoforms, which are frequently generated by alternative cleavage and polyadenylation (APA). Consequently, it is often not possible to identify patterns of differential APA using existing assembly tools. To address these limitations, we present a new method for transcript assembly, Isoform Structural Change Model (IsoSCM) that incorporates change-point analysis to improve the 3' UTR annotation process. Through evaluation on simulated and genuine data sets, we demonstrate that IsoSCM annotates 3' termini with higher sensitivity and specificity than can be achieved with existing methods. We highlight the utility of IsoSCM by demonstrating its ability to recover known patterns of tissue-regulated APA. IsoSCM will facilitate future efforts for 3' UTR annotation and genome-wide studies of the breadth, regulation, and roles of APA leveraging RNA-seq data. The IsoSCM software and source code are available from our website https://github.com/shenkers/isoscm.

Indexed as

3' Untranslated RegionsMolecular Sequence AnnotationAnimalsComputational BiologyComputer SimulationGene Expression ProfilingHumansReproducibility of ResultsSequence Analysis, RNASoftware3' Untranslated Regions3′ UTRAPAIsoSCMRNA-seq

Identifiers

PMID25406361
PMCPMC4274634
OpenAlexW2133019267

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.