Evidence map›Paper›PMID 22293517›Full record

ArticleGenome biology2012

SpliceGrapher: detecting patterns of alternative splicing from RNA-Seq data in the context of gene models and EST data.

Mark F Rogers, Julie Thomas, Anireddy Sn Reddy, Asa Ben-Hur

Abstract read
In one paragraph

Article in Genome biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.

0numbers the graph read from it
0cells of the map it votes in
75citing 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

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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

75 citing papers in PubMed.

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  14. Global Survey of the Full-Length Cabbage Transcriptome (International journal of molecular sciences · 2021
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15 more citing papers are in PubMed but not listed here.

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.

Mark F RogersDepartment of Computer Science, 1873 Campus Delivery, Colorado State University, Fort Collins, CO 80523-1873, USA.
Julie Thomas
Anireddy Sn Reddy
Asa Ben-Hur

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a method for predicting splice graphs that enhances curated gene models using evidence from RNA-Seq and EST alignments. Results obtained using RNA-Seq experiments in Arabidopsis thaliana show that predictions made by our SpliceGrapher method are more consistent with current gene models than predictions made by TAU and Cufflinks. Furthermore, analysis of plant and human data indicates that the machine learning approach used by SpliceGrapher is useful for discriminating between real and spurious splice sites, and can improve the reliability of detection of alternative splicing. SpliceGrapher is available for download at http://SpliceGrapher.sf.net.

Indexed as

Databases, Nucleic AcidSoftwareAlgorithmsAlternative SplicingArabidopsisComputational BiologyExpressed Sequence TagsGenome, HumanHumansRNA Splice SitesRNA Splice Sites

Identifiers

PMID22293517
PMCPMC3334585

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.