Evidence map›Paper›PMID 23537109›Full record

ArticleGenome biology2013

CRAC: an integrated approach to the analysis of RNA-seq reads.

Nicolas Philippe, Mikaël Salson, Thérèse Commes, Eric Rivals

Abstract read
In one paragraph

Article in Genome biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

42 citing papers in PubMed.

  1. A Versatile Ribo-seq Workflow for Enhanced Analysis of Ribosome Dynamics.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Alternative splicing analysis benchmark with DICAST.NAR genomics and bioinformatics · 2023
    Article
  7. Article
  8. Kmerator Suite: design of specificNAR genomics and bioinformatics · 2021
    Article
  9. Review
  10. Article
  11. Comparative Genome-Wide Transcriptome Analysis ofFrontiers in microbiology · 2021
    Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Improved Search of Large Transcriptomic Sequencing Databases Using Split Sequence Bloom Trees.Journal of computational biology : a journal of computational molecular cell biology · 2018
    Article
  19. Review
  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.

Nicolas Philippe
Mikaël Salson
Thérèse Commes
Eric Rivals

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A large number of RNA-sequencing studies set out to predict mutations, splice junctions or fusion RNAs. We propose a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-seq read analysis. A k-mer profiling approach detects candidate mutations, indels and splice or chimeric junctions in each single read. CRAC increases precision compared with existing tools, reaching 99:5% for splice junctions, without losing sensitivity. Importantly, CRAC predictions improve with read length. In cancer libraries, CRAC recovered 74% of validated fusion RNAs and predicted novel recurrent chimeric junctions. CRAC is available at http://crac.gforge.inria.fr.

Indexed as

AlgorithmsBreast NeoplasmsComputer SimulationFemaleGene LibraryGenomeHumansRNA Splice SitesSequence Analysis, RNARNA Splice Sites

Identifiers

PMID23537109
PMCPMC4053775

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.