Evidence map›Paper›PMID 29650026›Full record

ArticleGenome biology2018

SQUID: transcriptomic structural variation detection from RNA-seq.

Cong Ma, Mingfu Shao, Carl Kingsford

Abstract read
In one paragraph

Article in Genome biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Cancers · 2024
    Article
  8. Article
  9. OnACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Frequency and Genomic Aspects of Intrinsic Resistance to Vismodegib in Locally Advanced Basal Cell Carcinoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022
    Article
  15. Review
  16. RNA-seq Fusion Detection in Clinical Oncology.Advances in experimental medicine and biology · 2022
    Article
  17. Article
  18. Identification and Characterization of Non-Coding RNAs in Thymoma.Medical science monitor : international medical journal of experimental and clinical research · 2021
    Article
  19. Full-Length Transcriptome Analysis ofFrontiers in cellular and infection microbiology · 2021
    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

3 authors.

Cong MaComputational Biology Department, Carnegie Mellon University, Pittsburgh, 5000 Forbes Ave., PA, USA.
Mingfu ShaoComputational Biology Department, Carnegie Mellon University, Pittsburgh, 5000 Forbes Ave., PA, USA.
Carl KingsfordComputational Biology Department, Carnegie Mellon University, Pittsburgh, 5000 Forbes Ave., PA, USA. carlk@cs.cmu.edu.

Funding

Algorithms for Managing Uncertainty in Chromosome Conformation Capture DataR01HG007104 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI KINGSFORD, CARLETON LEE · 2013 to 2015
$1.3M
Data Discovery: Computational Methods for Searching Short-Read Sequencing Experiments - Administrative SupplementR01GM122935 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI KINGSFORD, CARLETON LEE · 2017 to 2021
$1.2M
Fast k-mer Counting to Quantify Gene Expression and Improve Genome AssemblyR21HG006913 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI KINGSFORD, CARLETON LEE · 2012 to 2014
$430k
Gordon and Betty Moore Foundation GBMF4554National Science Foundation CCF-1256087National Science Foundation CCF-1319998NIGMS NIH HHS R01 GM122935NIH HHS R01HG007104NIH HHS R21HG006913Pennsylvania Department of Health 4100070287
6 · The paper itself

Abstract

Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to predict both fusion-gene and non-fusion-gene TSVs accurately from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model and doubles the precision on simulation data compared to other approaches. Using SQUID, we identify novel non-fusion-gene TSVs on TCGA samples.

Indexed as

AlgorithmsGenomic Structural VariationCell Line, TumorGene Expression ProfilingGene FusionGenes, Tumor SuppressorHumansSequence AlignmentSequence Analysis, RNASoftwareRNA-seqTCGATranscriptomic structural variation

Identifiers

PMID29650026
PMCPMC5896115

What OpenQuestion holds

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LicenceCC BY
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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.