Evidence map›Paper›PMID 18951448›Full record

ArticleHuman mutation2008

Prediction and assessment of splicing alterations: implications for clinical testing.

Amanda B Spurdle, Fergus J Couch, Frans B L Hogervorst, Paolo Radice, Olga M Sinilnikova, IARC Unclassified Genetic Variants Working Group

Abstract read
In one paragraph

Article in Human mutation, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing 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

58 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Novel de novoCold Spring Harbor molecular case studies · 2020
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Non-Coding Variants inCancers · 2018
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Cancer prevention research (Philadelphia, Pa.) · 2017
    Article
  20. Review
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

6 authors.

Amanda B SpurdleGenetics and Population Health Division, Queensland Institute of Medical Research, Brisbane, Australia. Amanda.Spurdle@qimr.edu.au
Fergus J Couch
Frans B L Hogervorst
Paolo Radice
Olga M Sinilnikova
IARC Unclassified Genetic Variants Working Group

Funding

The Role of CHFR in Tumorigenesis and Paclitaxel-Sensitivity in Breast CancerP50CA116201 · NCI · MAYO CLINIC ROCHESTER · PI PETER C LUCAS · 2005 to 2026
$49.9M
BRCA2 missense mutations and breast cancerR01CA116167 · NCI · MAYO CLINIC ROCHESTER · PI COUCH, FERGUS JOSEPH · 2007 to 2011
$3.0M
BRCA1 and BRCA2 missense mutations and breast cancer riskU01CA116167 · NCI · MAYO CLINIC ROCHESTER · PI COUCH, FERGUS JOSEPH · 2013 to 2017
$2.7M
NCI NIH HHS CA116167NCI NIH HHS P50 CA116201NCI NIH HHS P50CA116201NCI NIH HHS R01 CA116167NCI NIH HHS U01 CA116167
6 · The paper itself

Abstract

Sequence variants that may result in splicing alterations are a particular class of inherited variants for which consequences can be more readily assessed, using a combination of bioinformatic prediction methods and in vitro assays. There is also a general agreement that a variant would invariably be considered pathogenic on the basis of convincing evidence that it results in transcript(s) carrying a premature stop codon or an in-frame deletion disrupting known functional domain(s). This commentary discusses current practices used to assess the clinical significance of this class of variants, provides suggestions to improve assessment, and highlights the issues involved in routine assessment of potential splicing aberrations. We conclude that classification of sequence variants that may alter splicing is greatly enhanced by supporting in vitro analysis. Additional studies that assess large numbers of variants for induction of splicing aberrations and exon skipping are needed to define the contribution of splicing/exon skipping to cancer and disease. These studies will also provide the impetus for development of algorithms that better predict splicing patterns. To facilitate variant classification and development of more specific bioinformatic tools, we call for the deposition of all laboratory data from splicing analyses into national and international databases.

Indexed as

Alternative SplicingGenes, NeoplasmGenetic VariationAlgorithmsCodon, NonsenseComputational BiologyGenetic Predisposition to DiseaseGenetic TestingHumansSequence DeletionCodon, Nonsense

Identifiers

PMID18951448
PMCPMC2832470

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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.