Evidence map›Paper›PMID 21480434›Full record

ArticleHuman mutation2011

Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed.

Stephanie Hicks, David A Wheeler, Sharon E Plon, Marek Kimmel

Abstract read
In one paragraph

Article in Human mutation, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
116citing papers in PubMed, 1 pooled it
10.8field-weighted citation impact, top 1% 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

116 citing papers in PubMed, 1 synthesis or guideline pooled it, 227 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Evaluating the Performance ofCombinatorial chemistry & high throughput screening · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Theranostic Interpolation of Genomic Instability in Breast Cancer.International journal of molecular sciences · 2022
    Review
  20. Article

56 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Stephanie HicksDepartment of Statistics, Rice University, Houston, Texas, USA.
David A Wheeler
Sharon E Plon
Marek Kimmel
Rice University · USBaylor College of Medicine · US

Funding

Training Program in Biostatistics for Cancer ResearchT32CA096520 · NCI · RICE UNIVERSITY · PI KIMMEL, MAREK, YUAN, YING · 2003 to 2023
$4.5M
NCI NIH HHS T32 CA096520
6 · The paper itself

Abstract

Multiple algorithms are used to predict the impact of missense mutations on protein structure and function using algorithm-generated sequence alignments or manually curated alignments. We compared the accuracy with native alignment of SIFT, Align-GVGD, PolyPhen-2, and Xvar when generating functionality predictions of well-characterized missense mutations (n = 267) within the BRCA1, MSH2, MLH1, and TP53 genes. We also evaluated the impact of the alignment employed on predictions from these algorithms (except Xvar) when supplied the same four alignments including alignments automatically generated by (1) SIFT, (2) Polyphen-2, (3) Uniprot, and (4) a manually curated alignment tuned for Align-GVGD. Alignments differ in sequence composition and evolutionary depth. Data-based receiver operating characteristic curves employing the native alignment for each algorithm result in area under the curve of 78-79% for all four algorithms. Predictions from the PolyPhen-2 algorithm were least dependent on the alignment employed. In contrast, Align-GVGD predicts all variants neutral when provided alignments with a large number of sequences. Of note, algorithms make different predictions of variants even when provided the same alignment and do not necessarily perform best using their own alignment. Thus, researchers should consider optimizing both the algorithm and sequence alignment employed in missense prediction.

Indexed as

AlgorithmsAdaptor Proteins, Signal TransducingBRCA1 ProteinComputational BiologyGenes, Tumor SuppressorHumansMutation, MissenseMutL Protein Homolog 1MutS Homolog 2 ProteinNuclear ProteinsSequence AlignmentSequence Analysis, ProteinTumor Suppressor Protein p53Adaptor Proteins, Signal TransducingBRCA1 ProteinMLH1 protein, humanMSH2 protein, humanMutL Protein Homolog 1MutS Homolog 2 ProteinNuclear ProteinsTumor Suppressor Protein p53

Identifiers

PMID21480434
PMCPMC4154965
OpenAlexW2039878336

What OpenQuestion holds

Textmetadata
LicenceTDM
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.