Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 324

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The KA/KS Ratio Test for Assessing the Protein-Coding Potential of Genomic Regions: An Empirical and Simulation Study

Open access
Nekrutenko, Anton, Makova, Kateryna D., Li, Wen-Hsiung·2002·Genome Research

Abstract

Comparative genomics is a simple, powerful way to increase the accuracy of gene prediction. In this study, we show the utility of a simple test for the identification of protein-coding exons using human/mouse sequence comparisons. The test takes advantage of the fact that in the vast majority of coding regions, synonymous substitutions (KS) occur much more frequently than nonsynonymous ones (KA) and uses the KA/KS ratio as the criterion. We show the following: (1) most of the human and mouse exons are sufficiently long and have a suitable degree of sequence divergence for the test to perform reliably; (2) the test is suited for the identification of long exons and single exon genes, which are difficult to predict by current methods; (3) the test has a false-negative rate, lower than most of current gene prediction methods and a false-positive rate lower than all current methods; (4) the test has been automated and can be used in combination with other existing gene-prediction methods.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/gr.200901

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 324

Metadata

Authors
Nekrutenko, A., Makova, K. D., & Li, W.-H.
Venue
Genome Research
Published
2001-12-14
Volume
12
Issue
1
Pages
198-202
ISSN
  • 1088-9051 (ISSN-L)
  • 1549-5469
OpenAlex
W2152873040
Cited by
324
References
2
Type
posted content
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.