The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
In silico prediction of splice-altering single nucleotide variants in the human genome
Open accessAbstract
In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.
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.
Integrity signals
Registry
Cites
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.
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,621
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183
Prediction of complete gene structures in human genomic DNA
Burge, C., & Karlin, S. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.0951 · Cited by 4,228
Pan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008) · Nature Genetics · doi:10.1038/ng.259 · Cited by 3,951
ROCR: visualizing classifier performance in R
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti623 · Cited by 3,200
Cited by
The Ensembl Variant Effect Predictor
McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016) · Genome Biology · doi:10.1186/s13059-016-0974-4 · Cited by 8,099
Liu, X., Wu, C., Li, C., & Boerwinkle, E. (2016) · Human Mutation · doi:10.1002/humu.22932 · Cited by 983
Self-citationGenome-wide Analyses Identify KIF5A as a Novel ALS Gene
Nicolas, A., Kenna, K. P., Renton, A. E., Ticozzi, N., Faghri, F., Chia, R., Dominov, J. A., Kenna, B. J., Nalls, M. A., Keagle, P., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.02.027 · Cited by 678
Splicing mutations in human genetic disorders: Examples, detection, and confirmation
Anna, A., & Monika, G. (2018) · Journal of Applied Genetics · doi:10.1007/s13353-018-0444-7 · Cited by 657
Liu, X., Li, C., Mou, C., Dong, Y., & Tu, Y. (2020) · Genome Medicine · doi:10.1186/s13073-020-00803-9 · Cited by 657
Self-citation
Metadata
- Authors
- Jian, X., Boerwinkle, E., & Liu, X.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-11-21
- Volume
- 42
- Issue
- 22
- Pages
- 13534-13544
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2117856964
- Cited by
- 537
- References
- 59
- FWCI
- 7.42
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: In silico prediction of splice-altering single nucleotide variants in the human genome. https://app.cochat.ai/papers/10.1093/nar/gku1206
- The paper
- Jian, X., Boerwinkle, E., & Liu, X. (2014). In silico prediction of splice-altering single nucleotide variants in the human genome. Nucleic Acids Research. https://doi.org/10.1093/nar/gku1206
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.
