ArticlePLoS computational biology2015
A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Article
- NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis.BMC bioinformatics · 2020Article
- LncRNAs as Chromatin Regulators in Cancer: From Molecular Function to Clinical Potential.Cancers · 2019Review
- Regulatory variants: from detection to predicting impact.Briefings in bioinformatics · 2019Review
- The Identification and Interpretation ofHigh-throughput · 2018Review
- Quantification of somatic mutation flow across individual cell division events by lineage sequencing.Genome research · 2018Article
- A pan-cancer atlas of cancer hallmark-associated candidate driver lncRNAs.Molecular oncology · 2018Article
- Reduced mutation rate in exons due to differential mismatch repair.Nature genetics · 2017Article
- Mismatch repair prefers exons.Nature genetics · 2017Article
- A method to reduce ancestry related germline false positives in tumor only somatic variant calling.BMC medical genomics · 2017Article
- Retraction Note to: Mutation pattern is an influential factor on functional mutation rates in cancer.Cancer cell international · 2017Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.
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