ArticleNucleic acids research2015
LARVA: an integrative framework for large-scale analysis of recurrent variants in noncoding annotations.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
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- Preliminary examination of noncoding mutations of esophageal squamous cell carcinoma in African Americans.Frontiers in genetics · 2026Article
- Leveraging Tissue-Specific Enhancer-Target Gene Regulatory Networks Identifies Enhancer Somatic Mutations That Functionally Impact Lung Cancer.Cancer research · 2024Article
- Incorporating mutational heterogeneity to identify genes that are enriched for synonymous mutations in cancer.BMC bioinformatics · 2023Article
- Cancer driver mutations: predictions and reality.Trends in molecular medicine · 2023Review
- Unified views on variant impact across many diseases.Trends in genetics : TIG · 2023Review
- Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization.BMC bioinformatics · 2023Article
- Widespread perturbation of ETS factor binding sites in cancer.Nature communications · 2023Article
- Comparative Genomics Provides Etiologic and Biological Insight into Melanoma Subtypes.Cancer discovery · 2022Article
- Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers.Nucleic acids research · 2022Article
- Genome-wide mapping of somatic mutation rates uncovers drivers of cancer.Nature biotechnology · 2022Article
- Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis.Clinical epigenetics · 2022Review
- Transcription Factor-Centric Approach to Identify Non-Recurring Putative Regulatory Drivers in Cancer.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2022Article
- Genome-wide analysis of somatic noncoding mutation patterns in cancer.Science (New York, N.Y.) · 2022Article
- Article
- Functional annotation of noncoding mutations in cancer.Life science alliance · 2021Article
- Non-coding driver mutations in human cancer.Nature reviews. Cancer · 2021Review
- Multi-resolution modeling of a discrete stochastic process identifies causes of cancer.... International Conference on Learning Representations · 2021Article
- Pan-cancer noncoding genomic analysis identifies functionaliScience · 2021Article
- Detection of new drivers of frequent B-cell lymphoid neoplasms using an integrated analysis of whole genomes.PloS one · 2021Article
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5 authors.
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Abstract
In cancer research, background models for mutation rates have been extensively calibrated in coding regions, leading to the identification of many driver genes, recurrently mutated more than expected. Noncoding regions are also associated with disease; however, background models for them have not been investigated in as much detail. This is partially due to limited noncoding functional annotation. Also, great mutation heterogeneity and potential correlations between neighboring sites give rise to substantial overdispersion in mutation count, resulting in problematic background rate estimation. Here, we address these issues with a new computational framework called LARVA. It integrates variants with a comprehensive set of noncoding functional elements, modeling the mutation counts of the elements with a β-binomial distribution to handle overdispersion. LARVA, moreover, uses regional genomic features such as replication timing to better estimate local mutation rates and mutational hotspots. We demonstrate LARVA's effectiveness on 760 whole-genome tumor sequences, showing that it identifies well-known noncoding drivers, such as mutations in the TERT promoter. Furthermore, LARVA highlights several novel highly mutated regulatory sites that could potentially be noncoding drivers. We make LARVA available as a software tool and release our highly mutated annotations as an online resource (larva.gersteinlab.org).
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