ArticleNucleic acids research2014
Interaction-based discovery of functionally important genes in cancers.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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.
Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Topsy-Turvy: integrating a global view into sequence-based PPI prediction.Bioinformatics (Oxford, England) · 2022Article
- Decoding disease: from genomes to networks to phenotypes.Nature reviews. Genetics · 2021Review
- dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.Nucleic acids research · 2021Article
- Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms.Journal of genetics and genomics = Yi chuan xue bao · 2020Review
- PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities.Cell systems · 2020Article
- FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis.BMC bioinformatics · 2019Article
- Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions.Nucleic acids research · 2019Article
- Computational Approaches to Prioritize Cancer Driver Missense Mutations.International journal of molecular sciences · 2018Review
- Two critical positions in zinc finger domains are heavily mutated in three human cancer types.PLoS computational biology · 2018Article
- In silico cancer research towards 3R.BMC cancer · 2018Review
- Functional variomics and network perturbation: connecting genotype to phenotype in cancer.Nature reviews. Genetics · 2017Review
- Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes.Briefings in bioinformatics · 2016Review
- Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics.PLoS computational biology · 2016Review
- Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations.Nature genetics · 2016Article
- An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer.PLoS computational biology · 2015Article
- Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach.Genome medicine · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
A major challenge in cancer genomics is uncovering genes with an active role in tumorigenesis from a potentially large pool of mutated genes across patient samples. Here we focus on the interactions that proteins make with nucleic acids, small molecules, ions and peptides, and show that residues within proteins that are involved in these interactions are more frequently affected by mutations observed in large-scale cancer genomic data than are other residues. We leverage this observation to predict genes that play a functionally important role in cancers by introducing a computational pipeline (http://canbind.princeton.edu) for mapping large-scale cancer exome data across patients onto protein structures, and automatically extracting proteins with an enriched number of mutations affecting their nucleic acid, small molecule, ion or peptide binding sites. Using this computational approach, we show that many previously known genes implicated in cancers are enriched in mutations within the binding sites of their encoded proteins. By focusing on functionally relevant portions of proteins--specifically those known to be involved in molecular interactions--our approach is particularly well suited to detect infrequent mutations that may nonetheless be important in cancer, and should aid in expanding our functional understanding of the genomic landscape of cancer.
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What OpenQuestion holds
Registered trials
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.