Comparison of algorithms for the detection of cancer drivers at subgene resolution

[1]  Ilya Shmulevich,et al.  Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression , 2017, PLoS Comput. Biol..

[2]  C. Sander,et al.  3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets , 2017, Genome Medicine.

[3]  D. Cacchiarelli,et al.  Phenotypic Characterization of a Comprehensive Set of MAPK1/ERK2 Missense Mutants. , 2016, Cell reports.

[4]  Li Ding,et al.  Protein-structure-guided discovery of functional mutations across 19 cancer types , 2016, Nature Genetics.

[5]  Martin L. Miller,et al.  Pan-cancer analysis of mutation hotspots in protein domains , 2016 .

[6]  David L. Masica,et al.  Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure. , 2016, Cancer research.

[7]  A. Reményi,et al.  Systematic analysis of somatic mutations driving cancer: Uncovering functional protein regions in disease development , 2016 .

[8]  Yusuke Nakamura,et al.  Germline PARP4 mutations in patients with primary thyroid and breast cancers. , 2016, Endocrine-related cancer.

[9]  Matthew Mort,et al.  mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome , 2016, Human mutation.

[10]  Heiko Müller,et al.  LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer , 2016, BMC Bioinformatics.

[11]  Michael P Snyder,et al.  Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations , 2015, Nature Genetics.

[12]  Mayya Sedova,et al.  PDBFlex: exploring flexibility in protein structures , 2015, Nucleic Acids Res..

[13]  N. Socci,et al.  Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity , 2015, Nature Biotechnology.

[14]  Yusuke Nakamura,et al.  Germline PARP 4 mutations in patients with primary thyroid and breast cancers , 2016 .

[15]  Non-coding recurrent mutations in chronic lymphocytic leukaemia , 2016, Nature.

[16]  I. Tomlinson,et al.  The mini-driver model of polygenic cancer evolution , 2015, Nature Reviews Cancer.

[17]  Joaquín Dopazo,et al.  A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces , 2015, PLoS Comput. Biol..

[18]  Chad J. Miller,et al.  Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling , 2015, Cell.

[19]  E. Lander,et al.  Comprehensive assessment of cancer missense mutation clustering in protein structures , 2015, Proceedings of the National Academy of Sciences.

[20]  R. Nussinov,et al.  'Latent drivers' expand the cancer mutational landscape. , 2015, Current opinion in structural biology.

[21]  Kathleen Marchal,et al.  SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering , 2015, BMC Bioinformatics.

[22]  Tirso Pons,et al.  Structure-PPi: a module for the annotation of cancer-related single-nucleotide variants at protein–protein interfaces , 2015, Bioinform..

[23]  Jofre Tenorio-Laranga,et al.  dSysMap: exploring the edgetic role of disease mutations , 2015, Nature Methods.

[24]  Fan Yang,et al.  Protein Domain-Level Landscape of Cancer-Type-Specific Somatic Mutations , 2015, PLoS Comput. Biol..

[25]  Paola Guglielmelli,et al.  Effect of mutation order on myeloproliferative neoplasms. , 2015, The New England journal of medicine.

[26]  Mary Goldman,et al.  The UCSC Cancer Genomics Browser: update 2015 , 2014, Nucleic Acids Res..

[27]  Adam Godzik,et al.  Cancer3D: understanding cancer mutations through protein structures , 2014, Nucleic Acids Res..

[28]  Benjamin J. Raphael,et al.  Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations across Pathways and Protein Complexes , 2014, Nature Genetics.

[29]  Adam Godzik,et al.  e-Driver: a novel method to identify protein regions driving cancer , 2014, Bioinform..

[30]  Qingxia Chen,et al.  MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis , 2014, Genome Biology.

[31]  Benjamin J. Raphael,et al.  Expanding the computational toolbox for mining cancer genomes , 2014, Nature Reviews Genetics.

[32]  Steven J. M. Jones,et al.  Comprehensive molecular profiling of lung adenocarcinoma , 2014, Nature.

[33]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of urothelial bladder carcinoma , 2014, Nature.

[34]  S. Gabriel,et al.  Discovery and saturation analysis of cancer genes across 21 tumor types , 2014, Nature.

[35]  The Cancer Genome Atlas Research Network,et al.  Comprehensive molecular characterization of urothelial bladder carcinoma , 2014, Nature.

[36]  Kei-Hoi Cheung,et al.  A spatial simulation approach to account for protein structure when identifying non-random somatic mutations , 2013, BMC Bioinformatics.

[37]  D. Haussler,et al.  The Somatic Genomic Landscape of Glioblastoma , 2013, Cell.

[38]  Gary D Bader,et al.  Comprehensive identification of mutational cancer driver genes across 12 tumor types , 2013, Scientific Reports.

[39]  P. A. Futreal,et al.  Emerging patterns of somatic mutations in cancer , 2013, Nature Reviews Genetics.

[40]  Michael P. Schroeder,et al.  IntOGen-mutations identifies cancer drivers across tumor types , 2013, Nature Methods.

[41]  David Tamborero,et al.  OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes , 2013, Bioinform..

[42]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[43]  Gary D Bader,et al.  Computational approaches to identify functional genetic variants in cancer genomes , 2013, Nature Methods.

[44]  Steven A. Roberts,et al.  Mutational heterogeneity in cancer and the search for new cancer genes , 2014 .

[45]  Kei-Hoi Cheung,et al.  A graph theoretic approach to utilizing protein structure to identify non-random somatic mutations , 2013, BMC Bioinformatics.

[46]  Gregory A. Ryslik,et al.  Utilizing protein structure to identify non-random somatic mutations , 2013, BMC Bioinformatics.

[47]  Gary D Bader,et al.  Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers , 2013 .

[48]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[49]  Steven A. Roberts,et al.  Mutational heterogeneity in cancer and the search for new cancer-associated genes , 2013 .

[50]  A. Gonzalez-Perez,et al.  Functional impact bias reveals cancer drivers , 2012, Nucleic acids research.

[51]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[52]  I. Tomlinson,et al.  The continuum model of selection in human tumors: general paradigm or niche product? , 2012, Cancer research.

[53]  Jing Hu,et al.  SIFT web server: predicting effects of amino acid substitutions on proteins , 2012, Nucleic Acids Res..

[54]  R. Arceci.  Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing , 2012 .

[55]  David Haussler,et al.  Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM , 2010, Bioinform..

[56]  Jingjing Ye,et al.  Statistical method on nonrandom clustering with application to somatic mutations in cancer , 2010, BMC Bioinformatics.

[57]  M. Vidal,et al.  Edgetic perturbation models of human inherited disorders , 2009, Molecular systems biology.

[58]  G. Unger,et al.  Protein kinase CK2--a key suppressor of apoptosis. , 2008, Advances in enzyme regulation.

[59]  D. Seldin,et al.  CK2 as a positive regulator of Wnt signalling and tumourigenesis , 2005, Molecular and Cellular Biochemistry.

[60]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.