Page 99 - Read Online
P. 99
Papadodima et al. J Transl Genet Genom 2019;3:7. I https://doi.org/10.20517/jtgg.2018.33 Page 9 of 12
20. Greenman C, Stephens P, Smith R, Dalgliesh GL, Hunter C, et al. Patterns of somatic mutation in human cancer genomes. Nature
2007;446:153-8.
21. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S, et al. Signatures of mutational processes in human cancer. Nature
2013;500:415-21.
22. Hayward NK, Wilmott JS, Waddell N, Johansson PA, Field MA, et al. Whole-genome landscapes of major melanoma subtypes. Nature
2017;545:175-80.
23. Gonzalez-Perez A, Lopez-Bigas N. Functional impact bias reveals cancer drivers. Nucleic Acids Res 2012;40:e169.
24. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, et al. IntOGen-mutations identifies cancer drivers across
tumor types. Nature methods 2013;10:1081-2.
25. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, et al. Mutational heterogeneity in cancer and the search for new cancer-
associated genes. Nature 2013;499:214-8.
26. Raphael BJ, Dobson JR, Oesper L, Vandin F. Identifying driver mutations in sequenced cancer genomes: computational approaches to
enable precision medicine. Genome medicine 2014;6:5.
27. Xu C. A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data. Comput Struct Biotechnol J
2018;16:15-24.
28. Zhang J, Liu J, Sun J, Chen C, Foltz G, et al. Identifying driver mutations from sequencing data of heterogeneous tumors in the era of
personalized genome sequencing. Brief Bioinformatics 2014;15:244-55.
29. Cosmic. COSMIC - Catalogue of Somatic Mutations in Cancer [Internet]. Available from: https://cancer.sanger.ac.uk/cosmic. [Last accessed
on 24 Feb 2019]
30. Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res
2017;45:D777-83.
31. Kassahn KS, Holmes O, Nones K, Patch AM, Miller DK, et al. Somatic point mutation calling in low cellularity tumors. PLoS One
2013;8:e74380.
32. Radenbaugh AJ, Ma S, Ewing A, Stuart JM, Collisson EA, et al. RADIA: RNA and DNA integrated analysis for somatic mutation detection.
PLoS One 2014;9:e111516.
33. Hansen NF, Gartner JJ, Mei L, Samuels Y, Mullikin JC. Shimmer: detection of genetic alterations in tumors using next-generation sequence
data. Bioinformatics 2013;29:1498-503.
34. SOAP : Short Oligonucleotide Analysis Package [Internet]. Available from: http://soap.genomics.org.cn/. [Last accessed on 24 Feb 2019]
35. Lai Z, Markovets A, Ahdesmaki M, Chapman B, Hofmann O, et al. VarDict: a novel and versatile variant caller for next-generation
sequencing in cancer research. Nucleic Acids Res 2016;44:e108.
36. Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, et al. VarScan 2: somatic mutation and copy number alteration discovery in
cancer by exome sequencing. Genome Res 2012;22:568-76.
37. Jones D, Raine KM, Davies H, Tarpey PS, Butler AP, et al. cgpCaVEManWrapper: Simple Execution of CaVEMan in Order to Detect
Somatic Single Nucleotide Variants in NGS Data. Curr Protoc Bioinformatics 2016;56:15.10.1-15.10.18.
38. Wang W, Wang P, Xu F, Luo R, Wong MP, et al. FaSD-somatic: a fast and accurate somatic SNV detection algorithm for cancer genome
sequencing data. Bioinformatics 2014;30:2498-500.
39. Roth A, Ding J, Morin R, Crisan A, Ha G, et al. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/
tumour paired next-generation sequencing data. Bioinformatics 2012;28:907-13.
40. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from
sequencing data. Bioinformatics. 2011;27:2987-93.
41. Christoforides A, Carpten JD, Weiss GJ, Demeure MJ, Von Hoff DD, et al. Identification of somatic mutations in cancer through Bayesian-
based analysis of sequenced genome pairs. BMC Genomics 2013;14:302.
42. Liu Y, Loewer M, Aluru S, Schmidt B. SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.
BMC Syst Biol 2016;10:47.
43. Larson DE, Harris CC, Chen K, Koboldt DC, Abbott TE, et al. SomaticSniper: identification of somatic point mutations in whole genome
sequencing data. Bioinformatics 2012;28:311-7.
44. Kim S, Jeong K, Bhutani K, Lee J, Patel A, et al. Virmid: accurate detection of somatic mutations with sample impurity inference. Genome
Biol 2013;14:R90.
45. Gerstung M, Beisel C, Rechsteiner M, Wild P, Schraml P, et al. Reliable detection of subclonal single-nucleotide variants in tumour cell
populations. Nat Commun 2012;3:811.
46. Shiraishi Y, Sato Y, Chiba K, Okuno Y, Nagata Y, et al. An empirical Bayesian framework for somatic mutation detection from cancer
genome sequencing data. Nucleic Acids Res 2013;41:e89.
47. Wilm A, Aw PPK, Bertrand D, Yeo GHT, Ong SH, et al. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-
population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res 2012;40:11189-201.
48. Carrot-Zhang J, Majewski J. LoLoPicker: detecting low allelic-fraction variants from low-quality cancer samples. Oncotarget 2017;8:37032-
40.
49. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, et al. Sensitive detection of somatic point mutations in impure and
heterogeneous cancer samples. Nat Biotechnol 2013;31:213-9.
50. Saunders CT, Wong WSW, Swamy S, Becq J, Murray LJ, et al. Strelka: accurate somatic small-variant calling from sequenced tumor-
normal sample pairs. Bioinformatics 2012;28:1811-7.