Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator.
Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator.
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DOI：
10.1186/s12859-020-03772-3
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发表时间：
2020-10-07
影响因子：
3
通讯作者：
Alexandrov LB
中科院分区：
文献类型：
--
作者：
Bergstrom EN;Barnes M;Martincorena I;Alexandrov LB
执行统计检验需要零假设。在癌症基因组学中，一个关键的挑战是快速生成准确的体细胞突变景观，可以用作进行生物学发现的现实零假设。 在这里，我们介绍了SigProfilerSimulator，这是一个强大的工具，能够在几秒钟内以不同的分辨率模拟数千个癌症基因组的突变景观。将SigProfilerSimulator应用于2144个全基因组测序的癌症揭示：（i）大多数双碱基替换不是由于两个相邻的单碱基替换，而是可能作为单个基因组事件发生;（ii）需要± 2bp的扩展测序背景以更完整地捕获人类癌症中替换突变特征的模式;（iii）有关检测驱动基因常用生物信息学工具的假阳性发现率的信息。SigProfilerSimulator的广泛功能允许构建定制的零假设，并将其用于评估其他生物信息学工具的准确性或用于生物发现的下游统计分析。SigProfilerSimulator可在https://github.com/AlexandrovLab/SigProfilerSimulator上免费获得https://osf.io/usxjz/wiki/home/并在www.example.com上提供大量文档。
Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of ± 2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. SigProfilerSimulator’s breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/.
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影响因子：
12.3
作者：
McLaren W;Gil L;Hunt SE;Riat HS;Ritchie GR;Thormann A;Flicek P;Cunningham F
通讯作者：
Cunningham F
DOI：
10.1093/bioinformatics/btx346
发表时间：
2017-10-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Gel B;Serra E
通讯作者：
Serra E
影响因子：
4
作者：
Alexandrov, Ludmil B.;Stratton, Michael R.
通讯作者：
Stratton, Michael R.
影响因子：
0.9
作者：
Lemire, Daniel
通讯作者：
Lemire, Daniel
影响因子：
16.6
作者：
Morganella, Sandro;Alexandrov, Ludmil B.;Glodzik, Dominik;Zou, Xueqing;Davies, Helen;Staaf, Johan;Sieuwerts, Anieta M.;Brinkman, Arie B.;Martin, Sancha;Ramakrishna, Manasa;Butler, Adam;Kim, Hyung-Yong;Borg, Ake;Sotiriou, Christos;Futreal, P. Andrew;Campbell, Peter J.;Span, Paul N.;Van Laere, Steven;Lakhani, Sunil R.;Eyfjord, Jorunn E.;Thompson, Alastair M.;Stunnenberg, Hendrik G.;de Vijver, Marc J. van;Martens, John W. M.;Borresen-Dale, Anne-Lise;Richardson, Andrea L.;Kong, Gu;Thomas, Gilles;Sale, Julian;Rada, Cristina;Stratton, Michael R.;Birney, Ewan;Nik-Zainal, Serena
通讯作者：
Nik-Zainal, Serena