Protocol for comparing gene-level selection on coding mutations between two groups of samples with Coselens.
Protocol for comparing gene-level selection on coding mutations between two groups of samples with Coselens.
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DOI:
10.1016/j.xpro.2023.102117
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发表时间:
2023-03-17
期刊:
影响因子:
--
通讯作者:
Koonin EV
中科院分区:
文献类型:
--
作者:
Iranzo J;Gruenhagen G;Calle-Espinosa J;Koonin EV
The study of genes that evolve under conditional selection can shed light on the genomic underpinnings of adaptation, revealing epistasis and phenotypic plasticity. This protocol describes how to use the Coselens package to compare gene-level selection between two groups of samples. After installing Coselens and preparing the datasets, a typical run on a laptop takes less than 10 min. Coselens is best suited to analyze somatic mutations and data from experimental evolution, for which independently evolved samples are available. For complete details on the use and execution of this protocol, please refer to Iranzo et al. (2022). Coselens quantifies gene-level conditional selection based on coding mutations Analyze medium-size datasets on a laptop in less than 10 min Input mutation tables from whole-genome or targeted sequencing Output genes subject to conditional selection, effect sizes, and p values Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The study of genes that evolve under conditional selection can shed light on the genomic underpinnings of adaptation, revealing epistasis and phenotypic plasticity. This protocol describes how to use the Coselens package to compare gene-level selection between two groups of samples. After installing Coselens and preparing the datasets, a typical run on a laptop takes less than 10 min. Coselens is best suited to analyze somatic mutations and data from experimental evolution, for which independently evolved samples are available.
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DOI:
10.1073/pnas.1807256115
发表时间:
2018-11-20
影响因子:
11.1
作者:
Persi E;Wolf YI;Leiserson MDM;Koonin EV;Ruppin E
通讯作者:
Ruppin E
影响因子:
64.5
作者:
Martincorena I;Raine KM;Gerstung M;Dawson KJ;Haase K;Van Loo P;Davies H;Stratton MR;Campbell PJ
通讯作者:
Campbell PJ
影响因子:
8.8
作者:
Iranzo, Jaime;Gruenhagen, George;V. Koonin, Eugene
通讯作者:
V. Koonin, Eugene
影响因子:
5.8
作者:
Zhao, Hao;Sun, Zhifu;Wang, Liguo
通讯作者:
Wang, Liguo
影响因子:
9.3
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通讯作者:
Cancer Genome Atlas Research Network