Asset Details
Do you wish to reserve the book?
Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator
Do you wish to request the book?
Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator
2020
Overview
Background
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.
Results
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.
Conclusions
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/
.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
