TY - JOUR
T1 - Mutational Signatures
T2 - From Methods to Mechanisms
AU - Kim, Yoo Ah
AU - Leiserson, Mark D.M.
AU - Moorjani, Priya
AU - Sharan, Roded
AU - Wojtowicz, Damian
AU - Przytycka, Teresa M.
N1 - Publisher Copyright:
© Annual Review of Biomedical Data Science.
PY - 2021/5/11
Y1 - 2021/5/11
N2 - Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.
AB - Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.
KW - cancer
KW - cancer evolution
KW - computational models
KW - mutagenic processes
KW - mutational signatures
KW - population genetics
UR - http://www.scopus.com/inward/record.url?scp=85114370118&partnerID=8YFLogxK
U2 - 10.1146/annurev-biodatasci-122320-120920
DO - 10.1146/annurev-biodatasci-122320-120920
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C2 - 34465178
AN - SCOPUS:85114370118
SN - 2574-3414
VL - 4
SP - 189
EP - 206
JO - Annual review of biomedical data science
JF - Annual review of biomedical data science
ER -