@article{
 title = {Hidden markov modelling reveals neighborhood dependence of dnmt3a and 3b activity},
 type = {article},
 year = {2019},
 keywords = {DNA methylation,hidden Markov model,spatial stochastic model},
 volume = {16},
 id = {560c6d16-0b8d-3f79-80ec-da7bc8784e9e},
 created = {2024-05-14T10:29:56.268Z},
 file_attached = {false},
 profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},
 group_id = {5fd9c994-005c-3618-9b81-242857024eb0},
 last_modified = {2024-05-14T10:29:56.268Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. DNA methylation is an epigenetic mark whose important role in development has been widely recognized. This epigenetic modification results in heritable information not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood. Several mechanistic models of enzyme activities responsible for DNA methylation have been proposed. Here, we extend existing Hidden Markov Models HMMs for DNA methylation by describing the occurrence of spatial methylation patterns over time and propose several models with different neighborhood dependences. Furthermore, we investigate correlations between the neighborhood dependence and other genomic information. We perform numerical analysis of the HMMs applied to comprehensive hairpin and non-hairpin bisulfite sequencing measurements and accurately predict wild-type data. We find evidence that the activities of Dnmt3a and Dnmt3b responsible for de novo methylation depend on 5' left but not on 3' right neighboring CpGs in a sequencing string.},
 bibtype = {article},
 author = {Luck, A. and Giehr, P. and Nordstrom, K. and Walter, J. and Wolf, V.},
 doi = {10.1109/TCBB.2019.2910814},
 journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
 number = {5}
}

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DNA methylation is an epigenetic mark whose important role in development has been widely recognized. This epigenetic modification results in heritable information not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood. Several mechanistic models of enzyme activities responsible for DNA methylation have been proposed. Here, we extend existing Hidden Markov Models HMMs for DNA methylation by describing the occurrence of spatial methylation patterns over time and propose several models with different neighborhood dependences. Furthermore, we investigate correlations between the neighborhood dependence and other genomic information. We perform numerical analysis of the HMMs applied to comprehensive hairpin and non-hairpin bisulfite sequencing measurements and accurately predict wild-type data. We find evidence that the activities of Dnmt3a and Dnmt3b responsible for de novo methylation depend on 5' left but not on 3' right neighboring CpGs in a sequencing string.","bibtype":"article","author":"Luck, A. and Giehr, P. and Nordstrom, K. and Walter, J. and Wolf, V.","doi":"10.1109/TCBB.2019.2910814","journal":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","number":"5","bibtex":"@article{\n title = {Hidden markov modelling reveals neighborhood dependence of dnmt3a and 3b activity},\n type = {article},\n year = {2019},\n keywords = {DNA methylation,hidden Markov model,spatial stochastic model},\n volume = {16},\n id = {560c6d16-0b8d-3f79-80ec-da7bc8784e9e},\n created = {2024-05-14T10:29:56.268Z},\n file_attached = {false},\n profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},\n group_id = {5fd9c994-005c-3618-9b81-242857024eb0},\n last_modified = {2024-05-14T10:29:56.268Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. DNA methylation is an epigenetic mark whose important role in development has been widely recognized. This epigenetic modification results in heritable information not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood. Several mechanistic models of enzyme activities responsible for DNA methylation have been proposed. Here, we extend existing Hidden Markov Models HMMs for DNA methylation by describing the occurrence of spatial methylation patterns over time and propose several models with different neighborhood dependences. Furthermore, we investigate correlations between the neighborhood dependence and other genomic information. We perform numerical analysis of the HMMs applied to comprehensive hairpin and non-hairpin bisulfite sequencing measurements and accurately predict wild-type data. 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