@article{
 title = {A stochastic model for the formation of spatial methylation patterns},
 type = {article},
 year = {2017},
 keywords = {DNA methylation,Hidden Markov model,Spatial stochastic model},
 volume = {10545 LNBI},
 websites = {https://link.springer.com/chapter/10.1007/978-3-319-67471-1_10},
 id = {c321ad54-fe9f-3e23-98ee-9076f44a9004},
 created = {2017-10-16T22:20:36.889Z},
 file_attached = {false},
 profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},
 last_modified = {2017-11-29T15:21:33.510Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2017, Springer International Publishing AG. DNA methylation is an epigenetic mechanism whose important role in development has been widely recognized. This epigenetic modification results in heritable changes in gene expression not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood and recently different 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 dependencies. We perform numerical analysis of the HMMs applied to bisulfite sequencing measurements and accurately predict wild-type data. In addition, we find evidence that the enzymes’ activities depend on the left 5’ neighborhood but not on the right 3’ neighborhood.},
 bibtype = {article},
 author = {Lück, A. and Giehr, P. and Walter, J. and Wolf, V.},
 doi = {10.1007/978-3-319-67471-1_10},
 journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

{"_id":"DARtYXbTnEvJFS7Bc","bibbaseid":"lck-giehr-walter-wolf-astochasticmodelfortheformationofspatialmethylationpatterns-2017","downloads":0,"creationDate":"2017-11-29T15:20:30.505Z","title":"A stochastic model for the formation of spatial methylation patterns","author_short":["Lück, A.","Giehr, P.","Walter, J.","Wolf, V."],"year":2017,"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/bbb99b2d-2278-3254-820f-2de6d915ce63","bibdata":{"title":"A stochastic model for the formation of spatial methylation patterns","type":"article","year":"2017","keywords":"DNA methylation,Hidden Markov model,Spatial stochastic model","volume":"10545 LNBI","websites":"https://link.springer.com/chapter/10.1007/978-3-319-67471-1_10","id":"c321ad54-fe9f-3e23-98ee-9076f44a9004","created":"2017-10-16T22:20:36.889Z","file_attached":false,"profile_id":"bbb99b2d-2278-3254-820f-2de6d915ce63","last_modified":"2017-11-29T15:21:33.510Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"private_publication":false,"abstract":"© 2017, Springer International Publishing AG. DNA methylation is an epigenetic mechanism whose important role in development has been widely recognized. This epigenetic modification results in heritable changes in gene expression not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood and recently different 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 dependencies. We perform numerical analysis of the HMMs applied to bisulfite sequencing measurements and accurately predict wild-type data. In addition, we find evidence that the enzymes’ activities depend on the left 5’ neighborhood but not on the right 3’ neighborhood.","bibtype":"article","author":"Lück, A. and Giehr, P. and Walter, J. and Wolf, V.","doi":"10.1007/978-3-319-67471-1_10","journal":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","bibtex":"@article{\n title = {A stochastic model for the formation of spatial methylation patterns},\n type = {article},\n year = {2017},\n keywords = {DNA methylation,Hidden Markov model,Spatial stochastic model},\n volume = {10545 LNBI},\n websites = {https://link.springer.com/chapter/10.1007/978-3-319-67471-1_10},\n id = {c321ad54-fe9f-3e23-98ee-9076f44a9004},\n created = {2017-10-16T22:20:36.889Z},\n file_attached = {false},\n profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},\n last_modified = {2017-11-29T15:21:33.510Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2017, Springer International Publishing AG. DNA methylation is an epigenetic mechanism whose important role in development has been widely recognized. This epigenetic modification results in heritable changes in gene expression not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood and recently different 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 dependencies. We perform numerical analysis of the HMMs applied to bisulfite sequencing measurements and accurately predict wild-type data. In addition, we find evidence that the enzymes’ activities depend on the left 5’ neighborhood but not on the right 3’ neighborhood.},\n bibtype = {article},\n author = {Lück, A. and Giehr, P. and Walter, J. and Wolf, V.},\n doi = {10.1007/978-3-319-67471-1_10},\n journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}","author_short":["Lück, A.","Giehr, P.","Walter, J.","Wolf, V."],"urls":{"Website":"https://link.springer.com/chapter/10.1007/978-3-319-67471-1_10"},"biburl":"https://bibbase.org/service/mendeley/bbb99b2d-2278-3254-820f-2de6d915ce63","bibbaseid":"lck-giehr-walter-wolf-astochasticmodelfortheformationofspatialmethylationpatterns-2017","role":"author","keyword":["DNA methylation","Hidden Markov model","Spatial stochastic model"],"metadata":{"authorlinks":{"wolf, v":"https://mosi.uni-saarland.de/research/"}},"downloads":0},"search_terms":["stochastic","model","formation","spatial","methylation","patterns","lück","giehr","walter","wolf"],"keywords":["dna methylation","hidden markov model","spatial stochastic model"],"authorIDs":["6TtMsgho6wSDppwNh"],"dataSources":["me3WD7pwWySxCKW8s","ya2CyA73rpZseyrZ8","5u2EFGtZ3pCiduxDD","dXRJbWa2wiNDJKxYE","qA8W3BSHDuk7cCNvk","2252seNhipfTmjEBQ"]}