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dc.contributor.authorWilson, Gareth A.
dc.contributor.authorBeck, Stephan
dc.date.accessioned2021-02-10T12:58:18Z
dc.date.issued2016
dc.date.submitted2019-10-04 14:42:20
dc.date.submitted2020-04-01T13:39:01Z
dc.date.submitted2017-04-12 23:55
dc.date.submitted2019-10-04 14:42:20
dc.date.submitted2020-04-01T13:39:01Z
dc.date.submitted2017-03-01 23:55:55
dc.date.submitted2019-10-04 14:42:20
dc.date.submitted2020-04-01T13:39:01Z
dc.identifier627316
dc.identifierOCN: 1030819225
dc.identifierhttp://library.oapen.org/handle/20.500.12657/31543
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/26001
dc.description.abstractThe combinatorial number of possible methylomes in biological time and space is astronomical. Consequently, the computational analysis of methylomes needs to cater for a variety of data, throughput and resolution. Here, we review recent advances in 2nd generation sequencing (2GS) with a focus on the different methods used for the analysis of MeDIP-seq data. The challenges and opportunities presented by the integration of methylation data with other genomic data types are discussed as is the potential impact of emerging 3rd generation sequencing (3GS) based technologies on methylation analysis.
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::P Mathematics & science::PS Biology, life sciences::PSB Biochemistry
dc.subject.othersequencing
dc.subject.otherbiochemistry
dc.titleChapter 5 Computational Analysis and Integration of MeDIP-seq Methylome Data
dc.typechapter
oapen.identifier.doi10.5772/61207
oapen.relation.isPublishedBy035ecc65-6737-43cf-a13a-6bdf67ce01f4
oapen.relation.isPartOfBook056456de-7826-45bc-9bac-a5a1144f80cf
oapen.relation.isFundedByf6fcd900-36e2-4bc9-939e-ad820802e21f
oapen.relation.isFundedByd859fbd3-d884-4090-a0ec-baf821c9abfd
oapen.collectionWellcome
oapen.grant.number99148
dc.relationisFundedByd859fbd3-d884-4090-a0ec-baf821c9abfd
dc.chapternumber1


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open access
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