Show simple item record

dc.contributor.authorWilson, Gareth A.
dc.contributor.authorBeck, Stephan
dc.date.accessioned2019-10-04 14:42:20
dc.date.accessioned2020-04-01T13:39:01Z
dc.date.accessioned2017-04-12 23:55
dc.date.accessioned2019-10-04 14:42:20
dc.date.accessioned2020-04-01T13:39:01Z
dc.date.accessioned2017-03-01 23:55:55
dc.date.accessioned2019-10-04 14:42:20
dc.date.accessioned2020-04-01T13:39:01Z
dc.date.available2020-04-01T13:39:01Z
dc.date.issued2016
dc.identifier627316
dc.identifierOCN: 1030819225en_US
dc.identifier.urihttp://library.oapen.org/handle/20.500.12657/31543
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.subject.classificationthema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSB Biochemistryen_US
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.isPublishedBy09f6769d-48ed-467d-b150-4cf2680656a1
oapen.relation.isPartOfBooka37acbe5-fa0f-4e03-8449-5716595869a4
oapen.relation.isFundedByd859fbd3-d884-4090-a0ec-baf821c9abfd
oapen.collectionWellcome
oapen.chapternumber1
oapen.grant.number99148
oapen.identifier.ocn1030819225


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record