MixChIP

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Rautio, Sini
dc.contributor.author Lähdesmäki, Harri
dc.date.accessioned 2016-12-16T14:09:47Z
dc.date.issued 2015-12-24
dc.identifier.citation Rautio , S & Lähdesmäki , H 2015 , ' MixChIP : a probabilistic method for cell type specific protein-DNA binding analysis ' BMC Bioinformatics . DOI: 10.1186/s12859-015-0834-3 en
dc.identifier.issn 1471-2105
dc.identifier.other PURE UUID: 5579cd2b-b73d-4e1f-8b3d-4f2014fa22cb
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/mixchip(5579cd2b-b73d-4e1f-8b3d-4f2014fa22cb).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/9934788/art_3A10.1186_2Fs12859_015_0834_3.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/23789
dc.description.abstract BACKGROUND: Transcription factors (TFs) are proteins that bind to DNA and regulate gene expression. To understand details of gene regulation, characterizing TF binding sites in different cell types, diseases and among individuals is essential. However, sometimes TF binding can only be measured from biological samples that contain multiple cell or tissue types. Sample heterogeneity can have a considerable effect on TF binding site detection. While manual separation techniques can be used to isolate a cell type of interest from heterogeneous samples, such techniques are challenging and can change intra-cellular interactions, including protein-DNA binding. Computational deconvolution methods have emerged as an alternative strategy to study heterogeneous samples and numerous methods have been proposed to analyze gene expression. However, no computational method exists to deconvolve cell type specific TF binding from heterogeneous samples. RESULTS: We present a probabilistic method, MixChIP, to identify cell type specific TF binding sites from heterogeneous chromatin immunoprecipitation sequencing (ChIP-seq) data. Our method simultaneously estimates the binding strength in different cell types as well as the proportions of different cell types in each sample when only partial prior information about cell type composition is available. We demonstrate the utility of MixChIP by analyzing ChIP-seq data from two cell lines which we artificially mix to generate (simulated) heterogeneous samples and by analyzing ChIP-seq data from breast cancer patients measuring oestrogen receptor (ER) binding in primary breast cancer tissues. We show that MixChIP is more accurate in detecting TF binding sites from multiple heterogeneous ChIP-seq samples than the standard methods which do not account for sample heterogeneity. CONCLUSIONS: Our results show that MixChIP can estimate cell-type proportions and identify cell type specific TF binding sites from heterogeneous ChIP-seq samples. Thus, MixChIP can be an invaluable tool in analyzing heterogeneous ChIP-seq samples, such as those originating from cancer studies. R implementation is available at http://research.ics.aalto.fi/csb/software/mixchip/ . en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries BMC Bioinformatics en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title MixChIP en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201612165966
dc.identifier.doi 10.1186/s12859-015-0834-3


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