000200887 001__ 200887
000200887 005__ 20190316235957.0
000200887 0247_ $$2doi$$a10.5075/epfl-thesis-6327
000200887 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis6327-0
000200887 02471 $$2nebis$$a10207580
000200887 037__ $$aTHESIS
000200887 041__ $$aeng
000200887 088__ $$a6327
000200887 245__ $$aComputational studies in epigenomics using histone modification data
000200887 269__ $$a2014
000200887 260__ $$aLausanne$$bEPFL$$c2014
000200887 336__ $$aTheses
000200887 502__ $$aProf. E. Telatar (président) ;
 Prof. B. Moret, Dr Ph. Bucher (directeurs) ;
 Prof. S. Hannenhalli, 
 Prof. F. Naef, 
 Prof. W. Stafford Noble (rapporteurs)
000200887 520__ $$aEpigenetic factors like histone modifications are known to play an important role in gene
 regulation and cell differentiation. Recently, thanks to advances in technologies like ChIP-Seq
 which is a high-throughput, high resolution, and low cost technology for studying histone
 modifications and transcription factors, we have large amounts of data available. Therefore
 computational techniques become important for studying and interpreting this data.
 In this thesis, we have focused on primarily building computational methods to analyze and
 study ChIP-Seq histone modification data. The work can be divided into two broad topics : (a)
 to process ChIP-Seq data computationally and to identify regions of biological interest ; (b)
 to use processed data for higher-level analysis to study problems in cell differentiation and
 evolution of cell types, based on phylogenetic approaches.
 In the first topic, this thesis makes a contribution by addressing two problems : (i) We propose
 a two-stage statistical method, called ChIPnorm, to normalize ChIP-Seq data, and to find
 differential regions in the genome, given two libraries of histone modifications of different
 cell types. We show that our method removes most of the bias in the data and also provides a
 normalization that enables direct comparison of values between the two cell types. We show
 that our method outperforms the state of the art techniques in literature. (ii) We propose
 probabilistic partitioning methods to discover significant patterns in ChIP-Seq data. Our
 methods work on the principle of expectation-maximization, is simple and flexible, and takes
 into account signal magnitude, shape, strand orientation, and shifts. It runs in linear time and
 gives improved results on the state of the art techniques especially when used on sparse data.
 In the second topic, we try to provide a link between the fields of epigenomics and evolution.
 We introduce the concept of cell-type trees based on the principles of phylogenetic inference on
 ChIP-Seq histone modification data. These cell-type trees are precisely defined and algorithmic
 techniques are designed to infer these trees from the data. In the process, we develop new
 data representation techniques and also a peak-finder to help us build good cell-type trees.
 We obtain biologically meaningful results and show that cell-type trees have the potential to
 study cell differentiation and the evolution of cell types across species.
000200887 6531_ $$aepigenomics
000200887 6531_ $$aepigenetics
000200887 6531_ $$ahistone modifications
000200887 6531_ $$aChIP-Seq
000200887 6531_ $$acell-type trees
000200887 6531_ $$aevolution
000200887 6531_ $$aphylogeny
000200887 6531_ $$aevolution of cell types
000200887 6531_ $$aChIPnorm
000200887 6531_ $$aprobabilistic partitioning
000200887 6531_ $$aexpectation maximization
000200887 6531_ $$aphylogenetic trees
000200887 700__ $$0242195$$aNair, Nishanth Ulhas$$g190820
000200887 720_2 $$0241987$$aMoret, Bernard$$edir.$$g172233
000200887 720_2 $$0244404$$aBucher, Philipp$$edir.$$g113607
000200887 8564_ $$s6595622$$uhttps://infoscience.epfl.ch/record/200887/files/EPFL_TH6327.pdf$$yn/a$$zn/a
000200887 909C0 $$0252020$$pLCBB$$xU11274
000200887 909C0 $$0252244$$pGR-BUCHER$$xU11780
000200887 909CO $$ooai:infoscience.tind.io:200887$$pIC$$pSV$$pthesis$$pthesis-bn2018$$pDOI$$qDOI2$$qGLOBAL_SET
000200887 917Z8 $$x108898
000200887 917Z8 $$x108898
000200887 918__ $$aIC$$cIIF$$dEDIC2005-2015
000200887 919__ $$aLCBB
000200887 920__ $$a2014-8-28$$b2014
000200887 970__ $$a6327/THESES
000200887 973__ $$aEPFL$$sPUBLISHED
000200887 980__ $$aTHESIS