Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models.
BACKGROUND
Blood cell development is thought to be controlled by a circuit of transcription factors (TFs) and chromatin modifications that determine the cell fate through activating cell type-specific expression programs. To shed light on the interplay between histone marks and TFs during blood cell development, we model gene expression from regulatory signals by means of combinations of sparse linear regression models.


RESULTS
The mixture of sparse linear regression models was able to improve the gene expression prediction in relation to the use of a single linear model. Moreover, it performed an efficient selection of regulatory signals even when analyzing all TFs with known motifs (>600). The method identified interesting roles for histone modifications and a selection of TFs related to blood development and chromatin remodelling.


BACKGROUND
The method and datasets are available from http://www.cin.ufpe.br/~igcf/SparseMix.


BACKGROUND
igcf@cin.ufpe.br


BACKGROUND
Supplementary data are available at Bioinformatics online.
Center of Informatics, Federal University of Pernambuco, Recife 50740-560, Brazil.
Thais G
TG
do Rego
Helge G
HG
Roider
Francisco A T
FA
de Carvalho
Ivan G
IG
Costa
D000818:Animals; D001499:Bayes Theorem; D001665:Binding Sites; D001773:Blood Cells / Q000378:metabolism*; D002454:Cell Differentiation / Q000235:genetics; D053595:Embryonic Stem Cells / Q000378:metabolism; D044127:Epigenesis, Genetic; D006657:Histones / Q000378:metabolism; D016014:Linear Models; D051379:Mice; D011401:Promoter Regions, Genetic; D014157:Transcription Factors / Q000378:metabolism; D014158:Transcription, Genetic
D006657:Histones; D014157:Transcription Factors