Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees

BMC Bioinformatics

Table 1 Significant TFs selected in the 10-tissue experiment.

Transcription factor	Number of occurrences	Expressed in tissue(s)	Support from literature
HNF1	10	Liver	[27]
C/EBP	10	Liver	[27]
C/EBPalpha	10	Liver	[27]
AR	8	Liver	[28]
Sp1	8	Erythroid	[29]
E2F	7	Erythroid	[30]
MAZ	7	Erythroid	[31]
Tax/CREB	7	Erythroid	[32]
GATA1	7	Erythroid	[33]
ERRalpha	6	Liver, Heart	[34]
CREB	6	Erythroid	[32]
HNF4	6	Liver, Skeletal muscle	[27]
YY1	5	Erythroid	[35]
Cdx2	5	Skeletal muscle
LXR	5	Liver	[36]
GATA4	5	Heart	[37]
RFX1	5	Skeletal muscle
XBP1	5	Liver, Pancreatic islets	[38]
N-Myc	5	Heart	[39]

Transcription factors present in the regression tree in at least five of the 10 runs. References in bold refer to papers arguing for tissue-specificity of the given factor in the given tissue, whereas those in normal font point to papers showing the involvement of the given TF for the proper expression of some gene(s) expressed in the given tissue, but where the TF is not tissue-specific.

ISSN: 1471-2105