Publication Type:Journal Article
Source:Bioinformatics, Volume 31, Issue 15, p.2537-44 (2015)
Keywords:Algorithms, Cell Differentiation, Cell Lineage, Cells, Cultured, Chromatin Immunoprecipitation, Erythroid Cells, Erythropoiesis, Gene Expression Regulation, Gene Regulatory Networks, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Nuclear Receptor Subfamily 2, Group C, Member 2, Prions, ROC Curve, Sample Size, Stem Cells, Transcription Factors
MOTIVATION: Modeling regulatory networks using expression data observed in a differentiation process may help identify context-specific interactions. The outcome of the current algorithms highly depends on the quality and quantity of a single time-course dataset, and the performance may be compromised for datasets with a limited number of samples.
RESULTS: In this work, we report a multi-layer graphical model that is capable of leveraging many publicly available time-course datasets, as well as a cell lineage-specific data with small sample size, to model regulatory networks specific to a differentiation process. First, a collection of network inference methods are used to predict the regulatory relationships in individual public datasets. Then, the inferred directional relationships are weighted and integrated together by evaluating against the cell lineage-specific dataset. To test the accuracy of this algorithm, we collected a time-course RNA-Seq dataset during human erythropoiesis to infer regulatory relationships specific to this differentiation process. The resulting erythroid-specific regulatory network reveals novel regulatory relationships activated in erythropoiesis, which were further validated by genome-wide TR4 binding studies using ChIP-seq. These erythropoiesis-specific regulatory relationships were not identifiable by single dataset-based methods or context-independent integrations. Analysis of the predicted targets reveals that they are all closely associated with hematopoietic lineage differentiation.