Biostatistics & Computational Biology Branch
Order-restricted inference for gene expression patterns: The Branch developed methods based on order-restricted inference for classifying response profiles for genes over time or over doses, to aid in identifying families of genes that are differentially-expressed and possibly co-regulated. Downloadable software, ORIOGEN, is available without charge.
Transcription factor binding site analysis: The Branch is developing and implementing methods for detecting and discovering functional elements such as the cis-regulatory motifs in a set of sequences such as from ChIP-seq experiments using Markov models and Expectation Maximization (EM) methods. The Branch is also developing methods to identify transcription factor co-regulators in ChIP-seq datasets.
ChIP-seq data analysis: The Next-Gen sequencing based mRNA-seq and ChIP-seq are increasingly used for identifying genome-wide epigenetic/genetic changes. The new type and huge volume of data from these technologies, however, pose computational challenges unmet by existing methods. The Branch is also developing computational/statistical methods for identifying genomic loci that are differentially enriched in sequence read counts in ChIP-seq and mRNA-seq data.
Phenotypic anchoring: The Branch developed a modified k-prototypes semi-supervised clustering algorithm, which integrates and analyzes phenotypic observations, end-point measurements and associated biological information with gene expression data. The purpose of the algorithm is to identify biological mechanisms and pathways that are perturbed by environmental stressors. This approach allows for construction of phenotypic prototypes using key histopathologic severity scores, clinical chemistry measurements and significantly differentially expressed genes, which prototypes can group biological samples according to pathophysiological states.