Integrative analysis of deep sequencing data identifies estrogen receptor early response genes and links ATAD3B to poor survival in breast cancer.

ABSTRACT: Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor α (ERα), H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estra

SUBMITTER: Ovaska K 

PROVIDER: S-EPMC3688481 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature