Integrative Analysis of Deep Sequencing Data Identifies Estrogen Receptor Early Response Genes and Links ATAD3B to Poor Survival in Breast Cancer

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 [Image: see text] ([Image: see text]), H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 [Image: see text] early responsive genes and compared the biological functions of the genes having [Image: see text] binding within 20 kb of the transcription start site (TSS) to genes without such binding site. Our results show that the non-genomic action of [Image: see text] via the MAPK pathway, instead of direct [Image: see text] binding, may be responsible for early cell responses to [Image: see text] activation. Our results also indicate that the [Image: see text] responsive genes triggered by the genomic pathway are transcribed faster than those without [Image: see text] binding sites. The survival analysis of the 777 [Image: see text] responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have [Image: see text] binding site within 20 kb of its TSS, to be significantly associated with poor patient survival.