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

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