A Small-Molecule Inhibitor of Lin28

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Analysis of Gene Regulatory Networks Inferred from ChIP-seq Data

Computational network biology aims to understand cell behavior through complex network analysis. The Chromatin ImmunoPrecipitation sequencing (ChIP-seq) technique allows interrogating the physical binding interactions between proteins and DNA using Next-Generation Sequencing. Taking advantage of this technique, in this study we propose a computational framework to analyze gene regulatory networks built from ChIP-seq data. We focus on two different cell lines: GM12878, a normal lymphoblastoid cell line, and K562, an immortalised myelogenous leukemia cell line. In the proposed framework, we preprocessed the data, derived network relationships in the data, analyzed their network properties, and identified differences between the two cell lines through network comparison analysis. Throughout our analysis, we identified known cancer genes and other genes that may play important roles in chronic myelogenous leukemia

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets

BACKGROUND: The global effort to annotate the non-coding portion of the human genome relies heavily on chromatin immunoprecipitation data generated with high-throughput DNA sequencing (ChIP-seq). ChIP-seq is generally successful in detailing the segments of the genome bound by the immunoprecipitated transcription factor (TF), however almost all datasets contain genomic regions devoid of the canonical motif for the TF. It remains to be determined if these regions are related to the immunoprecipitated TF or whether, despite the use of controls, there is a portion of peaks that can be attributed to other causes. RESULTS: Analyses across hundreds of ChIP-seq datasets generated for sequence-specific DNA binding TFs reveal a small set of TF binding profiles for which predicted TF binding site motifs are repeatedly observed to be significantly enriched. Grouping related binding profiles, the set includes: CTCF-like, ETS-like, JUN-like, and THAP11 profiles. These frequently enriched profiles are termed ‘zingers’ to highlight their unanticipated enrichment in datasets for which they were not the targeted TF, and their potential impact on the interpretation and analysis of TF ChIP-seq data. Peaks with zinger motifs and lacking the ChIPped TF’s motif are observed to compose up to 45% of a ChIP-seq dataset. There is substantial overlap of zinger motif containing regions between diverse TF datasets, suggesting a mechanism that is not TF-specific for the recovery of these regions. CONCLUSIONS: Based on the zinger regions proximity to cohesin-bound segments, a loading station model is proposed. Further study of zingers will advance understanding of gene regulation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-014-0412-4) contains supplementary material, which is available to authorized users