Project description:This SuperSeries is composed of the SubSeries listed below. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. RekinM-bM-^@M-^Ys Janky, Annelien Verfaillie, Hana ImrichovM-CM-!, Bram Van de Sande, Laura Standaert, Valerie Christiaens, Gert Hulselmans, Koen Herten, Marina Naval Sanchez, Delphine Potier, Dmitry Svetlichnyy, Zeynep Kalender Atak, Mark Fiers, Jean-Christophe Marine, and Stein Aerts. PLOS Computational Biology, 2014. Refer to individual Series
Project description:RNA-seq and ChIP-seq on MCF-7 breast cancer cell line upon activation of p53 by the non-genotoxic small molecule Nutlin-3a RNA-seq on MCF7 without (NS) or with Nutlin-3a stimulation (S), in duplicate, using illumina HiSeq 2000
Project description:RNA-seq and ChIP-seq on MCF-7 breast cancer cell line upon activation of p53 by the non-genotoxic small molecule Nutlin-3a ChIP-seq on p53 in MCF7 with Nutlin-3a stimulation (S) in triplicate, and the control (input), in stimulated and non stimulated, using illumina HiSeq 2000
Project description:RNA-seq and ChIP-seq on MCF-7 breast cancer cell line upon activation of p53 by the non-genotoxic small molecule Nutlin-3a RNA-seq on MCF7 without (NS) or with Nutlin-3a stimulation (S), in duplicate, using illumina HiSeq 2000
Project description:Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.
Project description:Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, due to consideration of computational expense, most of existing DMD methods have to choose approximate schemes that greatly restrict the search space, leading to significant loss of predictive accuracy. In this paper, we propose Weakly-Supervised Motif Discovery (WSMD) to discover motifs from ChIP-seq datasets. In contrast to the learning strategies adopted by previous DMD methods, WSMD allows a "global" optimization scheme of the motif parameters in continuous space, thereby reducing the information loss of model representation and improving the quality of resultant motifs. Meanwhile, by exploiting the connection between DMD framework and existing weakly supervised learning (WSL) technologies, we also present highly scalable learning strategies for the proposed method. The experimental results on both real ChIP-seq datasets and synthetic datasets show that WSMD substantially outperforms former DMD methods (including DREME, HOMER, XXmotif, motifRG and DECOD) in terms of predictive accuracy, while also achieving a competitive computational speed.