Probing transcription factor combinatorics in different promoter classes and in enhancers

19 pagesInternational audienceBackgroundIn eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combinations among different gene classes, regulatory regions and cell types.ResultsWe propose a new approach named TFcoop to infer the TF combinations involved in the binding of a target TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the nucleotide content of the sequences and of the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combinations involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for predicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in promoters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and enhancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the different targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately distinguish promoters associated with specific biological processes.ConclusionsTFcoop appears as an accurate approach for studying TF combinations. Its use on ENCODE and FANTOM data allowed us to discover important properties of human TF combinations in different promoter classes and in enhancers. The R code for learning a TFcoop model and for reproducing the main experiments described in the paper is available in an R Markdown file at address https://gite.lirmm.fr/brehelin/TFcoop

Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis

Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth “Dialogue for Reverse Engineering Assessments and Methods” (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on “Systems Genetics” proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics

Reconstruction quality of a biological network when its constituting elements are partially observed

International audienceUnravelling regulatory regulations between biological entities is of utmost importance to understand the functioning of living organisms. As the number of available samples is often very low (often less than one hundred), inference methods are frequently performed on a subset of variables which make sense in the mechanisms under study. Classical remedies are either data driven (e.g., differentially expressed genes) or knowledge driven (e.g., using ontology information). However, whatever the chosen solution, important variables are very likely missed by the selection process, which is the issue at stake in the present paper

Modeling transcription factor combinatorics in promoters and enhancers

We propose a new approach (TFcoop) that takes into account cooperation between transcription factors (TFs) for predicting TF binding sites. For a given a TF, TFcoop bases its prediction upon the binding affinity of the target TF as well as any other TF identified as cooperating with this TF. The set of cooperating TFs and the model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combinations involved in the binding of 106 different TFs on 41 different cell types and in four different regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. Our experiments show that the approach is accurate and outperforms simple PWM methods. Moreover, analysis of the learned models sheds light on important properties of TF combinations. First, for a given TF and region, we show that TF combinations governing the binding of the target TF are similar for the different cell-types. Second, for a given TF, we observe that TF combinations are different between promoters and enhancers, but similar for promoters of distinct gene classes (mRNAs, lncRNAs and miRNAs). Analysis of the TFs cooperating with the different targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately distinguish promoters into classes associated with specific biological processes

GIANT: Galaxy-based Interactive tools for ANalysis of Transcriptomic data

GIANT is a User-friendly tools suite for micro-arrays analyses and for exploring RNA-seq & Micro-Arrays differential result