Data exploration, quality control and statistical analysis of ChIP-exo/nexus experiments

Exploiting Genomic Features to Improve the Prediction of Transcription Factor-Binding Sites in Plants

The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performance of pattern matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows decision rule models to be learnt using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA sequence conservation, the chromatin state and various CRE footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TFs controlling metabolite biosynthesis at the organ level in Arabidopsis. We developed a tool-Wimtrap-to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports an R Shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa and Zea mays.

Comparative analysis of ChIP-exo peak-callers: impact of data quality, read duplication and binding subtypes

Background

ChIP (Chromatin immunoprecipitation)-exo has emerged as an important and versatile improvement over conventional ChIP-seq as it reduces the level of noise, maps the transcription factor (TF) binding location in a very precise manner, upto single base-pair resolution, and enables binding mode prediction. Availability of numerous peak-callers for analyzing ChIP-exo reads has motivated the need to assess their performance and report which tool executes reasonably well for the task.

Results

This study has focussed on comparing peak-callers that report direct binding events with those that report indirect binding events. The effect of strandedness of reads and duplication of data on the performance of peak-callers has been investigated. The number of peaks reported by each peak-caller is compared followed by a comparison of the annotated motifs present in the reported peaks. The significance of peaks is assessed based on the presence of a motif in top peaks. Indirect binding tools have been compared on the basis of their ability to identify annotated motifs and predict mode of protein-DNA interaction.

Conclusion

By studying the output of the peak-callers investigated in this study, it is concluded that the tools that use self-learning algorithms, i.e. the tools that estimate all the essential parameters from the aligned reads, perform better than the algorithms which require formation of peak-pairs. The latest tools that account for indirect binding of TFs appear to be an upgrade over the available tools, as they are able to reveal valuable information about the mode of binding in addition to direct binding. Furthermore, the quality of ChIP-exo reads have important consequences on the output of data analysis.

IRF5 regulates unique subset of genes in dendritic cells during West Nile virus infection

Pathogen recognition receptor (PRR) signaling is critical for triggering innate immune activation and the expression of immune response genes, including genes that impart restriction against virus replication. RIG-I-like receptors and TLRs are PRRs that signal immune activation and drive the expression of antiviral genes and the production of type I IFN leading to induction of IFN-stimulated genes, in part through the interferon regulatory factor (IRF) family of transcription factors. Previous studies with West Nile virus (WNV) showed that IRF3 and IRF7 regulate IFN expression in fibroblasts and neurons, whereas macrophages and dendritic cells (DCs) retained the ability to induce IFN-β in the absence of IRF3 and IRF7 in a manner implicating IRF5 in PRR signaling actions. Here we assessed the contribution of IRF5 to immune gene induction in response to WNV infection in DCs. We examined IRF5-dependent gene expression and found that loss of IRF5 in mice resulted in modest and subtle changes in the expression of WNV-regulated genes. Anti-IRF5 chromatin immunoprecipitation with next-generation sequencing of genomic DNA coupled with mRNA analysis revealed unique IRF5 binding motifs within the mouse genome that are distinct from the canonical IRF binding motif and that link with IRF5-target gene expression. Using integrative bioinformatics analyses, we identified new IRF5 primary target genes in DCs in response to virus infection. This study provides novel insights into the distinct and unique innate immune and immune gene regulatory program directed by IRF5.