i-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules

Abstract

The field of regulatory genomics today is characterized by the generation of high-throughput data sets that capture genome-wide transcription factor (TF) binding, histone modifications, or DNAseI hypersensitive regions across many cell types and conditions. In this context, a critical question is how to make optimal use of these publicly available datasets when studying transcriptional regulation. Here, we address this question in Drosophila melanogaster for which a large number of high-throughput regulatory datasets are available. We developed i-cisTarget (where the ‘i’ stands for integrative), for the first time enabling the discovery of different types of enriched ‘regulatory features’ in a set of co-regulated sequences in one analysis, being either TF motifs or ‘in vivo’ chromatin features, or combinations thereof. We have validated our approach on 15 co-expressed gene sets, 21 ChIP data sets, 628 curated gene sets and multiple individual case studies, and show that meaningful regulatory features can be confidently discovered; that bona fide enhancers can be identified, both by in vivo events and by TF motifs; and that combinations of in vivo events and TF motifs further increase the performance of enhancer prediction.

p53 induces formation of NEAT1 lncRNA-containing paraspeckles that modulate replication stress response and chemosensitivity

“…We identified 110 and 147 annotated lncRNAs that were downregulated and upregulated, respectively, in cells treated with Nutlin-3a (|log 2 fold change (FC)| > 1, adjusted P < 0.05). To determine which of these transcripts are bona fide p53 targets, we searched for p53-binding sites within 10 kb upstream and downstream of their transcription start sites (TSS) by adapting the i-cisTarget method 15 . The p53-responsive element was the most significantly enriched motif within the upregulated genes, whereas it was undetectable among the downregulated loci.…”

“…To detect putative p53 binding sites we adapted the i-cisTarget method 15 . In short, a region of 10 kb flanking the TSS of each lncRNA (GENCODE annotation V18) was scored for 6,863 different position weight matrices (PWM), representing a large collection of transcription factors.…”

Comparative motif discovery combined with comparative transcriptomics yields accurate targetome and enhancer predictions

“…kuleuven.be/lcb/cisTargetX) as described before (Aerts et al 2010;Herrmann et al 2012) using version 1 of the motif collection (3731 position weight matrices), and using the 5 kb upstream and first intron as search space. For each motif, this search space is scored for clusters of PWM matches using a Hidden Markov Model, and orthologous regions of 11 other Drosophila species are scored in parallel.…”

TargetOrtho: A Phylogenetic Footprinting Tool to Identify Transcription Factor Targets

“…This more relaxed definition of conservation accounts for the inherent degeneracy and orientation independence of TFBS so that variant nucleotides within a motif do not prevent conservation calls between species. Such strategies for target gene prediction have been implemented for specific TF regulatory target gene discovery (Aerts et al 2006;Ward and Bussemaker 2008;Herrmann et al 2012), but these approaches have not been applied to the automated prediction of TF regulatory target genes from user-defined PWMs together with a target gene ranking system that accounts for the degree of motif match conservation, quality, and frequency for target gene prediction. For an overview of target gene prediction strategies, see Aerts et al 2012.…”