No abstract

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

The binding and contribution of transcription factors (TF) to cell specific gene Using machine learning, we find low affinity binding sites to improve our ability to 10 explain gene expression variability compared to the standard presence/absence 11 classification of binding sites. Further, we show that both footprints and peaks capture 12 essential TF binding events and lead to a good prediction performance. In our nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly 15

Phenotypic variation of a single genotype is achieved by alterations in gene expression patterns. Regulation of such alterations depends on their time scale, where short-time adaptations differ from permanently established gene expression patterns maintained by epigenetic mechanisms. In the ciliate Paramecium, serotypes were described for an epigenetically controlled gene expression pattern of an individual multigene family. Paradoxically, individual serotypes can be triggered in Paramecium by alternating environments but are then stabilized by epigenetic mechanisms, thus raising the question to which extend their expression follows environmental stimuli. To characterize environmental adaptation in the context of epigenetically controlled serotype expression, we used RNA-seq to characterize transcriptomes of serotype pure cultures. The resulting vegetative transcriptome resource is first analysed for genes involved in the adaptive response to the altered environment. Secondly, we identified groups of genes that do not follow the adaptive response but show co-regulation with the epigenetically controlled serotype system, suggesting that their gene expression pattern becomes manifested by similar mechanisms. In our experimental set-up, serotype expression and the entire group of co-regulated genes were stable among environmental changes and only heat-shock genes altered expression of these gene groups. The data suggest that the maintenance of these gene expression patterns in a lineage represents epigenetically controlled robustness counteracting short-time adaptation processes.