The IPB has once again been recognized for its exemplary actions in terms of equal opportunity-oriented personnel and organizational policies and has received the TOTAL E-QUALITY certification for the…
The Plant Science Student Conference (PSSC) has been organised by students from the two Leibniz institutes, IPK and IPB, every year for the last 20 years. In this interview, Christina Wäsch (IPK) and…
Nettling, M.; Treutler, H.; Cerquides, J.; Grosse, I.;Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species informationBMC Genomics17347(2016)DOI: 10.1186/s12864-016-2682-6
BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. ChIP-seq has become the major technology to uncover genomic regions containing those binding sites, but motifs predicted by traditional computational approaches using these data are distorted by a ubiquitous binding-affinity bias. Here, we present an approach for detecting and correcting this bias using inter-species information.ResultsWe find that the binding-affinity bias caused by the ChIP-seq experiment in the reference species is stronger than the indirect binding-affinity bias in orthologous regions from phylogenetically related species. We use this difference to develop a phylogenetic footprinting model that is capable of detecting and correcting the binding-affinity bias. We find that this model improves motif prediction and that the corrected motifs are typically softer than those predicted by traditional approaches.ConclusionsThese findings indicate that motifs published in databases and in the literature are artificially sharpened compared to the native motifs. These findings also indicate that our current understanding of transcriptional gene regulation might be blurred, but that it is possible to advance this understanding by taking into account inter-species information available today and even more in the future.
Publications
Ryan, P. T.; Ó’Maoiléidigh, D. S.; Drost, H.-G.; Kwaśniewska, K.; Gabel, A.; Grosse, I.; Graciet, E.; Quint, M.; Wellmer, F.;Patterns of gene expression during Arabidopsis flower development from the time of initiation to maturationBMC Genomics16488(2015)DOI: 10.1186/s12864-015-1699-6
BackgroundThe formation of flowers is one of the main model systems to elucidate the molecular mechanisms that control developmental processes in plants. Although several studies have explored gene expression during flower development in the model plant Arabidopsis thaliana on a genome-wide scale, a continuous series of expression data from the earliest floral stages until maturation has been lacking. Here, we used a floral induction system to close this information gap and to generate a reference dataset for stage-specific gene expression during flower formation.ResultsUsing a floral induction system, we collected floral buds at 14 different stages from the time of initiation until maturation. Using whole-genome microarray analysis, we identified 7,405 genes that exhibit rapid expression changes during flower development. These genes comprise many known floral regulators and we found that the expression profiles for these regulators match their known expression patterns, thus validating the dataset. We analyzed groups of co-expressed genes for over-represented cellular and developmental functions through Gene Ontology analysis and found that they could be assigned specific patterns of activities, which are in agreement with the progression of flower development. Furthermore, by mapping binding sites of floral organ identity factors onto our dataset, we were able to identify gene groups that are likely predominantly under control of these transcriptional regulators. We further found that the distribution of paralogs among groups of co-expressed genes varies considerably, with genes expressed predominantly at early and intermediate stages of flower development showing the highest proportion of such genes.ConclusionsOur results highlight and describe the dynamic expression changes undergone by a large number of genes during flower development. They further provide a comprehensive reference dataset for temporal gene expression during flower formation and we demonstrate that it can be used to integrate data from other genomics approaches such as genome-wide localization studies of transcription factor binding sites.