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We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

In recent years, gene fusions have gained significant recognition as biomarkers. They can assist treatment decisions, are seldom found in normal tissue and are detectable through Next-generation sequencing (NGS) of the transcriptome (RNA-seq). To transform the data provided by the sequencer into robust gene fusion detection several analysis steps are needed. Usually the first step is to map the sequenced transcript fragments (RNA-seq) to a reference genome. One standard application of this approach is to estimate expression and detect variants within known genes, e.g. SNPs and indels. In case of gene fusions, however, completely novel gene structures have to be detected. Here, we describe the detection of such gene fusion events based on our comprehensive transcript annotation (ElDorado). To demonstrate the utility of our approach, we extract gene fusion candidates from eight breast cancer cell lines, which we compare to experimentally verified gene fusions. We discuss several gene fusion events, like BCAS3-BCAS4 that was only detected in the breast cancer cell line MCF7. As supporting evidence we show that gene fusions occur more frequently in copy number enriched regions (CNV analysis). In addition, we present the Transcriptome Viewer (TViewer) a tool that allows to interactively visualize gene fusions. Finally, we support detected gene fusions through literature mining based annotations and network analyses. In conclusion, we present a platform that allows detecting gene fusions and supporting them through literature knowledge as well as rich visualization capabilities. This enables scientists to better understand molecular processes, biological functions and disease associations, which will ultimately lead to better biomedical knowledge for the development of biomarkers for diagnostics and therapies.

We carried out a genome-wide prediction of scaffold/matrix attachment regions (S/MARs) in Arabidopsis. Results indicate no uneven distribution on the chromosomal level but a clear underrepresentation of S/MARs inside genes. In cases where S/MARs were predicted within genes, these intragenic S/MARs were preferentially located within the 5′-half, most prominently within introns 1 and 2. Using Arabidopsis whole-genome expression data generated by the massively parallel signature sequencing methodology, we found a negative correlation between S/MAR-containing genes and transcriptional abundance. Expressed sequence tag data correlated the same way with S/MAR-containing genes. Thus, intragenic S/MARs show a negative correlation with transcription level. For various genes it has been shown experimentally that S/MARs can function as transcriptional regulators and that they have an implication in stabilizing expression levels within transgenic plants. On the basis of a genome-wide in silico S/MAR analysis, we found a significant correlation between the presence of intragenic S/MARs and transcriptional down-regulation.

Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles due to differences in transcription time, degradation rate and RNA processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. We introduce a joint model of transcriptional activation and mRNA accumulation which can be used for inference of transcription rate, RNA production delay and degradation rate given genome-wide data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a non-parametric statistical modelling approach allowing us to capture a broad range of activation kinetics, and use Bayesian parameter estimation to quantify the uncertainty in the estimates of the kinetic parameters. We apply the model to data from estrogen receptor (ER-{\alpha}) activation in the MCF-7 breast cancer cell line. We use RNA polymerase II (pol-II) ChIP-Seq time course data to characterise transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 minutes between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes., Comment: 42 pages, 17 figures

Motivation: Promoter analysis is an essential step on the way to identify regulatory networks. A prerequisite for successful promoter analysis is the prediction of potential transcription factor binding sites (TFBS) with reasonable accuracy. The next steps in promoter analysis can be tackled only with reliable predictions, e.g. finding phylogenetically conserved patterns or identifying higher order combinations of sites in promoters of co-regulated genes. Results: We present a new version of the program MatInspector that identifies TFBS in nucleotide sequences using a large library of weight matrices. By introducing a matrix family concept, optimized thresholds, and comparative analysis, the enhanced program produces concise results avoiding redundant and false-positive matches. We describe a number of programs based on MatInspector allowing in-depth promoter analysis (DiAlignTF, FrameWorker) and targeted design of regulatory sequences (SequenceShaper). Availability: MatInspector and the other programs described here can be used online at http://www.genomatix.de/matinspector.html. Access is free after registration within certain limitations (e.g. the number of analysis per month is currently limited to 20 analyses of arbitrary sequences). Contact: cartharius@genomatix.de Supplementary information: http://www.genomatix.de/matinspector.html

The publication of the first almost complete sequence of a human chromosome (chromosome 22) is a major milestone in human genomics. Together with the sequence, an excellent annotation of genes was published which certainly will serve as an information resource for numerous future projects. We noted that the annotation did not cover regulatory regions; in particular, no promoter annotation has been provided. Here we present an analysis of the complete published chromosome 22 sequence for promoters. A recent breakthrough in specific in silico prediction of promoter regions enabled us to attempt large-scale prediction of promoter regions on chromosome 22. Scanning of sequence databases revealed only 20 experimentally verified promoters, of which 10 were correctly predicted by our approach. Nearly 40% of our 465 predicted promoter regions are supported by the currently available gene annotation. Promoter finding also provides a biologically meaningful method for “chromosomal scaffolding”, by which long genomic sequences can be divided into segments starting with a gene. As one example, the combination of promoter region prediction with exon/intron structure predictions greatly enhances the specificity of de novo gene finding. The present study demonstrates that it is possible to identify promoters in silico on the chromosomal level with sufficient reliability for experimental planning and indicates that a wealth of information about regulatory regions can be extracted from current large-scale (megabase) sequencing projects. Results are available on-line at http://genomatix.gsf.de/chr22/.

Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor (), H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 early responsive genes and compared the biological functions of the genes having binding within 20 kb of the transcription start site (TSS) to genes without such binding site. Our results show that the non-genomic action of via the MAPK pathway, instead of direct binding, may be responsible for early cell responses to activation. Our results also indicate that the responsive genes triggered by the genomic pathway are transcribed faster than those without binding sites. The survival analysis of the 777 responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have binding site within 20 kb of its TSS, to be significantly associated with poor patient survival., Author Summary Cellular processes in mammalian cells are tightly regulated to ensure that the cells function properly as a part of an organism. Dysregulation of some of these processes, such as apoptosis, cell proliferation and growth, can lead to cancer. One of the most important regulation mechanisms for cellular processes is via activation of membrane receptors by extra-cellular stimulus. Such cues trigger signal cascades that lead to altered expression of a number of genes in the cell nucleus; a key challenge in biomedicine is to identify which genes respond to a specific stimulus. These so called response genes can be investigated on a whole-genome scale with genomic sequencing, which is a technology that can quantify protein binding to DNA or gene activation. Analysis of such whole-genome data, however, is challenging due to billions of data points measured in the experiments. Here we introduce a novel computational method, SPINLONG, which is a widely applicable novel computational method that integrates multiple levels of deep sequencing data to produce experimentally testable hypotheses. We applied SPINLONG to breast cancer data and found early responsive genes for estrogen receptor and analyzed their regulation. These analyses resulted in a gene whose high activity is associated with decreased breast cancer patient survival.