MOTIVATION: Generating publication ready plots to display multiple genomic tracks can pose a serious challenge. Making desirable and accurate figures requires considerable effort. This is usually done by hand or using a vector graphic software. RESULTS: pyGenomeTracks (PGT) is a modular plotting tool that easily combines multiple tracks. It enables a reproducible and standardized generation of highly customizable and publication ready images. AVAILABILITY AND IMPLEMENTATION: PGT is available through a graphical interface on https://usegalaxy.eu and through the command line. It is provided on conda via the bioconda channel, on pip and it is openly developed on github: https://github.com/deeptools/pyGenomeTracks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: This paper presents Parkour, a software package for sample processing and quality management of next generation sequencing data and samples. RESULTS: Starting with user requests, Parkour allows tracking and assessing samples based on predefined quality criteria through different stages of the sample preparation workflow. Ideally suited for academic core laboratories, the software aims to maximize efficiency and reduce turnaround time by intelligent sample grouping and a clear assignment of staff to work units. Tools for automated invoicing, interactive statistics on facility usage and simple report generation minimize administrative tasks. Provided as a web application, Parkour is a convenient tool for both deep sequencing service users and laboratory personal. A set of web APIs allow coordinated information sharing with local and remote bioinformaticians. The flexible structure allows workflow customization and simple addition of new features as well as the expansion to other domains. AVAILABILITY AND IMPLEMENTATION: The code and documentation are available at https://github.com/maxplanck-ie/parkour. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Due to the rapidly increasing scale and diversity of epigenomic data, modular and scalable analysis workflows are of wide interest. Here we present snakePipes, a workflow package for processing and downstream analysis of data from common epigenomic assays: ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq. snakePipes enables users to assemble variants of each workflow and to easily install and upgrade the underlying tools, via its simple command-line wrappers and yaml files. AVAILABILITY AND IMPLEMENTATION: snakePipes can be installed via conda: `conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'. Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Genome-wide association studies and re-sequencing projects are revealing an increasing number of disease-associated SNPs, a large fraction of which are non-coding. Although they could have relevance for disease susceptibility and progression, the lack of information about regulatory regions impedes the assessment of their functionality. Here we present a web server, ChroMoS (Chromatin Modified SNPs), which combines genetic and epigenetic data with the goal of facilitating SNPs' classification, prioritization and prediction of their functional consequences. ChroMoS uses a large database of SNPs and chromatin states, but allows a user to provide his/her own genetic information. Based on the SNP classification and interactive prioritization, a user can compute the functional impact of multiple SNPs using two prediction tools, one for differential analysis of transcription factor binding (sTRAP) and another for SNPs with potential impact on binding of miRNAs (MicroSNiPer). AVAILABILITY: Web server, ChroMoS, is freely available at http://epicenter.immunbio.mpg.de/services/chromos.

MOTIVATION: A major challenge in regulatory genomics is the identification of associations between functional categories of genes (e.g. tissues, metabolic pathways) and their regulating transcription factors (TFs). While, for a limited number of categories, the regulating TFs are already known, still for many functional categories the responsible factors remain to be elucidated. RESULTS: We put forward a novel method (PASTAA) for detecting transcriptions factors associated with functional categories, which utilizes the prediction of binding affinities of a TF to promoters. This binding strength information is compared to the likelihood of membership of the corresponding genes in the functional category under study. Coherence between the two ranked datasets is seen as an indicator of association between a TF and the category. PASTAA is applied primarily to the determination of TFs driving tissue-specific expression. We show that PASTAA is capable of recovering many TFs acting tissue specifically and, in addition, provides novel associations so far not detected by alternative methods. The application of PASTAA to detect TFs involved in the regulation of tissue-specific gene expression revealed a remarkable number of experimentally supported associations. The validated success for various datasets implies that PASTAA can directly be applied for the detection of TFs associated with newly derived gene sets. AVAILABILITY: The PASTAA source code as well as a corresponding web interface is freely available at http://trap.molgen.mpg.de.

BACKGROUND: The identification of groups of co-regulated genes and their transcription factors, called transcriptional modules, has been a focus of many studies about biological systems. While methods have been developed to derive numerous modules from genome-wide data, individual links between regulatory proteins and target genes still need experimental verification. In this work, we aim to prioritize regulator-target links within transcriptional modules based on three types of large-scale data sources. RESULTS: Starting with putative transcriptional modules from ChIP-chip data, we first derive modules in which target genes show both expression and function coherence. The most reliable regulatory links between transcription factors and target genes are established by identifying intersection of target genes in coherent modules for each enriched functional category. Using a combination of genome-wide yeast data in normal growth conditions and two different reference datasets, we show that our method predicts regulatory interactions with significantly higher predictive power than ChIP-chip binding data alone. A comparison with results from other studies highlights that our approach provides a reliable and complementary set of regulatory interactions. Based on our results, we can also identify functionally interacting target genes, for instance, a group of co-regulated proteins related to cell wall synthesis. Furthermore, we report novel conserved binding sites of a glycoprotein-encoding gene, CIS3, regulated by Swi6-Swi4 and Ndd1-Fkh2-Mcm1 complexes. CONCLUSION: We provide a simple method to prioritize individual TF-gene interactions from large-scale transcriptional modules. In comparison with other published works, we predict a complementary set of regulatory interactions which yields a similar or higher prediction accuracy at the expense of sensitivity. Therefore, our method can serve as an alternative approach to prioritization for further experimental studies.

Recent experimental and theoretical efforts have highlighted the fact that binding of transcription factors to DNA can be more accurately described by continuous measures of their binding affinities, rather than a discrete description in terms of binding sites. While the binding affinities can be predicted from a physical model, it is often desirable to know the distribution of binding affinities for specific sequence backgrounds. In this paper, we present a statistical approach to derive the exact distribution for sequence models with fixed GC content. We demonstrate that the affinity distribution of almost all known transcription factors can be effectively parametrized by a class of generalized extreme value distributions. Moreover, this parameterization also describes the affinity distribution for sequence backgrounds with variable GC content, such as human promoter sequences. Our approach is applicable to arbitrary sequences and all transcription factors with known binding preferences that can be described in terms of a motif matrix. The statistical treatment also provides a proper framework to directly compare transcription factors with very different affinity distributions. This is illustrated by our analysis of human promoters with known binding sites, for many of which we could identify the known regulators as those with the highest affinity. The combination of physical model and statistical normalization provides a quantitative measure which ranks transcription factors for a given sequence, and which can be compared directly with large-scale binding data. Its successful application to human promoter sequences serves as an encouraging example of how the method can be applied to other sequences.

MOTIVATION: Theoretical efforts to understand the regulation of gene expression are traditionally centered around the identification of transcription factor binding sites at specific DNA positions. More recently these efforts have been supplemented by experimental data for relative binding affinities of proteins to longer intergenic sequences. The question arises to what extent these two approaches converge. In this paper, we adopt a physical binding model to predict the relative binding affinity of a transcription factor for a given sequence. RESULTS: We find that a significant fraction of genome-wide binding data in yeast can be accounted for by simple count matrices and a physical model with only two parameters. We demonstrate that our approach is both conceptually and practically more powerful than traditional methods, which require selection of a cutoff. Our analysis yields biologically meaningful parameters, suitable for predicting relative binding affinities in the absence of experimental binding data. AVAILABILITY: The C source code for our TRAP program is freely available for non-commercial use at http://www.molgen.mpg.de/~manke/papers/TFaffinities/