Peter Ebert

DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation

Summary Hematopoietic stem cells give rise to all blood cells in a differentiation process that involves widespread epigenome remodeling. Here we present genome-wide reference maps of the associated DNA methylation dynamics. We used a meta-epigenomic approach that combines DNA methylation profiles across many small pools of cells and performed single-cell methylome sequencing to assess cell-to-cell heterogeneity. The resulting dataset identified characteristic differences between HSCs derived from fetal liver, cord blood, bone marrow, and peripheral blood. We also observed lineage-specific DNA methylation between myeloid and lymphoid progenitors, characterized immature multi-lymphoid progenitors, and detected progressive DNA methylation differences in maturing megakaryocytes. We linked these patterns to gene expression, histone modifications, and chromatin accessibility, and we used machine learning to derive a model of human hematopoietic differentiation directly from DNA methylation data. Our results contribute to a better understanding of human hematopoietic stem cell differentiation and provide a framework for studying blood-linked diseases.

Ten Simple Rules for Developing Usable Software in Computational Biology

The rise of high-throughput technologies in molecular biology has led to a massive amount of publicly available data. While computational method development has been a cornerstone of biomedical research for decades, the rapid technological progress in the wet lab makes it difficult for software development to keep pace. Wet lab scientists rely heavily on computational methods, especially since more research is now performed in silico. However, suitable tools do not always exist, and not everyone has the skills to write complex software. Computational biologists are required to close this gap, but they often lack formal training in software engineering. To alleviate this, several related challenges have been previously addressed in the Ten Simple Rules series, including reproducibility [1], effectiveness [2], and open-source development of software [3, 4]. Here, we want to shed light on issues concerning software usability. Usability is commonly defined as “a measure of interface quality that refers to the effectiveness, efficiency, and satisfaction with which users can perform tasks with a tool” [5]. Considering the subjective nature of this topic, a broad consensus may be hard to achieve. Nevertheless, good usability is imperative for achieving wide acceptance of a software tool in the community. In many cases, academic software starts out as a prototype that solves one specific task and is not geared for a larger user group. As soon as the developer realizes that the complexity of the problems solved by the software could make it widely applicable, the software will grow to meet the new demands. At least by this point, if not sooner, usability should become a priority. Unfortunately, efforts in scientific software development are constrained by limited funding, time, and rapid turnover of group members. As a result, scientific software is often poorly documented, non-intuitive, non-robust with regards to input data and parameters, and hard to install. For many use cases, there is a plethora of tools that appear very similar and make it difficult for the user to select the one that best fits their needs. Not surprisingly, a substantial fraction of these tools are probably abandonware; i.e., these are no longer actively developed or supported in spite of their potential value to the scientific community. To our knowledge, software development as part of scientific research is usually carried out by individuals or small teams with no more than two or three members. Hence, the responsibility of designing, implementing, testing, and documenting the code rests on few shoulders. Additionally, there is pressure to produce publishable results or, at least, to contribute analysis work to ongoing projects. Consequently, academic software is typically released as a prototype. We acknowledge that such a tool cannot adhere to and should not be judged by the standards that we take for granted for production grade software. However, widespread use of a tool is typically in the interest of a researcher. To this end, we propose ten simple rules that, in our experience, have a considerable impact on improving usability of scientific software.

Epigenetic dynamics of monocyte-to-macrophage differentiation

BackgroundMonocyte-to-macrophage differentiation involves major biochemical and structural changes. In order to elucidate the role of gene regulatory changes during this process, we used high-throughput sequencing to analyze the complete transcriptome and epigenome of human monocytes that were differentiated in vitro by addition of colony-stimulating factor 1 in serum-free medium.ResultsNumerous mRNAs and miRNAs were significantly up- or down-regulated. More than 100 discrete DNA regions, most often far away from transcription start sites, were rapidly demethylated by the ten eleven translocation enzymes, became nucleosome-free and gained histone marks indicative of active enhancers. These regions were unique for macrophages and associated with genes involved in the regulation of the actin cytoskeleton, phagocytosis and innate immune response.ConclusionsIn summary, we have discovered a phagocytic gene network that is repressed by DNA methylation in monocytes and rapidly de-repressed after the onset of macrophage differentiation.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

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.

BiQ Analyzer HiMod: An Interactive Software Tool for High-throughput Locus-specific Analysis of 5-Methylcytosine and its Oxidized Derivatives

Recent data suggest important biological roles for oxidative modifications of methylated cytosines, specifically hydroxymethylation, formylation and carboxylation. Several assays are now available for profiling these DNA modifications genome-wide as well as in targeted, locus-specific settings. Here we present BiQ Analyzer HiMod, a user-friendly software tool for sequence alignment, quality control and initial analysis of locus-specific DNA modification data. The software supports four different assay types, and it leads the user from raw sequence reads to DNA modification statistics and publication-quality plots. BiQ Analyzer HiMod combines well-established graphical user interface of its predecessor tool, BiQ Analyzer HT, with new and extended analysis modes. BiQ Analyzer HiMod also includes updates of the analysis workspace, an intuitive interface, a custom vector graphics engine and support of additional input and output data formats. The tool is freely available as a stand-alone installation package from http://biq-analyzer-himod.bioinf.mpi-inf.mpg.de/.

A general concept for consistent documentation of computational analyses

The ever-growing amount of data in the field of life sciences demands standardized ways of high-throughput computational analysis. This standardization requires a thorough documentation of each step in the computational analysis to enable researchers to understand and reproduce the results. However, due to the heterogeneity in software setups and the high rate of change during tool development, reproducibility is hard to achieve. One reason is that there is no common agreement in the research community on how to document computational studies. In many cases, simple flat files or other unstructured text documents are provided by researchers as documentation, which are often missing software dependencies, versions and sufficient documentation to understand the workflow and parameter settings. As a solution we suggest a simple and modest approach for documenting and verifying computational analysis pipelines. We propose a two-part scheme that defines a computational analysis using a Process and an Analysis metadata document, which jointly describe all necessary details to reproduce the results. In this design we separate the metadata specifying the process from the metadata describing an actual analysis run, thereby reducing the effort of manual documentation to an absolute minimum. Our approach is independent of a specific software environment, results in human readable XML documents that can easily be shared with other researchers and allows an automated validation to ensure consistency of the metadata. Because our approach has been designed with little to no assumptions concerning the workflow of an analysis, we expect it to be applicable in a wide range of computational research fields. Database URL: http://deep.mpi-inf.mpg.de/DAC/cmds/pub/pyvalid.zip

Improving reference epigenome catalogs by computational prediction

Bioinformatic imputation of epigenomic marks promises to supplement catalogs of experimental data.

Temporal epigenomic profiling identifies AHR as dynamic super-enhancer controlled regulator of mesenchymal multipotency

Temporal data on gene expression and context-specific open chromatin states can improve identification of key transcription factors (TFs) and the gene regulatory networks (GRNs) controlling cellular differentiation. However, their integration remains challenging. Here, we delineate a general approach for data-driven and unbiased identification of key TFs and dynamic GRNs, called EPIC-DREM. We generated time-series transcriptomic and epigenomic profiles during differentiation of mouse multipotent bone marrow stromal cells (MSCs) towards adipocytes and osteoblasts. Using our novel approach we constructed time-resolved GRNs for both lineages. To prioritize the identified shared regulators, we mapped dynamic super-enhancers in both lineages and associated them to target genes with correlated expression profiles. We identified aryl hydrocarbon receptor (AHR) as a mesenchymal key TF controlled by a dynamic cluster of MSC-specific super-enhancers that become repressed in both lineages. AHR represses differentiation-induced genes such as Notch3 and we propose AHR to function as a guardian of mesenchymal multipotency.

Integrative analysis of single cell expression data reveals distinct regulatory states in bidirectional promoters

Background Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single cell technologies are not yet established, in the context of BPs. Results We performed integrative analyses of novel human single cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles. Conclusions Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

Fast Detection of Differential Chromatin Domains with SCIDDO

The generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing (ChIP-seq) is a common approach to dissect the complexity of the epigenome. However, interpretation and differential analysis of histone ChIP-seq datasets remains challenging due to the genomic co-occurrence of several marks and their difference in genomic spread. Here we present SCIDDO, a fast statistical method for the detection of differential chromatin domains (DCDs) from chromatin state maps. DCD detection simplifies relevant tasks such as the characterization of chromatin changes in differentially expressed genes or the examination of chromatin dynamics at regulatory elements. SCIDDO is available at github.com/ptrebert/sciddo