Also visit our Google Scholar page for a full list of publications.

2020

  • Trevino*, A. E., Müller*, F., Andersen*, J., Sundaram*, L., Kathiria, A., Shcherbina, A., Farh, K., Chang, H. Y., Pasca, A. M., Kundaje, A., Pasca, S. P., & Greenleaf, W. J.. (2020). Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Biorxiv. doi:10.1101/2020.12.29.424636
    [BibTeX] [Abstract]

    {Genetic perturbations of cerebral cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory into glutamatergic neurons, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Basepair-resolution neural network models identified strong cell-type specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD subjects and identified frequently disrupted TF binding sites. This approach illustrates how cell-type specific mapping can provide insights into the programs governing human development and disease.}

    @article{Trevino:2020,
    title = {{Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution}},
    author = {Trevino*, Alexandro E and Müller*, Fabian and Andersen*, Jimena and Sundaram*, Laksshman and Kathiria, Arwa and Shcherbina, Anna and Farh, Kyle and Chang, Howard Y and Pasca, Anca M and Kundaje, Anshul and Pasca, Sergiu P and Greenleaf, William J},
    journal = {bioRxiv},
    doi = {10.1101/2020.12.29.424636},
    abstract = {{Genetic perturbations of cerebral cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory into glutamatergic neurons, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Basepair-resolution neural network models identified strong cell-type specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD subjects and identified frequently disrupted TF binding sites. This approach illustrates how cell-type specific mapping can provide insights into the programs governing human development and disease.}},
    year = {2020}
    }

  • Scherer, M., Nebel, A., Franke, A., Walter, J., Lengauer, T., Bock, C., Müller, F., & List, M.. (2020). Quantitative comparison of within-sample heterogeneity scores for DNA methylation data. Nucleic acids research. doi:10.1093/nar/gkaa120
    [BibTeX] [Abstract]

    {DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows.}

    @article{Scherer:2020,
    title = {{Quantitative comparison of within-sample heterogeneity scores for DNA methylation data}},
    author = {Scherer, Michael and Nebel, Almut and Franke, Andre and Walter, Jörn and Lengauer, Thomas and Bock, Christoph and Müller, Fabian and List, Markus},
    journal = {Nucleic Acids Research},
    issn = {0305-1048},
    doi = {10.1093/nar/gkaa120},
    abstract = {{DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows.}},
    year = {2020}
    }

2019

  • Nordström, K. J. V., Schmidt, F., Gasparoni, N., Salhab, A., Gasparoni, G., Kattler, K., Müller, F., Ebert, P., Costa, I. G., consortium , D., Pfeifer, N., Lengauer, T., Schulz, M. H., & Walter, J.. (2019). Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data. Nucleic acids research, 47(20), 10580–10596. doi:10.1093/nar/gkz799
    [BibTeX] [Abstract]

    {Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-seq, ATAC-seq and NOMe-seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However, we also find a high proportion of assay specific NDRs that are often ‘called’ by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-seq and DNase I-seq provide a superb high NDR calling rate for relatively low sequencing costs in comparison to NOMe-seq, NOMe-seq singles out for its genome-wide coverage allowing to not only detect NDRs but also endogenous DNA methylation and as we show here genome wide segmentation into heterochromatic B domains and local phasing of nucleosomes outside of NDRs. In summary, our comparisons strongly suggest to consider assay specific differences for the experimental design and for generalized and comparative functional interpretations.}

    @article{Nordström:2019,
    title = {{Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data}},
    author = {Nordström, Karl J V and Schmidt, Florian and Gasparoni, Nina and Salhab, Abdulrahman and Gasparoni, Gilles and Kattler, Kathrin and Müller, Fabian and Ebert, Peter and Costa, Ivan G and consortium, DEEP and Pfeifer, Nico and Lengauer, Thomas and Schulz, Marcel H and Walter, Jörn},
    journal = {Nucleic Acids Research},
    issn = {0305-1048},
    doi = {10.1093/nar/gkz799},
    pmid = {31584093},
    abstract = {{Chromatin accessibility maps are important for the functional interpretation of the genome. Here, we systematically analysed assay specific differences between DNase I-seq, ATAC-seq and NOMe-seq in a side by side experimental and bioinformatic setup. We observe that most prominent nucleosome depleted regions (NDRs, e.g. in promoters) are roboustly called by all three or at least two assays. However, we also find a high proportion of assay specific NDRs that are often ‘called’ by only one of the assays. We show evidence that these assay specific NDRs are indeed genuine open chromatin sites and contribute important information for accurate gene expression prediction. While technically ATAC-seq and DNase I-seq provide a superb high NDR calling rate for relatively low sequencing costs in comparison to NOMe-seq, NOMe-seq singles out for its genome-wide coverage allowing to not only detect NDRs but also endogenous DNA methylation and as we show here genome wide segmentation into heterochromatic B domains and local phasing of nucleosomes outside of NDRs. In summary, our comparisons strongly suggest to consider assay specific differences for the experimental design and for generalized and comparative functional interpretations.}},
    pages = {10580--10596},
    number = {20},
    volume = {47},
    language = {English},
    year = {2019},
    rating = {0}
    }

  • Calderon, D., Nguyen, M. L. T., Mezger, A., Kathiria, A., Müller, F., Nguyen, V., Lescano, N., Wu, B., Trombetta, J., Ribado, J. V., Knowles, D. A., Gao, Z., Blaeschke, F., Parent, A. V., Burt, T. D., Anderson, M. S., Criswell, L. A., Greenleaf, W. J., Marson, A., & Pritchard, J. K.. (2019). Landscape of stimulation-responsive chromatin across diverse human immune cells. Nature genetics, 51(10), 1494–1505. doi:10.1038/s41588-019-0505-9
    [BibTeX] [Abstract]

    {A hallmark of the immune system is the interplay among specialized cell types transitioning between resting and stimulated states. The gene regulatory landscape of this dynamic system has not been fully characterized in human cells. Here we collected assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA sequencing data under resting and stimulated conditions for up to 32 immune cell populations. Stimulation caused widespread chromatin remodeling, including response elements shared between stimulated B and T cells. Furthermore, several autoimmune traits showed significant heritability in stimulation-responsive elements from distinct cell types, highlighting the importance of these cell states in autoimmunity. Allele-specific read mapping identified variants that alter chromatin accessibility in particular conditions, allowing us to observe evidence of function for a candidate causal variant that is undetected by existing large-scale studies in resting cells. Our results provide a resource of chromatin dynamics and highlight the need to characterize the effects of genetic variation in stimulated cells. Analysis of gene expression and open chromatin regions in up to 32 immune cell populations under resting and stimulated conditions identifies widespread chromatin remodeling and shared response elements between stimulated B and T cells.}

    @article{Calderon:2019,
    title = {{Landscape of stimulation-responsive chromatin across diverse human immune cells}},
    author = {Calderon, Diego and Nguyen, Michelle L. T. and Mezger, Anja and Kathiria, Arwa and Müller, Fabian and Nguyen, Vinh and Lescano, Ninnia and Wu, Beijing and Trombetta, John and Ribado, Jessica V. and Knowles, David A. and Gao, Ziyue and Blaeschke, Franziska and Parent, Audrey V. and Burt, Trevor D. and Anderson, Mark S. and Criswell, Lindsey A. and Greenleaf, William J. and Marson, Alexander and Pritchard, Jonathan K.},
    journal = {Nature Genetics},
    issn = {1061-4036},
    doi = {10.1038/s41588-019-0505-9},
    pmid = {31570894},
    abstract = {{A hallmark of the immune system is the interplay among specialized cell types transitioning between resting and stimulated states. The gene regulatory landscape of this dynamic system has not been fully characterized in human cells. Here we collected assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA sequencing data under resting and stimulated conditions for up to 32 immune cell populations. Stimulation caused widespread chromatin remodeling, including response elements shared between stimulated B and T cells. Furthermore, several autoimmune traits showed significant heritability in stimulation-responsive elements from distinct cell types, highlighting the importance of these cell states in autoimmunity. Allele-specific read mapping identified variants that alter chromatin accessibility in particular conditions, allowing us to observe evidence of function for a candidate causal variant that is undetected by existing large-scale studies in resting cells. Our results provide a resource of chromatin dynamics and highlight the need to characterize the effects of genetic variation in stimulated cells. Analysis of gene expression and open chromatin regions in up to 32 immune cell populations under resting and stimulated conditions identifies widespread chromatin remodeling and shared response elements between stimulated B and T cells.}},
    pages = {1494--1505},
    number = {10},
    volume = {51},
    language = {English},
    year = {2019},
    rating = {0}
    }

  • Müller, F., Scherer, M., Assenov, Y., Lutsik, P., Walter, J., Lengauer, T., & Bock, C.. (2019). RnBeads 2.0: comprehensive analysis of DNA methylation data. Genome biology, 20(1), 55. doi:10.1186/s13059-019-1664-9
    [BibTeX] [Abstract]

    {DNA methylation is a widely investigated epigenetic mark with important roles in development and disease. High-throughput assays enable genome-scale DNA methylation analysis in large numbers of samples. Here, we describe a new version of our RnBeads software – an R/Bioconductor package that implements start-to-finish analysis workflows for Infinium microarrays and various types of bisulfite sequencing. RnBeads 2.0 ( https://rnbeads.org/ ) provides additional data types and analysis methods, new functionality for interpreting DNA methylation differences, improved usability with a novel graphical user interface, and better use of computational resources. We demonstrate RnBeads 2.0 in four re-runnable use cases focusing on cell differentiation and cancer.}

    @article{Müller:2019,
    title = {{RnBeads 2.0: comprehensive analysis of DNA methylation data}},
    author = {Müller, Fabian and Scherer, Michael and Assenov, Yassen and Lutsik, Pavlo and Walter, Jörn and Lengauer, Thomas and Bock, Christoph},
    journal = {Genome Biology},
    doi = {10.1186/s13059-019-1664-9},
    abstract = {{DNA methylation is a widely investigated epigenetic mark with important roles in development and disease. High-throughput assays enable genome-scale DNA methylation analysis in large numbers of samples. Here, we describe a new version of our RnBeads software - an R/Bioconductor package that implements start-to-finish analysis workflows for Infinium microarrays and various types of bisulfite sequencing. RnBeads 2.0 ( https://rnbeads.org/ ) provides additional data types and analysis methods, new functionality for interpreting DNA methylation differences, improved usability with a novel graphical user interface, and better use of computational resources. We demonstrate RnBeads 2.0 in four re-runnable use cases focusing on cell differentiation and cancer.}},
    pages = {55},
    number = {1},
    volume = {20},
    language = {English},
    year = {2019},
    rating = {0}
    }

2018

  • Salhab, A., Nordström, K., Gasparoni, G., Kattler, K., Ebert, P., Ramírez, F., Arrigoni, L., Müller, F., Polansky, J. K., Cadenas, C., Hengstler, J. G., Lengauer, T., Manke, T., Consortium, D., & Walter, J.. (2018). A comprehensive analysis of 195 DNA methylomes reveals shared and cell-specific features of partially methylated domains. Genome biology, 19(1), 150. doi:10.1186/s13059-018-1510-5
    [BibTeX] [Abstract]

    {BACKGROUND:Partially methylated domains are extended regions in the genome exhibiting a reduced average DNA methylation level. They cover gene-poor and transcriptionally inactive regions and tend to be heterochromatic. We present a comprehensive comparative analysis of partially methylated domains in human and mouse cells, to identify structural and functional features associated with them. RESULTS:Partially methylated domains are present in up to 75\% of the genome in human and mouse cells irrespective of their tissue or cell origin. Each cell type has a distinct set of partially methylated domains, and genes expressed in such domains show a strong cell type effect. The methylation level varies between cell types with a more pronounced effect in differentiating and replicating cells. The lowest level of methylation is observed in highly proliferating and immortal cancer cell lines. A decrease of DNA methylation within partially methylated domains tends to be linked to an increase in heterochromatic histone marks and a decrease of gene expression. Characteristic combinations of heterochromatic signatures in partially methylated domains are linked to domains of early and middle S-phase and late S-G2 phases of DNA replication. CONCLUSIONS:Partially methylated domains are prominent signatures of long-range epigenomic organization. Integrative analysis identifies them as important general, lineage- and cell type-specific topological features. Changes in partially methylated domains are hallmarks of cell differentiation, with decreased methylation levels and increased heterochromatic marks being linked to enhanced cell proliferation. In combination with broad histone marks, partially methylated domains demarcate distinct domains of late DNA replication.}

    @article{Salhab:2018,
    title = {{A comprehensive analysis of 195 DNA methylomes reveals shared and cell-specific features of partially methylated domains}},
    author = {Salhab, Abdulrahman and Nordström, Karl and Gasparoni, Gilles and Kattler, Kathrin and Ebert, Peter and Ramírez, Fidel and Arrigoni, Laura and Müller, Fabian and Polansky, Julia K and Cadenas, Cristina and Hengstler, Jan G and Lengauer, Thomas and Manke, Thomas and Consortium, DEEP and Walter, Jörn},
    journal = {Genome Biology},
    doi = {10.1186/s13059-018-1510-5},
    abstract = {{BACKGROUND:Partially methylated domains are extended regions in the genome exhibiting a reduced average DNA methylation level. They cover gene-poor and transcriptionally inactive regions and tend to be heterochromatic. We present a comprehensive comparative analysis of partially methylated domains in human and mouse cells, to identify structural and functional features associated with them.
    RESULTS:Partially methylated domains are present in up to 75\% of the genome in human and mouse cells irrespective of their tissue or cell origin. Each cell type has a distinct set of partially methylated domains, and genes expressed in such domains show a strong cell type effect. The methylation level varies between cell types with a more pronounced effect in differentiating and replicating cells. The lowest level of methylation is observed in highly proliferating and immortal cancer cell lines. A decrease of DNA methylation within partially methylated domains tends to be linked to an increase in heterochromatic histone marks and a decrease of gene expression. Characteristic combinations of heterochromatic signatures in partially methylated domains are linked to domains of early and middle S-phase and late S-G2 phases of DNA replication.
    CONCLUSIONS:Partially methylated domains are prominent signatures of long-range epigenomic organization. Integrative analysis identifies them as important general, lineage- and cell type-specific topological features. Changes in partially methylated domains are hallmarks of cell differentiation, with decreased methylation levels and increased heterochromatic marks being linked to enhanced cell proliferation. In combination with broad histone marks, partially methylated domains demarcate distinct domains of late DNA replication.}},
    pages = {150},
    number = {1},
    volume = {19},
    language = {English},
    year = {2018},
    rating = {0}
    }

  • Pan, W., Sommer, F., Falk-Paulsen, M., Ulas, T., Best, P., Fazio, A., Kachroo, P., Luzius, A., Jentzsch, M., Rehman, A., Müller, F., Lengauer, T., Walter, J., Künzel, S., Baines, J. F., Schreiber, S., Franke, A., Schultze, J. L., Bäckhed, F., & Rosenstiel, P.. (2018). Exposure to the gut microbiota drives distinct methylome and transcriptome changes in intestinal epithelial cells during postnatal development. Genome medicine, 10(1), 27. doi:10.1186/s13073-018-0534-5
    [BibTeX] [Abstract]

    {The interplay of epigenetic processes and the intestinal microbiota may play an important role in intestinal development and homeostasis. Previous studies have established that the microbiota regulates a large proportion of the intestinal epithelial transcriptome in the adult host, but microbial effects on DNA methylation and gene expression during early postnatal development are still poorly understood. Here, we sought to investigate the microbial effects on DNA methylation and the transcriptome of intestinal epithelial cells (IECs) during postnatal development. We collected IECs from the small intestine of each of five 1-, 4- and 12 to 16-week-old mice representing the infant, juvenile, and adult states, raised either in the presence or absence of a microbiota. The DNA methylation profile was determined using reduced representation bisulfite sequencing (RRBS) and the epithelial transcriptome by RNA sequencing using paired samples from each individual mouse to analyze the link between microbiota, gene expression, and DNA methylation. We found that microbiota-dependent and -independent processes act together to shape the postnatal development of the transcriptome and DNA methylation signatures of IECs. The bacterial effect on the transcriptome increased over time, whereas most microbiota-dependent DNA methylation differences were detected already early after birth. Microbiota-responsive transcripts could be attributed to stage-specific cellular programs during postnatal development and regulated gene sets involved primarily immune pathways and metabolic processes. Integrated analysis of the methylome and transcriptome data identified 126 genomic loci at which coupled differential DNA methylation and RNA transcription were associated with the presence of intestinal microbiota. We validated a subset of differentially expressed and methylated genes in an independent mouse cohort, indicating the existence of microbiota-dependent “functional” methylation sites which may impact on long-term gene expression signatures in IECs. Our study represents the first genome-wide analysis of microbiota-mediated effects on maturation of DNA methylation signatures and the transcriptional program of IECs after birth. It indicates that the gut microbiota dynamically modulates large portions of the epithelial transcriptome during postnatal development, but targets only a subset of microbially responsive genes through their DNA methylation status.}

    @article{Pan:2018,
    title = {{Exposure to the gut microbiota drives distinct methylome and transcriptome changes in intestinal epithelial cells during postnatal development}},
    author = {Pan, Wei-Hung and Sommer, Felix and Falk-Paulsen, Maren and Ulas, Thomas and Best, Philipp and Fazio, Antonella and Kachroo, Priyadarshini and Luzius, Anne and Jentzsch, Marlene and Rehman, Ateequr and Müller, Fabian and Lengauer, Thomas and Walter, Jörn and Künzel, Sven and Baines, John F and Schreiber, Stefan and Franke, Andre and Schultze, Joachim L and Bäckhed, Fredrik and Rosenstiel, Philip},
    journal = {Genome Medicine},
    doi = {10.1186/s13073-018-0534-5},
    pmid = {29653584},
    abstract = {{The interplay of epigenetic processes and the intestinal microbiota may play an important role in intestinal development and homeostasis. Previous studies have established that the microbiota regulates a large proportion of the intestinal epithelial transcriptome in the adult host, but microbial effects on DNA methylation and gene expression during early postnatal development are still poorly understood. Here, we sought to investigate the microbial effects on DNA methylation and the transcriptome of intestinal epithelial cells (IECs) during postnatal development. We collected IECs from the small intestine of each of five 1-, 4- and 12 to 16-week-old mice representing the infant, juvenile, and adult states, raised either in the presence or absence of a microbiota. The DNA methylation profile was determined using reduced representation bisulfite sequencing (RRBS) and the epithelial transcriptome by RNA sequencing using paired samples from each individual mouse to analyze the link between microbiota, gene expression, and DNA methylation. We found that microbiota-dependent and -independent processes act together to shape the postnatal development of the transcriptome and DNA methylation signatures of IECs. The bacterial effect on the transcriptome increased over time, whereas most microbiota-dependent DNA methylation differences were detected already early after birth. Microbiota-responsive transcripts could be attributed to stage-specific cellular programs during postnatal development and regulated gene sets involved primarily immune pathways and metabolic processes. Integrated analysis of the methylome and transcriptome data identified 126 genomic loci at which coupled differential DNA methylation and RNA transcription were associated with the presence of intestinal microbiota. We validated a subset of differentially expressed and methylated genes in an independent mouse cohort, indicating the existence of microbiota-dependent “functional” methylation sites which may impact on long-term gene expression signatures in IECs. Our study represents the first genome-wide analysis of microbiota-mediated effects on maturation of DNA methylation signatures and the transcriptional program of IECs after birth. It indicates that the gut microbiota dynamically modulates large portions of the epithelial transcriptome during postnatal development, but targets only a subset of microbially responsive genes through their DNA methylation status.}},
    pages = {27},
    number = {1},
    volume = {10},
    language = {English},
    year = {2018},
    rating = {0}
    }

2017

  • Müller, F., Lengauer, T., Bock, C., & Brors, B.. (2017). Analyzing DNA Methylation Signatures of Cell Identity. , 1 – 177. doi:10.17617/2.2474737
    [BibTeX] [Abstract]

    {Although virtually all cells in an organism share the same genome, regulatory mechanisms give rise to hundreds of different, highly specialized cell types. Understanding these mechanisms has been in the limelight of epigenomic research. It is now evident that cellular identity is inscribed in the epigenome of each individual cell. Nonetheless, the precise mechanisms by which different epigenomic marks are involved in regulating gene expression are just beginning to be unraveled. Furthermore, epigenomic patterns are highly dynamic and subject to environmental influences. Any given cell type is defined by cell populations exhibiting epigenetic heterogeneity at different levels. Characterizing this heterogeneity is paramount in understanding the regulatory role of the epigenome. Different epigenomic marks can be profiled using high-throughput sequencing, and global initiatives have started to provide a comprehensive picture of the human epigenome by assaying a multitude of marks across a broad panel of cell types and conditions. In particular, DNA methylation has been extensively studied for its gene-regulatory role in health and disease. This thesis describes computational methods and pipelines for the analysis of DNA methylation data. It provides concepts for addressing bioinformatic challenges such as the processing of large, epigenome-wide datasets and integrating multiple levels of information in an interpretable manner. We developed RnBeads, an R package that facilitates comprehensive, interpretable analysis of large-scale DNA methylation datasets at the level of single CpGs or genomic regions of interest. With the epiRepeatR pipeline, we introduced additional tools for studying global patterns of epigenomic marks in transposons and other repetitive regions of the genome. Blood-cell differentiation represents a useful model for studying trajectories of cellular differentiation. We developed and applied bioinformatic methods to dissect the DNA methylation landscape of the hematopoietic system. Here, we provide a broad outline of cell-type-specific DNA methylation signatures and phenotypic diversity reflected in the epigenomes of human mature blood cells. We also describe the DNA methylation dynamics in the process of immune memory formation in T helper cells. Moreover, we portrayed epigenetic fingerprints of defined progenitor cell types and derived computational models that were capable of accurately inferring cell identity. We used these models in order to characterize heterogeneity in progenitor cell populations, to identify DNA methylation signatures of hematopoietic differentiation and to infer the epigenomic similarities of blood cell types. Finally, by interpreting DNA methylation patterns in leukemia and derived pluripotent cells, we started to discern how epigenomic patterns are altered in disease and explored how reprogramming of these patterns could potentially be used to restore a non-malignant state. In summary, this work showcases novel methods and computational tools for the identification and interpretation of epigenetic signatures of cell identity. It provides a detailed view on the epigenomic landscape spanned by DNA methylation patterns in hematopoietic cells that enhances our understanding of epigenetic regulation in cell differentiation and disease.}

    @article{Müller:2017,
    title = {{Analyzing DNA Methylation Signatures of Cell Identity}},
    author = {Müller, Fabian and Lengauer, Thomas and Bock, Christoph and Brors, Benedikt},
    doi = {10.17617/2.2474737},
    abstract = {{Although virtually all cells in an organism share the same genome, regulatory mechanisms give rise to hundreds of different, highly specialized cell types. Understanding these mechanisms has been in the limelight of epigenomic research. It is now evident that cellular identity is inscribed in the epigenome of each individual cell. Nonetheless, the precise mechanisms by which different epigenomic marks are involved in regulating gene expression are just beginning to be unraveled. Furthermore, epigenomic patterns are highly dynamic and subject to environmental influences. Any given cell type is defined by cell populations exhibiting epigenetic heterogeneity at different levels. Characterizing this heterogeneity is paramount in understanding the regulatory role of the epigenome.
    Different epigenomic marks can be profiled using high-throughput sequencing, and global initiatives have started to provide a comprehensive picture of the human epigenome by assaying a multitude of marks across a broad panel of cell types and conditions. In particular, DNA methylation has been extensively studied for its gene-regulatory role in health and disease.
    This thesis describes computational methods and pipelines for the analysis of DNA methylation data. It provides concepts for addressing bioinformatic challenges such as the processing of large, epigenome-wide datasets and integrating multiple levels of information in an interpretable manner. We developed RnBeads, an R package that facilitates comprehensive, interpretable analysis of large-scale DNA methylation datasets at the level of single CpGs or genomic regions of interest. With the epiRepeatR pipeline, we introduced additional tools for studying global patterns of epigenomic marks in transposons and other repetitive regions of the genome.
    Blood-cell differentiation represents a useful model for studying trajectories of cellular differentiation. We developed and applied bioinformatic methods to dissect the DNA methylation landscape of the hematopoietic system. Here, we provide a broad outline of cell-type-specific DNA methylation signatures and phenotypic diversity reflected in the epigenomes of human mature blood cells. We also describe the DNA methylation dynamics in the process of immune memory formation in T helper cells. Moreover, we portrayed epigenetic fingerprints of defined progenitor cell types and derived computational models that were capable of accurately inferring cell identity. We used these models in order to characterize heterogeneity in progenitor cell populations, to identify DNA methylation signatures of hematopoietic differentiation and to infer the epigenomic similarities of blood cell types.
    Finally, by interpreting DNA methylation patterns in leukemia and derived pluripotent cells, we started to discern how epigenomic patterns are altered in disease and explored how reprogramming of these patterns could potentially be used to restore a non-malignant state.
    In summary, this work showcases novel methods and computational tools for the identification and interpretation of epigenetic signatures of cell identity. It provides a detailed view on the epigenomic landscape spanned by DNA methylation patterns in hematopoietic cells that enhances our understanding of epigenetic regulation in cell differentiation and disease.}},
    pages = {1 -- 177},
    language = {English},
    year = {2017},
    rating = {0}
    }

2016

  • Farlik, M., Halbritter, F., Müller, F., Choudry, F. A., Ebert, P., Klughammer, J., Farrow, S., Santoro, A., Ciaurro, V., Mathur, A., Uppal, R., Stunnenberg, H. G., Ouwehand, W. H., Laurenti, E., Lengauer, T., Frontini, M., & Bock, C.. (2016). DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation. Cell stem cell, 19(6), 808 – 822. doi:10.1016/j.stem.2016.10.019
    [BibTeX] [Abstract]

    {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.}

    @article{Farlik:2016,
    title = {{DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation}},
    author = {Farlik, Matthias and Halbritter, Florian and Müller, Fabian and Choudry, Fizzah A and Ebert, Peter and Klughammer, Johanna and Farrow, Samantha and Santoro, Antonella and Ciaurro, Valerio and Mathur, Anthony and Uppal, Rakesh and Stunnenberg, Hendrik G and Ouwehand, Willem H and Laurenti, Elisa and Lengauer, Thomas and Frontini, Mattia and Bock, Christoph},
    journal = {Cell Stem Cell},
    doi = {10.1016/j.stem.2016.10.019},
    abstract = {{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.}},
    pages = {808 -- 822},
    number = {6},
    volume = {19},
    language = {English},
    year = {2016},
    rating = {0}
    }

  • Durek, P., Nordström, K., Gasparoni, G., Salhab, A., Kressler, C., de Almeida, M., Bassler, K., Ulas, T., Schmidt, F., Xiong, J., Glažar, P., Klironomos, F., Sinha, A., Kinkley, S., Yang, X., Arrigoni, L., Amirabad, A. D., Ardakani, F. B., Feuerbach, L., Gorka, O., Ebert, P., Müller, F., Li, N., Frischbutter, S., Schlickeiser, S., Cendon, C., Fröhler, S., Felder, B., Gasparoni, N., Imbusch, C. D., Hutter, B., Zipprich, G., Tauchmann, Y., Reinke, S., Wassilew, G., Hoffmann, U., Richter, A. S., Sieverling, L., Chang, H., Syrbe, U., Kalus, U., Eils, J., Brors, B., Manke, T., Ruland, J., Lengauer, T., Rajewsky, N., Chen, W., Dong, J., Sawitzki, B., Chung, H., Rosenstiel, P., Schulz, M. H., Schultze, J. L., Radbruch, A., Walter, J., Hamann, A., & Polansky, J. K.. (2016). Epigenomic Profiling of Human CD4+ T Cells Supports a Linear Differentiation Model and Highlights Molecular Regulators of Memory Development. Immunity, 45(5), 1148 – 1161. doi:10.1016/j.immuni.2016.10.022
    [BibTeX] [Abstract]

    {Immunity, 45 (2016) 1148-1161. doi:10.1016/j.immuni.2016.10.022}

    @article{Durek:2016,
    title = {{Epigenomic Profiling of Human CD4+ T Cells Supports a Linear Differentiation Model and Highlights Molecular Regulators of Memory Development}},
    author = {Durek, Pawel and Nordström, Karl and Gasparoni, Gilles and Salhab, Abdulrahman and Kressler, Christopher and Almeida, Melanie de and Bassler, Kevin and Ulas, Thomas and Schmidt, Florian and Xiong, Jieyi and Glažar, Petar and Klironomos, Filippos and Sinha, Anupam and Kinkley, Sarah and Yang, Xinyi and Arrigoni, Laura and Amirabad, Azim Dehghani and Ardakani, Fatemeh Behjati and Feuerbach, Lars and Gorka, Oliver and Ebert, Peter and Müller, Fabian and Li, Na and Frischbutter, Stefan and Schlickeiser, Stephan and Cendon, Carla and Fröhler, Sebastian and Felder, Bärbel and Gasparoni, Nina and Imbusch, Charles D and Hutter, Barbara and Zipprich, Gideon and Tauchmann, Yvonne and Reinke, Simon and Wassilew, Georgi and Hoffmann, Ute and Richter, Andreas S and Sieverling, Lina and Chang, Hyun-Dong and Syrbe, Uta and Kalus, Ulrich and Eils, Jürgen and Brors, Benedikt and Manke, Thomas and Ruland, Jürgen and Lengauer, Thomas and Rajewsky, Nikolaus and Chen, Wei and Dong, Jun and Sawitzki, Birgit and Chung, Ho-Ryun and Rosenstiel, Philip and Schulz, Marcel H and Schultze, Joachim L and Radbruch, Andreas and Walter, Jörn and Hamann, Alf and Polansky, Julia K},
    journal = {Immunity},
    doi = {10.1016/j.immuni.2016.10.022},
    abstract = {{Immunity, 45 (2016) 1148-1161. doi:10.1016/j.immuni.2016.10.022}},
    pages = {1148 -- 1161},
    number = {5},
    volume = {45},
    year = {2016},
    rating = {0}
    }

  • Martínez-Cardús, A., Moran, S., Musulen, E., Moutinho, C., Manzano, J. L., Martinez-Balibrea, E., Tierno, M., Élez, E., Landolfi, S., Lorden, P., Arribas, C., Müller, F., Bock, C., Tabernero, J., & Esteller, M.. (2016). Epigenetic Homogeneity Within Colorectal Tumors Predicts Shorter Relapse-free and Overall Survival Times for Patients With Loco-regional Cancer. Gastroenterology. doi:10.1053/j.gastro.2016.08.001
    [BibTeX] [Abstract]

    {… Christoph Bock, Josep Tabernero, Manel Esteller PII: S0016-5085(16)34892-2 DOI: 10.1053 / j . gastro . 2016.08 . 001 Reference: YGAST 60617 To appear in: Gastroenterology Accepted Date: 2 August 2016 Please cite this article as …}

    @article{Martínez-Cardús:2016,
    title = {{Epigenetic Homogeneity Within Colorectal Tumors Predicts Shorter Relapse-free and Overall Survival Times for Patients With Loco-regional Cancer}},
    author = {Martínez-Cardús, A and Moran, S and Musulen, E and Moutinho, C and Manzano, Jose L and Martinez-Balibrea, Eva and Tierno, Montserrat and Élez, Elena and Landolfi, Stefania and Lorden, Patricia and Arribas, Carles and Müller, Fabian and Bock, Christoph and Tabernero, Josep and Esteller, Manel},
    journal = {Gastroenterology},
    doi = {10.1053/j.gastro.2016.08.001},
    abstract = {{... Christoph Bock, Josep Tabernero, Manel Esteller PII: S0016-5085(16)34892-2 DOI: 10.1053 / j . gastro . 2016.08 . 001 Reference: YGAST 60617 To appear in: Gastroenterology Accepted Date: 2 August 2016 Please cite this article as ...}},
    year = {2016},
    rating = {0}
    }

  • Wallner, S., Schröder, C., Leitão, E., Berulava, T., Haak, C., Beisser, D., Rahmann, S., Richter, A. S., Manke, T., Bönisch, U., Arrigoni, L., Fröhler, S., Klironomos, F., Chen, W., Rajewsky, N., Müller, F., Ebert, P., Lengauer, T., Barann, M., Rosenstiel, P., Gasparoni, G., Nordström, K., Walter, J., Brors, B., Zipprich, G., Felder, B., Klein-Hitpass, L., Attenberger, C., Schmitz, G., & Horsthemke, B.. (2016). Epigenetic dynamics of monocyte-to-macrophage differentiation.. Epigenetics & chromatin, 9(1), 33. doi:10.1186/s13072-016-0079-z
    [BibTeX] [Abstract]

    {BACKGROUND:Monocyte-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. RESULTS:Numerous 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. CONCLUSIONS:In 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.}

    @article{Wallner:2016,
    title = {{Epigenetic dynamics of monocyte-to-macrophage differentiation.}},
    author = {Wallner, Stefan and Schröder, Christopher and Leitão, Elsa and Berulava, Tea and Haak, Claudia and Beisser, Daniela and Rahmann, Sven and Richter, Andreas S and Manke, Thomas and Bönisch, Ulrike and Arrigoni, Laura and Fröhler, Sebastian and Klironomos, Filippos and Chen, Wei and Rajewsky, Nikolaus and Müller, Fabian and Ebert, Peter and Lengauer, Thomas and Barann, Matthias and Rosenstiel, Philip and Gasparoni, Gilles and Nordström, Karl and Walter, Jörn and Brors, Benedikt and Zipprich, Gideon and Felder, Bärbel and Klein-Hitpass, Ludger and Attenberger, Corinna and Schmitz, Gerd and Horsthemke, Bernhard},
    journal = {Epigenetics \& Chromatin},
    doi = {10.1186/s13072-016-0079-z},
    abstract = {{BACKGROUND:Monocyte-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.
    RESULTS:Numerous 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.
    CONCLUSIONS:In 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.}},
    pages = {33},
    number = {1},
    volume = {9},
    language = {English},
    year = {2016},
    rating = {0}
    }

2015

  • Schneider, E., Hajj, N. E., Müller, F., Navarro, B., & Haaf, T.. (2015). Epigenetic Dysregulation in the Prefrontal Cortex of Suicide Completers.. Cytogenetic and genome research, 146(1), 19 – 27. doi:10.1159/000435778
    [BibTeX] [Abstract]

    {The epigenome is thought to mediate between genes and the environment, particularly in response to adverse life experiences. Similar to other psychiatric diseases, the suicide liability of an individual appears to be influenced by many genetic factors of small effect size as well as by environmental stressors. To identify epigenetic marks associated with suicide, which is considered the endpoint of complex gene-environment interactions, we compared the cortex DNA methylation patterns of 6 suicide completers versus 6 non-psychiatric sudden-death controls, using Illumina 450K methylation arrays. Consistent with a multifactorial disease model, we found DNA methylation changes in a large number of genes, but no changes with large effects reaching genome-wide significance. Global methylation of all analyzed CpG sites was significantly (0.25 percentage point) lower in suicide than in control brains, whereas the vast majority (97\%) of the top 1,000 differentially methylated regions (DMRs) were higher methylated (0.6 percentage point) in suicide brains. Annotation analysis of the top 1,000 DMRs revealed an enrichment of differentially methylated promoters in functional categories associated with transcription and expression in the brain. In addition, we performed a comprehensive literature research to identify suicide genes that have been replicated in independent genetic association, brain methylation and/or expression studies. Although, in general, there was no significant overlap between different published data sets or between our top 1,000 DMRs and published data sets, our methylation screen strengthens a number of candidate genes (APLP2, BDNF, HTR1A, NUAK1, PHACTR3, MSMP, SLC6A4, SYN2, and SYNE2) and supports a role for epigenetics in the pathophysiology of suicide.}

    @article{Schneider:2015,
    title = {{Epigenetic Dysregulation in the Prefrontal Cortex of Suicide Completers.}},
    author = {Schneider, Eberhard and Hajj, Nady El and Müller, Fabian and Navarro, Bianca and Haaf, Thomas},
    journal = {Cytogenetic and Genome Research},
    doi = {10.1159/000435778},
    abstract = {{The epigenome is thought to mediate between genes and the environment, particularly in response to adverse life experiences. Similar to other psychiatric diseases, the suicide liability of an individual appears to be influenced by many genetic factors of small effect size as well as by environmental stressors. To identify epigenetic marks associated with suicide, which is considered the endpoint of complex gene-environment interactions, we compared the cortex DNA methylation patterns of 6 suicide completers versus 6 non-psychiatric sudden-death controls, using Illumina 450K methylation arrays. Consistent with a multifactorial disease model, we found DNA methylation changes in a large number of genes, but no changes with large effects reaching genome-wide significance. Global methylation of all analyzed CpG sites was significantly (0.25 percentage point) lower in suicide than in control brains, whereas the vast majority (97\%) of the top 1,000 differentially methylated regions (DMRs) were higher methylated (0.6 percentage point) in suicide brains. Annotation analysis of the top 1,000 DMRs revealed an enrichment of differentially methylated promoters in functional categories associated with transcription and expression in the brain. In addition, we performed a comprehensive literature research to identify suicide genes that have been replicated in independent genetic association, brain methylation and/or expression studies. Although, in general, there was no significant overlap between different published data sets or between our top 1,000 DMRs and published data sets, our methylation screen strengthens a number of candidate genes (APLP2, BDNF, HTR1A, NUAK1, PHACTR3, MSMP, SLC6A4, SYN2, and SYNE2) and supports a role for epigenetics in the pathophysiology of suicide.}},
    pages = {19 -- 27},
    number = {1},
    volume = {146},
    language = {English},
    note = {humble paper
    no significant differences on this relatively underpowered study
    some overlap in top DMR lists with literature},
    year = {2015},
    rating = {3}
    }

  • Ebert, P., Müller, F., Nordström, K., Lengauer, T., & Schulz, M. H.. (2015). A general concept for consistent documentation of computational analyses.. Database, 2015, bav050 – bav050. doi:10.1093/database/bav050
    [BibTeX] [Abstract]

    {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.}

    @article{Ebert:2015o5y,
    title = {{A general concept for consistent documentation of computational analyses.}},
    author = {Ebert, Peter and Müller, Fabian and Nordström, Karl and Lengauer, Thomas and Schulz, Marcel H},
    journal = {Database},
    doi = {10.1093/database/bav050},
    abstract = {{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.}},
    pages = {bav050 -- bav050},
    number = {0},
    volume = {2015},
    language = {English},
    year = {2015},
    rating = {0}
    }

  • Amabile, G., Ruscio, A. D., Müller, F., Welner, R. S., Yang, H., Ebralidze, A. K., Zhang, H., Levantini, E., Qi, L., Martinelli, G., Brummelkamp, T., Beau, M. L. M., Figueroa, M. E., Bock, C., & Tenen, D. G.. (2015). Dissecting the role of aberrant DNA methylation in human leukaemia. Nature communications, 6. doi:10.1038/ncomms8091
    [BibTeX] [Abstract]

    {Chronic myeloid leukaemia (CML) is a myeloproliferative disorder characterized by the genetic translocation t(9;22)(q34;q11.2) encoding for the BCR-ABL fusion oncogene. However, many molecular mechanisms of the disease progression still remain poorly understood. A growing body of evidence suggests that the epigenetic abnormalities are involved in tyrosine kinase resistance in CML, leading to leukaemic clone escape and disease propagation. Here we show that, by applying cellular reprogramming to primary CML cells, aberrant DNA methylation contributes to the disease evolution. Importantly, using a BCR-ABL inducible murine model, we demonstrate that a single oncogenic lesion triggers DNA methylation changes, which in turn act as a precipitating event in leukaemia progression.}

    @article{Amabile:2015,
    title = {{Dissecting the role of aberrant DNA methylation in human leukaemia}},
    author = {Amabile, Giovanni and Ruscio, Annalisa Di and Müller, Fabian and Welner, Robert S and Yang, Henry and Ebralidze, Alexander K and Zhang, Hong and Levantini, Elena and Qi, Lihua and Martinelli, Giovanni and Brummelkamp, Thijn and Beau, Michelle M Le and Figueroa, Maria E and Bock, Christoph and Tenen, Daniel G},
    journal = {Nature Communications},
    doi = {10.1038/ncomms8091},
    abstract = {{Chronic myeloid leukaemia (CML) is a myeloproliferative disorder characterized by the genetic translocation t(9;22)(q34;q11.2) encoding for the BCR-ABL fusion oncogene. However, many molecular mechanisms of the disease progression still remain poorly understood. A growing body of evidence suggests that the epigenetic abnormalities are involved in tyrosine kinase resistance in CML, leading to leukaemic clone escape and disease propagation. Here we show that, by applying cellular reprogramming to primary CML cells, aberrant DNA methylation contributes to the disease evolution. Importantly, using a BCR-ABL inducible murine model, we demonstrate that a single oncogenic lesion triggers DNA methylation changes, which in turn act as a precipitating event in leukaemia progression.}},
    volume = {6},
    language = {English},
    year = {2015},
    rating = {0}
    }

2014

  • Deplus, R., Blanchon, L., Rajavelu, A., Boukaba, A., Defrance, M., Luciani, J., Rothé, F., Dedeurwaerder, S., Denis, H., Brinkman, A. B., Simmer, F., Müller, F., Bertin, B., Berdasco, M., Putmans, P., Calonne, E., Litchfield, D. W., de Launoit, Y., Jurkowski, T. P., Stunnenberg, H. G., Bock, C., Sotiriou, C., Fraga, M. F., Esteller, M., Jeltsch, A., & Fuks, F.. (2014). Regulation of DNA methylation patterns by CK2-mediated phosphorylation of Dnmt3a.. Cellreports, 8(3), 743 – 753. doi:10.1016/j.celrep.2014.06.048
    [BibTeX] [Abstract]

    {DNA methylation is a central epigenetic modification that is established by de novo DNA methyltransferases. The mechanisms underlying the generation of genomic methylation patterns are still poorly understood. Using mass spectrometry and a phosphospecific Dnmt3a antibody, we demonstrate that CK2 phosphorylates endogenous Dnmt3a at two key residues located near its PWWP domain, thereby downregulating the ability of Dnmt3a to methylate DNA. Genome-wide DNA methylation analysis shows that CK2 primarily modulates CpG methylation of several repeats, most notably of Alu SINEs. This modulation can be directly attributed to CK2-mediated phosphorylation of Dnmt3a. We also find that CK2-mediated phosphorylation is required for localization of Dnmt3a to heterochromatin. By revealing phosphorylation as a mode of regulation of de novo DNA methyltransferase function and by uncovering a mechanism for the regulation of methylation at repetitive elements, our results shed light on the origin of DNA methylation patterns.}

    @article{Deplus:2014,
    title = {{Regulation of DNA methylation patterns by CK2-mediated phosphorylation of Dnmt3a.}},
    author = {Deplus, Rachel and Blanchon, Loïc and Rajavelu, Arumugam and Boukaba, Abdelhalim and Defrance, Matthieu and Luciani, Judith and Rothé, Françoise and Dedeurwaerder, Sarah and Denis, Helene and Brinkman, Arie B and Simmer, Femke and Müller, Fabian and Bertin, Benjamin and Berdasco, Maria and Putmans, Pascale and Calonne, Emilie and Litchfield, David W and Launoit, Yvan de and Jurkowski, Tomasz P and Stunnenberg, Hendrik G and Bock, Christoph and Sotiriou, Christos and Fraga, Mario F and Esteller, Manel and Jeltsch, Albert and Fuks, François},
    journal = {CellReports},
    doi = {10.1016/j.celrep.2014.06.048},
    abstract = {{DNA methylation is a central epigenetic modification that is established by de novo DNA methyltransferases. The mechanisms underlying the generation of genomic methylation patterns are still poorly understood. Using mass spectrometry and a phosphospecific Dnmt3a antibody, we demonstrate that CK2 phosphorylates endogenous Dnmt3a at two key residues located near its PWWP domain, thereby downregulating the ability of Dnmt3a to methylate DNA. Genome-wide DNA methylation analysis shows that CK2 primarily modulates CpG methylation of several repeats, most notably of Alu SINEs. This modulation can be directly attributed to CK2-mediated phosphorylation of Dnmt3a. We also find that CK2-mediated phosphorylation is required for localization of Dnmt3a to heterochromatin. By revealing phosphorylation as a mode of regulation of de novo DNA methyltransferase function and by uncovering a mechanism for the regulation of methylation at repetitive elements, our results shed light on the origin of DNA methylation patterns.}},
    pages = {743 -- 753},
    number = {3},
    volume = {8},
    language = {English},
    note = {- CK2 phosphorylates de-novo methyltransferase Dnmt3a
    - global DNA methylation levels increase when CK2 is knocked down
    - Methylation levels in non-repetitive protion of the genome highly correlated/conserved, but repeats show significant differences
    - KD cells hypermeth in SINEs (Alus), hypometh in LINE, LTRs and satellites compared to control
    - hypermeth in Alus confirmed by bisulfite sequencing
    - SINEs are highly rich in CpGs
    - could relate to altered targeting: CK2 depleted cells are less targeted to heterochromatin than control (immunofluorescence)
    - CK2 \textbackslashtextasciitilde Dnmt phosphorylation \textbackslashtextasciitilde reduced Dnmt activity \textbackslashtextasciitilde low global DNA methylation \textbackslashtextasciitilde heterochromatin localization
    - speculation: CK2 mediated phosphorylation of Dnmt singals keeps euchromatic regions open while still silencing of heterochromatin regions. When CK2 is knocked down, (Dnmts localize away from heterochromatin and) methylation spreads more into euchromatic regions (Alus)},
    year = {2014},
    rating = {4}
    }

  • Tobi, E. W., Goeman, J. J., Monajemi, R., Gu, H., Putter, H., Zhang, Y., Slieker, R. C., Stok, A. P., Thijssen, P. E., Müller, F., van Zwet, E. W., Bock, C., Meissner, A., Lumey, L. H., Slagboom, E. P., & Heijmans, B. T.. (2014). DNA methylation signatures link prenatal famine exposure to growth and metabolism.. Nature communications, 5, 5592. doi:10.1038/ncomms6592
    [BibTeX] [Abstract]

    {Periconceptional diet may persistently influence DNA methylation levels with phenotypic consequences. However, a comprehensive assessment of the characteristics of prenatal malnutrition-associated differentially methylated regions (P-DMRs) is lacking in humans. Here we report on a genome-scale analysis of differential DNA methylation in whole blood after periconceptional exposure to famine during the Dutch Hunger Winter. We show that P-DMRs preferentially occur at regulatory regions, are characterized by intermediate levels of DNA methylation and map to genes enriched for differential expression during early development. Validation and further exploratory analysis of six P-DMRs highlight the critical role of gestational timing. Interestingly, differential methylation of the P-DMRs extends along pathways related to growth and metabolism. P-DMRs located in INSR and CPT1A have enhancer activity in vitro and differential methylation is associated with birth weight and serum LDL cholesterol. Epigenetic modulation of pathways by prenatal malnutrition may promote an adverse metabolic phenotype in later life.}

    @article{Tobi:2014,
    title = {{DNA methylation signatures link prenatal famine exposure to growth and metabolism.}},
    author = {Tobi, Elmar W and Goeman, Jelle J and Monajemi, Ramin and Gu, Hongcang and Putter, Hein and Zhang, Yanju and Slieker, Roderick C and Stok, Arthur P and Thijssen, Peter E and Müller, Fabian and Zwet, Erik W van and Bock, Christoph and Meissner, Alexander and Lumey, L H and Slagboom, P Eline and Heijmans, Bastiaan T},
    journal = {Nature Communications},
    doi = {10.1038/ncomms6592},
    abstract = {{Periconceptional diet may persistently influence DNA methylation levels with phenotypic consequences. However, a comprehensive assessment of the characteristics of prenatal malnutrition-associated differentially methylated regions (P-DMRs) is lacking in humans. Here we report on a genome-scale analysis of differential DNA methylation in whole blood after periconceptional exposure to famine during the Dutch Hunger Winter. We show that P-DMRs preferentially occur at regulatory regions, are characterized by intermediate levels of DNA methylation and map to genes enriched for differential expression during early development. Validation and further exploratory analysis of six P-DMRs highlight the critical role of gestational timing. Interestingly, differential methylation of the P-DMRs extends along pathways related to growth and metabolism. P-DMRs located in INSR and CPT1A have enhancer activity in vitro and differential methylation is associated with birth weight and serum LDL cholesterol. Epigenetic modulation of pathways by prenatal malnutrition may promote an adverse metabolic phenotype in later life.}},
    pages = {5592},
    volume = {5},
    language = {English},
    year = {2014},
    rating = {0}
    }

  • Planello, A. C., Ji, J., Sharma, V., Singhania, R., Mbabaali, F., Müller, F., Alfaro, J. A., Bock, C., Carvalho, D. D. D., & Batada, N. N.. (2014). Aberrant DNA methylation reprogramming during induced pluripotent stem cell generation is dependent on the choice of reprogramming factors. Cell regeneration, 3(4). doi:10.1186/2045-9769-3-4
    [BibTeX] [Abstract]

    {The conversion of somatic cells into pluripotent stem cells via overexpression of reprogramming factors involves epigenetic remodeling. DNA methylation at a significant proportion of CpG sites in induced pluripotent stem cells (iPSCs) differs from that of embryonic stem cells (ESCs). Whether different sets of reprogramming factors influence the type and extent of aberrant DNA methylation in iPSCs differently remains unknown. In order to help resolve this critical question, we generated human iPSCs from a common fibroblast cell source using either the Yamanaka factors (OCT4, SOX2, KLF4 and cMYC) or the Thomson factors (OCT4, SOX2, NANOG and LIN28), and determined their genome-wide DNA methylation profiles. In addition to shared DNA methylation aberrations present in all our iPSCs, we identified Yamanaka-iPSC (Y-iPSC)-specific and Thomson-iPSC (T-iPSC)-specific recurrent aberrations. Strikingly, not only were the genomic locations of the aberrations different but also their types: reprogramming with Yamanaka factors mainly resulted in failure to demethylate CpGs, whereas reprogramming with Thomson factors mainly resulted in failure to methylate CpGs. Differences in the level of transcripts encoding DNMT3b and TET3 between Y-iPSCs and T-iPSCs may contribute partially to the distinct types of aberrations. Finally, de novo aberrantly methylated genes in Y-iPSCs were enriched for NANOG targets that are also aberrantly methylated in some cancers. Our study thus reveals that the choice of reprogramming factors influences the amount, location, and class of DNA methylation aberrations in iPSCs. These findings may provide clues into how to produce human iPSCs with fewer DNA methylation abnormalities.}

    @article{Planello:2014,
    title = {{Aberrant DNA methylation reprogramming during induced pluripotent stem cell generation is dependent on the choice of reprogramming factors}},
    author = {Planello, Aline C and Ji, Junfeng and Sharma, Vivek and Singhania, Rajat and Mbabaali, Faridah and Müller, Fabian and Alfaro, Javier A and Bock, Christoph and Carvalho, Daniel D De and Batada, Nizar N},
    journal = {Cell Regeneration},
    doi = {10.1186/2045-9769-3-4},
    abstract = {{The conversion of somatic cells into pluripotent stem cells via overexpression of reprogramming factors involves epigenetic remodeling. DNA methylation at a significant proportion of CpG sites in induced pluripotent stem cells (iPSCs) differs from that of embryonic stem cells (ESCs). Whether different sets of reprogramming factors influence the type and extent of aberrant DNA methylation in iPSCs differently remains unknown. In order to help resolve this critical question, we generated human iPSCs from a common fibroblast cell source using either the Yamanaka factors (OCT4, SOX2, KLF4 and cMYC) or the Thomson factors (OCT4, SOX2, NANOG and LIN28), and determined their genome-wide DNA methylation profiles. In addition to shared DNA methylation aberrations present in all our iPSCs, we identified Yamanaka-iPSC (Y-iPSC)-specific and Thomson-iPSC (T-iPSC)-specific recurrent aberrations. Strikingly, not only were the genomic locations of the aberrations different but also their types: reprogramming with Yamanaka factors mainly resulted in failure to demethylate CpGs, whereas reprogramming with Thomson factors mainly resulted in failure to methylate CpGs. Differences in the level of transcripts encoding DNMT3b and TET3 between Y-iPSCs and T-iPSCs may contribute partially to the distinct types of aberrations. Finally, de novo aberrantly methylated genes in Y-iPSCs were enriched for NANOG targets that are also aberrantly methylated in some cancers. Our study thus reveals that the choice of reprogramming factors influences the amount, location, and class of DNA methylation aberrations in iPSCs. These findings may provide clues into how to produce human iPSCs with fewer DNA methylation abnormalities.}},
    number = {4},
    volume = {3},
    language = {English},
    year = {2014},
    rating = {0}
    }

  • Assenov, Y., Müller, F., Lutsik, P., Walter, J., Lengauer, T., & Bock, C.. (2014). Comprehensive analysis of DNA methylation data with RnBeads. Nature methods, 11(11), 1138 – 1140. doi:10.1038/nmeth.3115
    [BibTeX] [Abstract]

    {RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/). Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data. Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.}

    @article{Assenov:2014rxh,
    title = {{Comprehensive analysis of DNA methylation data with RnBeads}},
    author = {Assenov, Yassen and Müller, Fabian and Lutsik, Pavlo and Walter, Jörn and Lengauer, Thomas and Bock, Christoph},
    journal = {Nature methods},
    doi = {10.1038/nmeth.3115},
    abstract = {{RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/). Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data. Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.}},
    pages = {1138 -- 1140},
    number = {11},
    volume = {11},
    language = {English},
    year = {2014},
    rating = {0}
    }

2013

  • Sandoval, J., Mendez-Gonzalez, J., Nadal, E., Chen, G., Carmona, J. F., Sayols, S., Moran, S., Heyn, H., Vizoso, M., Gomez, A., Sanchez-Cespedes, M., Assenov, Y., Müller, F., Bock, C., Taron, M., Mora, J., Muscarella, L. A., Liloglou, T., Davies, M., Pollan, M., Pajares, M. J., Torre, W., Montuenga, L. M., Brambilla, E., Field, J. K., Roz, L., Iacono, M. L., Scagliotti, G. V., Rosell, R., Beer, D. G., & Esteller, M.. (2013). A prognostic DNA methylation signature for stage I non-small-cell lung cancer.. Journal of clinical oncology, 31(32), 4140 – 4147. doi:10.1200/jco.2012.48.5516
    [BibTeX] [Abstract]

    {PURPOSE:Non-small-cell lung cancer (NSCLC) is a tumor in which only small improvements in clinical outcome have been achieved. The issue is critical for stage I patients for whom there are no available biomarkers that indicate which high-risk patients should receive adjuvant chemotherapy. We aimed to find DNA methylation markers that could be helpful in this regard. PATIENTS AND METHODS:A DNA methylation microarray that analyzes 450,000 CpG sites was used to study tumoral DNA obtained from 444 patients with NSCLC that included 237 stage I tumors. The prognostic DNA methylation markers were validated by a single-methylation pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC. RESULTS:Unsupervised clustering of the 10,000 most variable DNA methylation sites in the discovery cohort identified patients with high-risk stage I NSCLC who had shorter relapse-free survival (RFS; hazard ratio [HR], 2.35; 95\% CI, 1.29 to 4.28; P = .004). The study in the validation cohort of the significant methylated sites from the discovery cohort found that hypermethylation of five genes was significantly associated with shorter RFS in stage I NSCLC: HIST1H4F, PCDHGB6, NPBWR1, ALX1, and HOXA9. A signature based on the number of hypermethylated events distinguished patients with high- and low-risk stage I NSCLC (HR, 3.24; 95\% CI, 1.61 to 6.54; P = .001). CONCLUSION:The DNA methylation signature of NSCLC affects the outcome of stage I patients, and it can be practically determined by user-friendly polymerase chain reaction assays. The analysis of the best DNA methylation biomarkers improved prognostic accuracy beyond standard staging.}

    @article{Sandoval:2013,
    title = {{A prognostic DNA methylation signature for stage I non-small-cell lung cancer.}},
    author = {Sandoval, Juan and Mendez-Gonzalez, Jesus and Nadal, Ernest and Chen, Guoan and Carmona, F Javier and Sayols, Sergi and Moran, Sebastian and Heyn, Holger and Vizoso, Miguel and Gomez, Antonio and Sanchez-Cespedes, Montse and Assenov, Yassen and Müller, Fabian and Bock, Christoph and Taron, Miquel and Mora, Josefina and Muscarella, Lucia A and Liloglou, Triantafillos and Davies, Michael and Pollan, Marina and Pajares, Maria J and Torre, Wenceslao and Montuenga, Luis M and Brambilla, Elisabeth and Field, John K and Roz, Luca and Iacono, Marco Lo and Scagliotti, Giorgio V and Rosell, Rafael and Beer, David G and Esteller, Manel},
    journal = {Journal of Clinical Oncology},
    doi = {10.1200/jco.2012.48.5516},
    abstract = {{PURPOSE:Non-small-cell lung cancer (NSCLC) is a tumor in which only small improvements in clinical outcome have been achieved. The issue is critical for stage I patients for whom there are no available biomarkers that indicate which high-risk patients should receive adjuvant chemotherapy. We aimed to find DNA methylation markers that could be helpful in this regard.
    PATIENTS AND METHODS:A DNA methylation microarray that analyzes 450,000 CpG sites was used to study tumoral DNA obtained from 444 patients with NSCLC that included 237 stage I tumors. The prognostic DNA methylation markers were validated by a single-methylation pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC.
    RESULTS:Unsupervised clustering of the 10,000 most variable DNA methylation sites in the discovery cohort identified patients with high-risk stage I NSCLC who had shorter relapse-free survival (RFS; hazard ratio [HR], 2.35; 95\% CI, 1.29 to 4.28; P = .004). The study in the validation cohort of the significant methylated sites from the discovery cohort found that hypermethylation of five genes was significantly associated with shorter RFS in stage I NSCLC: HIST1H4F, PCDHGB6, NPBWR1, ALX1, and HOXA9. A signature based on the number of hypermethylated events distinguished patients with high- and low-risk stage I NSCLC (HR, 3.24; 95\% CI, 1.61 to 6.54; P = .001).
    CONCLUSION:The DNA methylation signature of NSCLC affects the outcome of stage I patients, and it can be practically determined by user-friendly polymerase chain reaction assays. The analysis of the best DNA methylation biomarkers improved prognostic accuracy beyond standard staging.}},
    pages = {4140 -- 4147},
    number = {32},
    volume = {31},
    language = {English},
    note = {Lung cancer 450k data
    Hierarchical clustering identifies 2 groups
    Associated with relapse free survival (RFS)
    Batch effects?},
    year = {2013},
    rating = {3}
    }

  • Ziller, M. J., Gu, H., Müller, F., Donaghey, J., Tsai, L. T. Y., Kohlbacher, O., Jager, P. D. L., Rosen, E. D., Bennett, D. A., Bernstein, B. E., Gnirke, A., & Meissner, A.. (2013). Charting a dynamic DNA methylation landscape of the human genome. Nature, 500(7463), 477 – 481. doi:10.1038/nature12433
    [BibTeX] [Abstract]

    {DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80\% of all CpGs being methylated. Despite recent advances, we still have a limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in-depth analysis of 42 whole-genome bisulphite sequencing data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8\% of autosomal CpGs within a normal developmental context, most of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription-factor-binding sites, which allow identification of key lineage-specific regulators. In addition, differentially methylated regions (DMRs) often contain single nucleotide polymorphisms associated with cell-type-related diseases as determined by genome-wide association studies. The results also highlight the general inefficiency of whole-genome bisulphite sequencing, as 70-80\% of the sequencing reads across these data sets provided little or no relevant information about CpG methylation. To demonstrate further the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results suggest that only a fraction does so as part of coordinated regulatory programs. Therefore, our selected DMRs can serve as a starting point to guide new, more effective reduced representation approaches to capture the most informative fraction of CpGs, as well as further pinpoint putative regulatory elements.}

    @article{Ziller:2013,
    title = {{Charting a dynamic DNA methylation landscape of the human genome}},
    author = {Ziller, Michael J and Gu, Hongcang and Müller, Fabian and Donaghey, Julie and Tsai, Linus T Y and Kohlbacher, Oliver and Jager, Philip L De and Rosen, Evan D and Bennett, David A and Bernstein, Bradley E and Gnirke, Andreas and Meissner, Alexander},
    journal = {Nature},
    doi = {10.1038/nature12433},
    abstract = {{DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80\% of all CpGs being methylated. Despite recent advances, we still have a limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in-depth analysis of 42 whole-genome bisulphite sequencing data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8\% of autosomal CpGs within a normal developmental context, most of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription-factor-binding sites, which allow identification of key lineage-specific regulators. In addition, differentially methylated regions (DMRs) often contain single nucleotide polymorphisms associated with cell-type-related diseases as determined by genome-wide association studies. The results also highlight the general inefficiency of whole-genome bisulphite sequencing, as 70-80\% of the sequencing reads across these data sets provided little or no relevant information about CpG methylation. To demonstrate further the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results suggest that only a fraction does so as part of coordinated regulatory programs. Therefore, our selected DMRs can serve as a starting point to guide new, more effective reduced representation approaches to capture the most informative fraction of CpGs, as well as further pinpoint putative regulatory elements.}},
    pages = {477 -- 481},
    number = {7463},
    volume = {500},
    language = {English},
    year = {2013},
    rating = {0}
    }

2012

  • Feuerbach, L., Halachev, K., Assenov, Y., Müller, F., Bock, C., & Lengauer, T.. (2012). Analyzing epigenome data in context of genome evolution and human diseases.. Methods in molecular biology (clifton, nj), 856, 431 – 467. doi:10.1007/978-1-61779-585-5_18
    [BibTeX] [Abstract]

    {This chapter describes bioinformatic tools for analyzing epigenome differences between species and in diseased versus normal cells. We illustrate the interplay of several Web-based tools in a case study of CpG island evolution between human and mouse. Starting from a list of orthologous genes, we use the Galaxy Web service to obtain gene coordinates for both species. These data are further analyzed in EpiGRAPH, a Web-based tool that identifies statistically significant epigenetic differences between genome region sets. Finally, we outline how the use of the statistical programming language R enables deeper insights into the epigenetics of human diseases, which are difficult to obtain without writing custom scripts. In summary, our tutorial describes how Web-based tools provide an easy entry into epigenome data analysis while also highlighting the benefits of learning a scripting language in order to unlock the vast potential of public epigenome datasets.}

    @article{Feuerbach:2012,
    title = {{Analyzing epigenome data in context of genome evolution and human diseases.}},
    author = {Feuerbach, Lars and Halachev, Konstantin and Assenov, Yassen and Müller, Fabian and Bock, Christoph and Lengauer, Thomas},
    journal = {Methods in molecular biology (Clifton, NJ)},
    doi = {10.1007/978-1-61779-585-5\_18},
    abstract = {{This chapter describes bioinformatic tools for analyzing epigenome differences between species and in diseased versus normal cells. We illustrate the interplay of several Web-based tools in a case study of CpG island evolution between human and mouse. Starting from a list of orthologous genes, we use the Galaxy Web service to obtain gene coordinates for both species. These data are further analyzed in EpiGRAPH, a Web-based tool that identifies statistically significant epigenetic differences between genome region sets. Finally, we outline how the use of the statistical programming language R enables deeper insights into the epigenetics of human diseases, which are difficult to obtain without writing custom scripts. In summary, our tutorial describes how Web-based tools provide an easy entry into epigenome data analysis while also highlighting the benefits of learning a scripting language in order to unlock the vast potential of public epigenome datasets.}},
    pages = {431 -- 467},
    volume = {856},
    language = {English},
    year = {2012},
    rating = {0}
    }

  • Xi, Y., Bock, C., Müller, F., Sun, D., Meissner, A., & Li, W.. (2012). RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing. Bioinformatics (oxford, england), 28(3), 430 – 432. doi:10.1093/bioinformatics/btr668
    [BibTeX] [Abstract]

    {SUMMARY:Reduced representation bisulfite sequencing (RRBS) is a powerful yet cost-efficient method for studying DNA methylation on a genomic scale. RRBS involves restriction-enzyme digestion, bisulfite conversion and size selection, resulting in DNA sequencing data that require special bioinformatic handling. Here, we describe RRBSMAP, a short-read alignment tool that is designed for handling RRBS data in a user-friendly and scalable way. RRBSMAP uses wildcard alignment, and avoids the need for any preprocessing or post-processing steps. We benchmarked RRBSMAP against a well-validated MAQ-based pipeline for RRBS read alignment and observed similar accuracy but much improved runtime performance, easier handling and better scaling to large sample sets. In summary, RRBSMAP removes bioinformatic hurdles and reduces the computational burden of large-scale epigenome association studies performed with RRBS. AVAILABILITY:http://rrbsmap.computational-epigenetics.org/ http://code.google.com/p/bsmap/ CONTACT:wl1@bcm.tmc.edu SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.}

    @article{Xi:2012,
    title = {{RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing}},
    author = {Xi, Yuanxin and Bock, Christoph and Müller, Fabian and Sun, Deqiang and Meissner, Alexander and Li, Wei},
    journal = {Bioinformatics (Oxford, England)},
    doi = {10.1093/bioinformatics/btr668},
    abstract = {{SUMMARY:Reduced representation bisulfite sequencing (RRBS) is a powerful yet cost-efficient method for studying DNA methylation on a genomic scale. RRBS involves restriction-enzyme digestion, bisulfite conversion and size selection, resulting in DNA sequencing data that require special bioinformatic handling. Here, we describe RRBSMAP, a short-read alignment tool that is designed for handling RRBS data in a user-friendly and scalable way. RRBSMAP uses wildcard alignment, and avoids the need for any preprocessing or post-processing steps. We benchmarked RRBSMAP against a well-validated MAQ-based pipeline for RRBS read alignment and observed similar accuracy but much improved runtime performance, easier handling and better scaling to large sample sets. In summary, RRBSMAP removes bioinformatic hurdles and reduces the computational burden of large-scale epigenome association studies performed with RRBS.
    AVAILABILITY:http://rrbsmap.computational-epigenetics.org/ http://code.google.com/p/bsmap/
    CONTACT:wl1@bcm.tmc.edu
    SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.}},
    pages = {430 -- 432},
    number = {3},
    volume = {28},
    language = {English},
    year = {2012},
    rating = {0}
    }

2011

  • Ziller, M. J., Müller, F., Liao, J., Zhang, Y., Gu, H., Bock, C., Boyle, P., Epstein, C. B., Bernstein, B. E., Lengauer, T., Gnirke, A., & Meissner, A.. (2011). Genomic Distribution and Inter-Sample Variation of Non-CpG Methylation across Human Cell Types. Plos genetics, 7(12), e1002389. doi:10.1371/journal.pgen.1002389
    [BibTeX] [Abstract]

    {… Table 1. Summary statistics for samples included in this study. doi: 10.1371 / journal . pgen . 1002389 . t001 . Starting with the H1 (passage 25) ESCs [14], we found that among the three possible non-CpG dinucleotides (CpA, CpT …}

    @article{Ziller:2011,
    title = {{Genomic Distribution and Inter-Sample Variation of Non-CpG Methylation across Human Cell Types}},
    author = {Ziller, Michael J and Müller, Fabian and Liao, Jing and Zhang, Yingying and Gu, Hongcang and Bock, Christoph and Boyle, Patrick and Epstein, Charles B and Bernstein, Bradley E and Lengauer, Thomas and Gnirke, Andreas and Meissner, Alexander},
    journal = {PLoS Genetics},
    doi = {10.1371/journal.pgen.1002389},
    abstract = {{... Table 1. Summary statistics for samples included in this study. doi: 10.1371 / journal . pgen . 1002389 . t001 . Starting with the H1 (passage 25) ESCs [14], we found that among the three possible non-CpG dinucleotides (CpA, CpT ...}},
    editor = {Schübeler, Dirk},
    pages = {e1002389},
    number = {12},
    volume = {7},
    language = {English},
    year = {2011},
    rating = {0}
    }

2010

  • Bock, C., Tomazou, E. M., Brinkman, A. B., Müller, F., Simmer, F., Gu, H., Jäger, N., Gnirke, A., Stunnenberg, H. G., & Meissner, A.. (2010). Quantitative comparison of genome-wide DNA methylation mapping technologies. Nature biotechnology, 28(10), 1106 – 1114. doi:10.1038/nbt.1681
    [BibTeX] [Abstract]

    {DNA methylation plays a key role in regulating eukaryotic gene expression. Although mitotically heritable and stable over time, patterns of DNA methylation frequently change in response to cell differentiation, disease and environmental influences. Several methods have been developed to map DNA methylation on a genomic scale. Here, we benchmark four of these approaches by analyzing two human embryonic stem cell lines derived from genetically unrelated embryos and a matched pair of colon tumor and adjacent normal colon tissue obtained from the same donor. Our analysis reveals that methylated DNA immunoprecipitation sequencing (MeDIP-seq), methylated DNA capture by affinity purification (MethylCap-seq), reduced representation bisulfite sequencing (RRBS) and the Infinium HumanMethylation27 assay all produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths and weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies.}

    @article{Bock:2010euk,
    title = {{Quantitative comparison of genome-wide DNA methylation mapping technologies}},
    author = {Bock, Christoph and Tomazou, Eleni M and Brinkman, Arie B and Müller, Fabian and Simmer, Femke and Gu, Hongcang and Jäger, Natalie and Gnirke, Andreas and Stunnenberg, Hendrik G and Meissner, Alexander},
    journal = {Nature Biotechnology},
    doi = {10.1038/nbt.1681},
    abstract = {{DNA methylation plays a key role in regulating eukaryotic gene expression. Although mitotically heritable and stable over time, patterns of DNA methylation frequently change in response to cell differentiation, disease and environmental influences. Several methods have been developed to map DNA methylation on a genomic scale. Here, we benchmark four of these approaches by analyzing two human embryonic stem cell lines derived from genetically unrelated embryos and a matched pair of colon tumor and adjacent normal colon tissue obtained from the same donor. Our analysis reveals that methylated DNA immunoprecipitation sequencing (MeDIP-seq), methylated DNA capture by affinity purification (MethylCap-seq), reduced representation bisulfite sequencing (RRBS) and the Infinium HumanMethylation27 assay all produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths and weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies.}},
    pages = {1106 -- 1114},
    number = {10},
    volume = {28},
    language = {English},
    note = {[JournalClub: Fabian]},
    keywords = {Journal Club},
    year = {2010},
    rating = {0}
    }