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Author

Yassen Assenov

Other affiliations: Heidelberg University, Max Planck Society, Epigenomics AG  ...read more
Bio: Yassen Assenov is an academic researcher from German Cancer Research Center. The author has contributed to research in topics: DNA methylation & Epigenetics. The author has an hindex of 28, co-authored 54 publications receiving 6718 citations. Previous affiliations of Yassen Assenov include Heidelberg University & Max Planck Society.


Papers
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Journal ArticleDOI
TL;DR: An R Bioconductor package, Maftools, is described, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses, and is independent of larger alignment files.
Abstract: Numerous large-scale genomic studies of matched tumor-normal samples have established the somatic landscapes of most cancer types. However, the downstream analysis of data from somatic mutations entails a number of computational and statistical approaches, requiring usage of independent software and numerous tools. Here, we describe an R Bioconductor package, Maftools, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses. Maftools only requires somatic variants in Mutation Annotation Format (MAF) and is independent of larger alignment files. With the implementation of well-established statistical and computational methods, Maftools facilitates data-driven research and comparative analysis to discover novel results from publicly available data sets. In the present study, using three of the well-annotated cohorts from The Cancer Genome Atlas (TCGA), we describe the application of Maftools to reproduce known results. More importantly, we show that Maftools can also be used to uncover novel findings through integrative analysis.

1,990 citations

Journal ArticleDOI
Peter J. Campbell1, Gad Getz2, Jan O. Korbel3, Joshua M. Stuart4  +1329 moreInstitutions (238)
06 Feb 2020-Nature
TL;DR: The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.
Abstract: Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1,2,3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10,11,12,13,14,15,16,17,18.

1,600 citations

Journal ArticleDOI
TL;DR: The versatile Cytoscape plugin NetworkAnalyzer computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, and the characteristic path length.
Abstract: Summary: Rapidly increasing amounts of molecular interaction data are being produced by various experimental techniques and computational prediction methods. In order to gain insight into the organization and structure of the resultant large complex networks formed by the interacting molecules, we have developed the versatile Cytoscape plugin NetworkAnalyzer. It computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, the characteristic path length, and the distributions of node degrees, neighborhood connectivities, average clustering coefficients, and shortest path lengths. NetworkAnalyzer can be applied to both directed and undirected networks and also contains extra functionality to construct the intersection or union of two networks. It is an interactive and highly customizable application that requires no expert knowledge in graph theory from the user. Availability: NetworkAnalyzer can be downloaded via the Cytoscape web site: http://www.cytoscape.org Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

1,476 citations

Journal ArticleDOI
TL;DR: 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).
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.

564 citations

Journal ArticleDOI
TL;DR: This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks, to perform a topological analysis of biological networks.
Abstract: Computational analysis and interactive visualization of biological networks and protein structures are common tasks for gaining insight into biological processes. This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks. NetworkAnalyzer has become a standard Cytoscape tool for comprehensive network topology analysis. In addition, RINalyzer provides methods for exploring residue interaction networks derived from protein structures. The first workflow uses NetworkAnalyzer to perform a topological analysis of biological networks. The second workflow applies RINalyzer to study protein structure and function and to compute network centrality measures. The third workflow combines NetworkAnalyzer and RINalyzer to compare residue networks. The full protocol can be completed in ∼2 h.

424 citations


Cited by
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Proceedings ArticleDOI
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

7,116 citations

Journal ArticleDOI
14 Jun 2007-Nature
TL;DR: Functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project are reported, providing convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts.
Abstract: We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

5,091 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

Journal ArticleDOI
TL;DR: It is proposed that DNA methylation age measures the cumulative effect of an epigenetic maintenance system, and can be used to address a host of questions in developmental biology, cancer and aging research.
Abstract: It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.

4,233 citations