Biology subjects

Assenov, Y.

Publications and source records attributed to Assenov, Y..

5 recordsLinked to original sources

bioRxiv2024

Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold-standard

DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposures, and disease. Whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods remains the reference method for DNA methylation profiling genome-wide. While numerous software tools facilitate processing of DNA methylation sequencing reads, a comprehensive benchmarking study has been lacking thus far. In this study, we systematically compared complete computational workflows for processing DNA methylation sequencing data using a dedicated benchmarking dataset generated with five genome-wide profiling protocols. As an evaluation reference, we employed highly quantitative locus-specific measurements from our preceding benchmark of targeted DNA methylation assays. Based on this experimental gold-standard assessment and several comprehensive metrics, we identified workflows that consistently demonstrated superior performance and revealed major workflow development trends. To facilitate the sustainability of our benchmark, we implemented an interactive workflow execution and data presentation platform, adaptable to user-defined criteria and seamlessly expandable to future software.

bioinformatics↗

bioRxiv2020

AmpliconDesign - An interactive web server for the design of high-throughput targeted DNA methylation assays

Targeted analysis of DNA methylation patterns based on bisulfite-treated genomic DNA (BT-DNA) is considered as a gold-standard for epigenetic biomarker development. Existing software tools facilitate primer design, primer quality control or visualization of primer localization. However, high-throughput design of primers for BT-DNA amplification is hampered by limits in throughput and functionality of existing tools, requiring users to repeatedly perform specific tasks manually. Consequently, the design of PCR primers for BT-DNA remains a tedious and time-consuming process. To bridge this gap, we developed AmpliconDesign, a webserver providing a scalable and user-friendly platform for the design and analysis of targeted DNA methylation studies based on BT-DNA, e.g. deep amplicon bisulfite sequencing (ampBS-seq), EpiTYPER MassArray, or pyrosequencing. Core functionality of the web server includes high-throughput primer design and binding site validation based on in silico bisulfite-converted DNA sequences, prediction of fragmentation patterns for EpiTYPER MassArray, an interactive quality control as well as a streamlined analysis workflow for ampBS-seq. Availability and ImplementationThe AmpliconDesign webserver is freely available online at: https://amplicondesign.dkfz.de/. AmpliconDesign has been implemented using the R Shiny framework (Chang et al., 2018). The source code is publicly available under the GNU General Public License v3.0 (https://github.com/MaxSchoenung/AmpliconDesign). ContactDaniel B. Lipka (d.lipka@dkfz.de) & Maximilian Schonung (m.schoenung@dkfz.de)

bioinformatics↗

bioRxiv2020

Methylome-based cell-of-origin modeling (Methyl-COOM) identifies aberrant expression of immune regulatory molecules in CLL

BackgroundIn cancer, normal epigenetic patterns are disturbed and contribute to gene expression changes, disease onset and progression. The cancer epigenome is composed of the epigenetic patterns present in the tumor-initiating cell at the time of transformation, and the tumor-specific epigenetic alterations that are acquired during tumor initiation and progression. The precise dissection of these two components of the tumor epigenome will facilitate a better understanding of the biological mechanisms underlying malignant transformation. Chronic lymphocytic leukemia (CLL) originates from differentiating B cells, which undergo extensive epigenetic programming. This poses the challenge to precisely determine the epigenomic ground-state of the cell-of-origin in order to identify CLL-specific epigenetic aberrations. MethodsWe developed a linear regression model, methylome-based cell-of-origin modeling (Methyl-COOM), to map the cell-of-origin for individual CLL patients based on the continuum of epigenomic changes during normal B cell differentiation. ResultsMethyl-COOM accurately maps the cell-of-origin of CLL and identifies CLL-specific aberrant DNA methylation events that are not confounded by physiologic epigenetic B cell programming. Furthermore, Methyl-COOM unmasks abnormal action of transcription factors, altered super-enhancer activities, and aberrant transcript expression in CLL. Among the aberrantly regulated transcripts were many genes that have previously been implicated in T cell biology. Flow cytometry analysis of these markers confirmed their aberrant expression on malignant B cells at the protein level. ConclusionsMethyl-COOM analysis of CLL identified disease-specific aberrant gene regulation. The aberrantly expressed genes identified in this study might play a role in immune-evasion in CLL and might serve as novel targets for immunotherapy approaches. In summary, we propose a novel framework for in silico modeling of reference DNA methylomes and for the identification of cancer-specific epigenetic changes, a concept that can be broadly applied to other human malignancies.

cancer biology↗

bioRxiv2019

Random forest-based modelling to detect biomarkers for prostate cancer progression

The clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy and robust prognostic markers for treatment decisions. We present a random forest-based classification model to predict aggressive behaviour of PCa. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n=70) were used as input. The model was validated with data from two large independent PCa cohorts from the \"International Cancer Genome Consortium\" (ICGC) and \"The Cancer Genome Atlas\" (TCGA). Ranking of cancer progression-related DNA methylation changes allowed selection of candidate genes for additional validation by immunohistochemistry. We identified loss of ZIC2 protein expression, mediated by alterations in DNA methylation, as a promising novel prognostic biomarker for PCa in >12,000 tissue micro-array tumors. The prognostic value of ZIC2 proved to be independent from established clinico-pathological variables including Gleason grade, tumor stage, nodal stage and PSA. In summary, we have developed a PCa classification model, which either directly or via expression analyses of the identified top ranked candidate genes might help in decision making related to the treatment of prostate cancer patients.

cancer biology↗

bioRxiv2017

Strategies for analyzing bisulfite sequencing data

DNA methylation is one of the main epigenetic modifications in the eukaryotic genome; it has been shown to play a role in cell-type specific regulation of gene expression, and therefore cell-type identity. Bisulfite sequencing is the gold-standard for measuring methylation over the genomes of interest. Here, we review several techniques used for the analysis of high-throughput bisulfite sequencing. We introduce specialized short-read alignment techniques as well as pre/post-alignment quality check methods to ensure data quality. Furthermore, we discuss subsequent analysis steps after alignment. We introduce various differential methylation methods and compare their performance using simulated and real bisulfite sequencing datasets. We also discuss the methods used to segment methylomes in order to pinpoint regulatory regions. We introduce annotation methods that can be used for further classification of regions returned by segmentation and differential methylation methods. Finally, we review software packages that implement strategies to efficiently deal with large bisulfite sequencing datasets locally and we discuss online analysis workflows that do not require any prior programming skills. The analysis strategies described in this review will guide researchers at any level to the best practices of bisulfite sequencing analysis.

bioinformatics↗