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

Strategies for analyzing bisulfite sequencing data

DNA methylation is one of the main epigenetic modifications in the eukaryotic genome and 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 further classification of regions returned by segmentation or differential methylation methods. Lastly, we review software packages that implement strategies to efficiently deal with large bisulfite sequencing datasets locally and also 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

PiGx: reproducible genomics analysis pipelines with GNU Guix

In bioinformatics, as well as other computationally-intensive research fields, there is a need for workflows that can reliably produce consistent output, from known sources, independent of the software environment or configuration settings of the machine on which they are executed. Indeed, this is essential for controlled comparison between different observations or for the wider dissemination of workflows. Providing this type of reproducibility and traceability, however, is often complicated by the need to accommodate the myriad dependencies included in a larger body of software, each of which generally come in various versions. Moreover, in many fields (bioinformatics being a prime example), these versions are subject to continual change due to rapidly evolving technologies, further complicating problems related to reproducibility. Here, we propose a principled approach for building analysis pipelines and managing their dependencies with GNU Guix. As a case study to demonstrate the utility of our approach, we present a set of highly reproducible pipelines called PiGx for the analysis of RNA-seq, ChIP-seq, Bisulfite-seq, and single-cell RNA-seq. All pipelines process raw experimental data, and generate reports containing publication-ready plots and figures, with interactive report elements and standard observables. Users may install these highly reproducible packages and apply them to their own datasets without any special computational expertise beyond the use of the command line. We hope such a toolkit will provide immediate benefit to laboratory workers wishing to process their own data sets or bioinformaticians seeking to automate all, or parts of, their analyses. In the long term, we hope our approach to reproducibility will serve as a blueprint for reproducible workflows in other areas. Our pipelines, along with their corresponding documentation and sample reports, are available at http://bioinformatics.mdc-berlin.de/pigx

MRG-1/MRG15 is a barrier for germ cell to neuron reprogramming in Caenorhabditis elegans

Chromatin regulators play important roles in the safeguarding of cell identities by opposing the induction of ectopic cell fates and, thereby, preventing forced conversion of cell identities by reprogramming approaches. Our knowledge of chromatin regulators acting as reprogramming barriers in living organisms needs improvement as most studies use tissue culture. We used C. elegans as an in vivo gene discovery model and automated solid-phase RNAi screening, by which we identified 10 chromatin-regulating factors that protect cells against ectopic fate induction. Specifically, the chromodomain protein MRG-1 safeguards germ cells against conversion into neurons. MRG-1 is the ortholog of mammalian MRG15 (MORF-related gene on chromosome 15) and is required during germline development in C. elegans. However, MRG-1's function as a barrier for germ cell reprogramming has not been revealed previously. Here, we further provide protein-protein and genome interactions of MRG-1 to characterize its molecular functions. Conserved chromatin regulators may have similar functions in higher organisms and, therefore, understanding cell fate protection in C. elegans may also help to facilitate reprogramming of human cells