Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies

Recent long-read assemblies often exceed the quality and completeness of available reference genomes, making validation challenging. Here we present Merqury, a novel tool for reference-free assembly evaluation based on efficient k-mer set operations. By comparing k-mers in a de novo assembly to those found in unassembled high-accuracy reads, Merqury estimates base-level accuracy and completeness. For trios, Merqury can also evaluate haplotype-specific accuracy, completeness, phase block continuity, and switch errors. Multiple visualizations, such as k-mer spectrum plots, can be generated for evaluation. We demonstrate on both human and plant genomes that Merqury is a fast and robust method for assembly validation.Availability of data and materialProject name: MerquryProject home page: https://github.com/marbl/merqury, https://github.com/marbl/merylArchived version: https://github.com/marbl/merqury/releases/tag/v1.0Operating system(s): Platform independentProgramming language: C++, Java, PerlOther requirements: gcc 4.8 or higher, java 1.6 or higherLicense: Public domain (see https://github.com/marbl/merqury/blob/master/README.license) Any restrictions to use by non-academics: No restrictions applied

PyMethylProcess—convenient high-throughput preprocessing workflow for DNA methylation data

Summary Performing highly parallelized preprocessing of methylation array data using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. We present a highly reproducible, scalable pipeline (PyMethylProcess) that can be quickly set-up and deployed through Docker and PIP. Availability and implementation Project Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI (pymethylprocess), Docker (joshualevy44/pymethylprocess). Supplementary information Supplementary data are available at Bioinformatics online.

PyMethylProcess - highly parallelized preprocessing for DNA methylation array data

SummaryThe ability to perform high-throughput preprocessing of methylation array data is essential in large scale methylation studies. While R is a convenient language for methylation analyses, performing highly parallelized preprocessing using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. Here, we present a methylation data preprocessing pipeline called PyMethylProcess that is highly reproducible, scalable, and that can be quickly set-up and deployed through Docker and PIP.Availability and ImplementationProject Name: PyMethylProcessProject Home Page:https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI aspymethylprocess.Available on DockerHub viajoshualevy44/pymethylprocess.Help Documentation:https://christensen-lab-dartmouth.github.io/PyMethylProcess/Operating Systems: Linux, MacOS, Windows (Docker)Programming Language: Python, ROther Requirements: Python 3.6, R 3.5.1, Docker (optional) License: [email protected]