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Westreich, S. T.

Publications and source records attributed to Westreich, S. T..

3 recordsLinked to original sources

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

bioinformatics↗

BigTop: A Three-Dimensional Virtual Reality Tool for GWAS Visualization

BackgroundGenome-wide association studies (GWAS) are typically visualized using a two-dimensional Manhattan plot, displaying chromosomal location of SNPs along the x-axis and the negative log-10 of their p-value on the y-axis. This traditional plot provides a broad overview of the results, but offers little opportunity for interaction or expansion of specific regions, and is unable to show additional dimensions of the dataset.\n\nResultsWe created BigTop, a visualization framework in virtual reality (VR), designed to render a Manhattan plot in three dimensions, wrapping the graph around the user in a simulated cylindrical room. BigTop uses the z-axis to display minor allele frequency of each SNP, allowing for the identification of allelic variants of genes. BigTop also offers additional interactivity, allowing users to select any individual SNP and receive expanded information, including SNP name, exact values, and gene location, if applicable. BigTop is built in JavaScript using the React and A-Frame frameworks, and can be rendered using commercially available VR headsets or in a two-dimensional web browser such as Google Chrome. Data is read into BigTop in JSON format, and can be provided as either JSON or a tab-separated text file.\n\nConclusionsUsing additional dimensions and interactivity options offered through VR, we provide a new, interactive, three-dimensional representation of the traditional Manhattan plot for displaying and exploring GWAS data.

bioinformatics↗

SAMSA2: A standalone metatranscriptome analysis pipeline

BackgroundComplex microbial communities are an area of rapid growth in biology. Metatranscriptomics allows one to investigate the gene activity in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and the sequences must be compared with many references.\n\nResultsHere we present SAMSA2, an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution.\n\nConclusionsUsing publicly available example data, we demonstrate that SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large paired-end RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the specifics of any experiment.

bioinformatics↗