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Bessell, B.

Publications and source records attributed to Bessell, B..

2 recordsLinked to original sources

AutoRNAseq: Automated Bulk RNA-seq Analysis Pipeline

SummaryImproved accessibility of high-throughput RNA sequencing has increased the amount of data generated each year. This increase in data creates a need for reproducible pipelines that can process RNA-seq data consistently across experiments. AutoRNAseq addresses this need by providing a Snakemake-based workflow for bulk RNA-seq analysis by automating data retrieval, quality control, and gene quantification. Unlike existing RNA-seq workflows that require users to coordinate multiple pipelines and pre-configure reference data, AutoRNAseq provides a single, end-to-end workflow that automates data acquisition, reference preparation, quality control, alignment, and quantification with minimal user intervention. AutoRNAseq is applicable to any domain requiring consistently processed RNA-seq datasets, including bioinformatics, computational biology, and drug-response studies. Availability and ImplementationAutoRNAseq is implemented in Snakemake and available at https://gitlab.com/unebraska/lagbh-public/autornaseq. Documentation and example configuration files are provided in the GitLab README file and this papers Supplementary Information. The code to reproduce the statistics presented here is in the GitLab repository under the "publication" folder.

bioinformatics↗

A personalized multi-platform assessment of somatic mosaicism in the human frontal cortex

Somatic mutations in individual cells create genomic mosaicism, influencing genetic disorders and cancers. While clonal mutations in cancers are well-studied, rarer somatic variants in normal tissues remain poorly characterized. This study systematically evaluates detection methods using a personalized donor-specific assembly (DSA) from a neurotypical individuals dorsolateral prefrontal cortex assessed with Oxford Nanopore, NovaSeq, linked-read sequencing, Cas9-targeted long-read sequencing (TEnCATS), and single-neuron MALBAC amplification. The haplotype-resolved DSA improved cross-platform analysis, dramatically increasing phasing rates. Germline SNVs, structural variations (SVs), and transposable elements (TEs) were recalled with 99.4%-99.7% accuracy in bulk tissue, and phased haplotype analysis reduced false positives by 15.4%-75.1% for putative somatic candidates. Long-read single-neuron sequencing detected nine somatic SV candidates, demonstrating enhanced sensitivity for rare variants, while TEnCATS identified eight low-frequency somatic TE candidates. These findings highlight advanced methodologies for precise somatic variant detection, critical for understanding mosaicisms role in health and disease.

genomics↗