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Next-generation sequencing (NGS) has drastically enhanced human cancer research, but diverse sequencing strategies, complicated open-source software, and the identification of massive numbers of mutations have limited the clinical application of NGS. Here, we first presented GPyFlow, a lightweight tool that flexibly customizes, executes, and shares workflows. We then introduced DIVIS, a customizable pipeline based on GPyFlow that integrates read preprocessing, alignment, variant detection, and annotation of whole-genome sequencing, whole-exome sequencing, and gene-panel sequencing. By default, DIVIS screens variants from multiple callers and generates a standard variant-detection format list containing caller evidence for each sample, which is compatible with advanced analyses. Lastly, DIVIS generates a statistical report, including command lines, parameters, quality-control indicators, and mutation summary. DIVIS substantially facilitates complex cancer genome sequencing analyses by means of a single powerful and easy-to-use command. The DIVIS code is freely available at https://github.com/niu-lab/DIVIS, and the docker image can be downloaded from https://hub.docker.com/repository/docker/sunshinerain/divis.
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http://dx.doi.org/10.3389/fonc.2021.672597 | DOI Listing |
Front Immunol
August 2025
Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, United States.
Introduction: RNA sequencing (RNA-seq) can measure whole transcriptome gene expression from tissues or even individual cells, providing a powerful tool to study the immune response. Analysis of RNA-seq data involves mapping relatively short sequence reads to a reference genome, and quantifying genes based on the position of alignments relative to annotated genes. While this is usually robust, genetic polymorphism or genome/annotation inaccuracies result in genes with systematically missing or inaccurate data.
View Article and Find Full Text PDFBMC Bioinformatics
August 2025
Centre for Discovery Brain Sciences, University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
Background: Analyzing calcium imaging data to understand complex functional networks can be challenging, often requiring multiple tools, custom scripts, and some coding expertise. To address these challenges, we present CalciumNetExploreR (CNER), an R package designed to streamline and standardize the analysis of time-series data from neuronal populations.
Results: CNER integrates essential steps-normalization, binarization, population activity visualization, network construction, degree distribution analysis, principal component analysis, power spectral density evaluation, and event frequency calculations-into a single, cohesive pipeline.
BMC Ecol Evol
August 2025
Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
NCBITaxonomy.jl is a Julia package designed to address the complex challenges of taxonomic name reconciliation using a local copy of the NCBI taxonomic backbone (Federhen in Nucleic Acids Res 40:D136-D143, 2012, Schoch et al. in Database 2020:baaa062, 2020).
View Article and Find Full Text PDFbioRxiv
August 2025
Department of Biological Chemistry, Life Sciences Institute, University of Michigan; Ann Arbor, MI USA.
Single-particle cryo-electron microscopy (cryo-EM) has become an essential tool in structural biology. However, automating repetitive tasks remains an ongoing challenge in cryo-EM dataset processing. Here, we present a platform-independent convolutional neural network (CNN) tool for assessing the quality of 2D averages to enable automatic selection of suitable particles for high-resolution reconstructions, termed CryoSift.
View Article and Find Full Text PDFFront Neuroimaging
July 2025
CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging (MRSI), are non-invasive techniques used to quantify biochemical compounds in tissue, such as choline, creatine, glutamate, glutamine, -aminobutyric acid, N-acetylaspartate, etc. However, reliable quantification of MRS and MRSI data is challenging due to the complex processing steps involved, often requiring advanced expertise. Existing data processing software solutions often demand MRS expertise or coding knowledge, presenting a steep learning curve for novel users.
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