Whole exome sequencing (WES) is a powerful approach for discovering sequence variants in cancer cells but its time effectiveness is limited by the complexity and issues of WES data analysis. Here we present iWhale, a customizable pipeline based on Docker and SCons, reliably detecting somatic variants by three complementary callers (MuTect2, Strelka2 and VarScan2). The results are combined to obtain a single variant call format file for each sample and variants are annotated by integrating a wide range of information extracted from several reference databases, ultimately allowing variant and gene prioritization according to different criteria. iWhale allows users to conduct a complex series of WES analyses with a powerful yet customizable and easy-to-use tool, running on most operating systems (macOs, GNU/Linux and Windows). iWhale code is freely available at https://github.com/alexcoppe/iWhale and the docker image is downloadable from https://hub.docker.com/r/alexcoppe/iwhale.
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http://dx.doi.org/10.1093/bib/bbaa065 | DOI Listing |
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Tissue microenvironments are extremely complex and heterogeneous. It is challenging to study metabolic interaction between the different cell types in a tissue with the techniques that are currently available. Here we describe a multimodal imaging pipeline that allows cell type identification and nanoscale tracing of stable isotope-labeled compounds.
View Article and Find Full Text PDFEpilepsia
January 2025
Applied Translational Neurogenomics Group, Vlaams Instituut voor Biotechnology (VIB) Center for Molecular Neurology, VIB, Antwerp, Belgium.
Objective: This study aims to improve genetic diagnosis in childhood onset epilepsy with neurodevelopmental problems by utilizing RNA sequencing of fibroblasts to identify pathogenic variants that may be missed by exome sequencing and copy number variation analysis.
Methods: We enrolled 41 individuals with childhood onset epilepsy and neurodevelopmental problems who previously had inconclusive genetic testing. Fibroblast samples were cultured and analyzed using RNA sequencing to detect aberrant expression, aberrant splicing, and monoallelic expression using the Detection of RNA Outlier Pipeline (DROP) pipeline.
Int J Gynecol Cancer
January 2025
Institute of Image-Guided Surgery, IHU Strasbourg, France; University of Strasbourg, ICube, Laboratory of Engineering, Computer Science and Imaging, Department of Robotics, Imaging, Teledetection and Healthcare Technologies, CNRS, UMR, Strasbourg, France.
Objective: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.
Methods: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus.
Ophthalmol Sci
November 2024
Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
Objective: Detecting and measuring changes in longitudinal fundus imaging is key to monitoring disease progression in chronic ophthalmic diseases, such as glaucoma and macular degeneration. Clinicians assess changes in disease status by either independently reviewing or manually juxtaposing longitudinally acquired color fundus photos (CFPs). Distinguishing variations in image acquisition due to camera orientation, zoom, and exposure from true disease-related changes can be challenging.
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