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Exome sequencing has enabled molecular diagnoses for rare disease patients but often with initial diagnostic rates of ~25-30%. Here we develop a robust computational pipeline to rank variants for reassessment of unsolved rare disease patients. A comprehensive web-based patient report is generated in which all deleterious variants can be filtered by gene, variant characteristics, OMIM disease and Phenolyzer scores, and all are annotated with an ACMG classification and links to ClinVar. The pipeline ranked 21/34 previously diagnosed variants as top, with 26 in total ranked ≤7th, 3 ranked ≥13th; 5 failed the pipeline filters. Pathogenic/likely pathogenic variants by ACMG criteria were identified for 22/145 unsolved cases, and a previously undefined candidate disease variant for 27/145. This open access pipeline supports the partnership between clinical and research laboratories to improve the diagnosis of unsolved exomes. It provides a flexible framework for iterative developments to further improve diagnosis.
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http://dx.doi.org/10.1038/s41525-020-00161-w | DOI Listing |
Patterns (N Y)
July 2025
Cedars-Sinai Medical Center, Los Angeles, CA, USA.
The tree-based pipeline optimization tool (TPOT) is one of the earliest automated machine learning (ML) frameworks developed for optimizing ML pipelines, with an emphasis on addressing the complexities of biomedical research. TPOT uses genetic programming to explore a diverse space of pipeline structures and hyperparameter configurations in search of optimal pipelines. Here, we provide a comparative overview of the conceptual similarities and implementation differences between the previous and latest versions of TPOT, focusing on two key aspects: (1) the representation of ML pipelines and (2) the underlying algorithm driving pipeline optimization.
View Article and Find Full Text PDFNat Microbiol
September 2025
Joan and Sanford I. Weill Department of Medicine, Gastroenterology and Hepatology Division, Weill Cornell Medicine, New York, NY, USA.
Microbial influence on cancer development and therapeutic response is a growing area of cancer research. Although it is known that microorganisms can colonize certain tissues and contribute to tumour initiation, the use of deep sequencing technologies and computational pipelines has led to reports of multi-kingdom microbial communities in a growing list of cancer types. This has prompted discussions on the role and scope of microbial presence in cancer, while raising the possibility of microbiome-based diagnostic, prognostic and therapeutic tools.
View Article and Find Full Text PDFNPJ Precis Oncol
September 2025
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Breast cancer is a highly heterogeneous disease with diverse outcomes, and intra-tumoral heterogeneity plays a significant role in both diagnosis and treatment. Despite its importance, the spatial distribution of intra-tumoral heterogeneity is not fully elucidated. Spatial transcriptomics has emerged as a promising tool to study the molecular mechanisms behind many diseases.
View Article and Find Full Text PDFMed Eng Phys
October 2025
Department of Engineering Science, University of Oxford, United Kingdom. Electronic address:
Traditionally, clinical devices are designed, tested and improved through lengthy and expensive laboratory experiments and clinical trials [1]. More recently, computational methods have allowed for rapid testing, speeding up the design process and enabling far more complete searches of design space. While computational models cannot fully capture the complexities of biological systems, they provide valuable insights into crucial underlying mechanisms, such as the effects of fluid-structure interactions (FSIs).
View Article and Find Full Text PDFCell Rep Methods
August 2025
Department of Biomedical Engineering and Computational Biology Program, OHSU, Portland, OR, USA; Knight Cancer Institute, OHSU, Portland, OR, USA. Electronic address:
We present UniFORM, a non-parametric, Python-based pipeline for normalizing multiplex tissue imaging (MTI) data at both the feature and pixel levels. UniFORM employs an automated rigid landmark registration method tailored to the distributional characteristics of MTI, with UniFORM operating without prior distributional assumptions and handling both unimodal and bimodal patterns. By aligning the biologically invariant negative populations, UniFORM removes technical variation while preserving tissue-specific expression patterns in positive populations.
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