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whole exome sequence (wes) data  (Federation of European Neuroscience Societies)

 
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    Federation of European Neuroscience Societies whole exome sequence (wes) data
    Whole Exome Sequence (Wes) Data, supplied by Federation of European Neuroscience Societies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/exome+sequence+data/whole+exome+sequence++wes++data/pm40504793-291-0-22
    Average 90 stars, based on 1 article reviews
    whole exome sequence (wes) data - by Bioz Stars, 2026-11
    90/100 stars

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    Sequencing:

    Article Title: Genomic modifiers of neurological resilience in a Niemann-Pick C family.
    Article Snippet: 1 Facultad de Medicina, Centro de Gen etica y Gen omica, Cl ınica Alemana Universidad del Desarrollo, Santiago, Chile 2 Department of Gastroenterology, Faculty of Medicine, Pontificia Universidad Cat olica de Chile, Santiago, Chile 3 Molecular Pathology Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Trieste, Italy 4 Regional Coordinator Centre for Rare Diseases, University Hospital of Udine, Italy 5 Departamento de Biolog ıa, Facultad de Qu ımica y Biolog ıa, Universidad de Santiago de Chile, Chile 6 ANID-Millennium Science Initiative-Millennium Institute for Integrative Biology (iBio), Santiago, Chile

    Generated:

    Article Title: Genomic modifiers of neurological resilience in a Niemann-Pick C family.
    Article Snippet: 1 Facultad de Medicina, Centro de Gen etica y Gen omica, Cl ınica Alemana Universidad del Desarrollo, Santiago, Chile 2 Department of Gastroenterology, Faculty of Medicine, Pontificia Universidad Cat olica de Chile, Santiago, Chile 3 Molecular Pathology Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Trieste, Italy 4 Regional Coordinator Centre for Rare Diseases, University Hospital of Udine, Italy 5 Departamento de Biolog ıa, Facultad de Qu ımica y Biolog ıa, Universidad de Santiago de Chile, Chile 6 ANID-Millennium Science Initiative-Millennium Institute for Integrative Biology (iBio), Santiago, Chile



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    Image Search Results


    Study design for patient cohorts and genomic analyses (A) Initial study criteria with study design based on sample availability and treatment timing. After whole-exome sequencing, PhylogicNDT and SignatureAnalyzer were used to identify clones and compare mutational signatures, respectively. Functional mutations were characterized based on existing evidence previously published or annotated in genomic databases, such as COSMIC, ClinVar, and OncoKB. (B) Signaling pathway landscape for paired-cohort participants ( n = 26) organized by time on tyrosine kinase treatment (days by sampled TKI) and acquired ( n = 8) or intrinsic ( n = 18) resistance. Known mechanisms in ERBB2 and PIK3CA are highlighted. Signaling pathways are depicted in decreasing frequency per number of mutations for the entire cohort of paired participants. Arrows indicate the subclonal trajectory as either growing, stable, or shrinking. The TP53 and ESR1+ regulator pathways were significantly mutated ( q < 0.05; MutSig, Fisher’s method). Stacked bar charts indicate relative contributions of mutational signatures identified by SignatureAnalyzer to the mutational spectrum of each participant. (C) Pathway-level comparison of mutation frequency between four cohorts: intrinsic ( n = 18), acquired ( n = 8), pre-TKI ( n = 55), and post-TKI ( n = 30). Pathways were included if they possessed at least one mutation. There were no significant differences in pathway-level mutation frequency between cohorts (Fisher’s exact test).

    Journal: Cell Genomics

    Article Title: Detection of heterogeneous resistance mechanisms to tyrosine kinase inhibitors from cell-free DNA

    doi: 10.1016/j.xgen.2025.100987

    Figure Lengend Snippet: Study design for patient cohorts and genomic analyses (A) Initial study criteria with study design based on sample availability and treatment timing. After whole-exome sequencing, PhylogicNDT and SignatureAnalyzer were used to identify clones and compare mutational signatures, respectively. Functional mutations were characterized based on existing evidence previously published or annotated in genomic databases, such as COSMIC, ClinVar, and OncoKB. (B) Signaling pathway landscape for paired-cohort participants ( n = 26) organized by time on tyrosine kinase treatment (days by sampled TKI) and acquired ( n = 8) or intrinsic ( n = 18) resistance. Known mechanisms in ERBB2 and PIK3CA are highlighted. Signaling pathways are depicted in decreasing frequency per number of mutations for the entire cohort of paired participants. Arrows indicate the subclonal trajectory as either growing, stable, or shrinking. The TP53 and ESR1+ regulator pathways were significantly mutated ( q < 0.05; MutSig, Fisher’s method). Stacked bar charts indicate relative contributions of mutational signatures identified by SignatureAnalyzer to the mutational spectrum of each participant. (C) Pathway-level comparison of mutation frequency between four cohorts: intrinsic ( n = 18), acquired ( n = 8), pre-TKI ( n = 55), and post-TKI ( n = 30). Pathways were included if they possessed at least one mutation. There were no significant differences in pathway-level mutation frequency between cohorts (Fisher’s exact test).

    Article Snippet: The remainder of our genomic analysis relied on whole exome sequencing (WES) data generated by the Broad Institute Genomics Platform.

    Techniques: Sequencing, Clone Assay, Functional Assay, Protein-Protein interactions, Comparison, Mutagenesis