Project description:A major challenge in genomics is deciphering the functional consequences of non-coding genetic variation. Here we created AlphaGenome Atlas, which enables the joint interpretation and prioritization of variant effects across the entire human genome. Using AlphaGenome, we predicted the regulatory effects of every possible human single nucleotide variant and many common indels. These predictions were then used to derive a unified and interpretable AlphaGenome Variant Impact (AVI) score and to discover and annotate cis-regulatory motifs across the genome. On clinical, complex trait and rare disease benchmarks, AVI achieved state-of-the-art performance especially on non-coding variants, enabling us to solve an epilepsy rare disease case. Application of Atlas including AVI increased the statistical power and interpretability for rare non-coding variants driving population-level phenotypes. Thus, AlphaGenome Atlas improves the prioritization and molecular interpretation of non-coding variants with genetic and clinical significance.
Project description:Metastatic melanoma patients carrying a BRAFV600 mutation can be treated with BRAF inhibitors (BRAFi), in combination with MEK inhibitors (MEKi), but innate and acquired resistance invariably occurs. Innate resistance can involve transcriptional and epigenetic-based phenotypic adaptations, remaining yet unpredictable. We describe here the development of a highly efficient patient-derived xenograft model adapted to patient melanoma biopsies, using the avian embryo as a host (AVI-PDXTM). In this particular paradigm, we depict a fast and reproducible tumor intake of patient samples within the embryonic skin, preserving key molecular and phenotypical features. We provide the proof of concept that the AVI-PDXTM allows modeling melanoma patient diversity of responses to BRAFi/MEKi, in very short delays, hence positioning it as a valuable tool for the design of personalized medicine assays.
Project description:Illumina 1M Omni Quad arrays were used to test mutation calling accuracy of qSNP tool (a mutation caller) Ilumina array genotypes with GenCal (GC score)>0.70 were used in the comparison of genotype calls using next generation sequencing data and qSNP (mutation caller) 2 samples (control cell line and Melanoma cell line). This is the data for a validation step. contributor: Australian Pancreatic Cancer Genome Initiative