3 research outputs found
Recommended from our members
Mapping DNA damageâdependent genetic interactions in yeast via party mating and barcode fusion genetics
Abstract Conditionâdependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. Stateâofâtheâart yeast genetic interaction mapping, which relies on robotic manipulation of arrays of doubleâmutant strains, does not scale readily to multiâcondition studies. Here, we describe barcode fusion genetics to map genetic interactions (BFGâGI), by which doubleâmutant strains generated via en masse âpartyâ mating can also be monitored en masse for growth to detect genetic interactions. By using siteâspecific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFGâGI enables multiplexed quantitative tracking of double mutants via nextâgeneration sequencing. We applied BFGâGI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4ânitroquinoline 1âoxide (4NQO), bleomycin, zeocin, and three other DNAâdamaging environments. BFGâGI recapitulated known genetic interactions and yielded new conditionâdependent genetic interactions. We validated and further explored a subnetwork of conditionâdependent genetic interactions involving MAG1,SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to an increase in the activation of the checkpoint protein kinase Rad53
CAGI, the critical assessment of genome interpretation, establishes progress and prospects for computational genetic variant interpretation methods
Background: The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. Results: Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. Conclusions: Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead