Abstract: This disclosure relates generally to identifying candidate genome sequences. Next generation sequencing (NGS) is a massively parallel sequencing technique for identifying candidate genome sequences. The current state-of-the-art techniques for identifying candidate genome sequences does not efficiently address the problem of distributing abundance values across several related strains that are present in the reference under the same species. The disclosed technique proposes a technique for identifying candidate genome sequences by estimating coverage. The disclosed technique includes a local search-based optimization to compute maximum likelihood-based estimates using constrains on coverage/cardinality thresholds for identifying candidate genome sequences.

Abstract: Diagnosis of rare human diseases using DNA sequencing is a fast growing area of research. Conventional methods carries a risk of incorrect phenotype interpretation. However, obtaining a correct genotype and phenotype matching is challenging. A system for matching phenotype descriptions and pathogenic variants provides a one to one mapping of the phenotype and genotypes of a plurality of subjects under test. Initially, a plurality of phenotypes and a plurality of genome sequences are segmented based on metadata. A phenotype driven gene prioritization and a variant prioritization is applied on the segmented data method. A similarity score is calculated between the phenotype driven gene prioritization output and the variant prioritization output. The similarity score is further utilized to obtain a one to one matching of the plurality of phenotypes and the plurality of genotype sequences of the plurality of subjects under test.