There is a fast growing interest in clinical genetics to the utilization of High Throughput Sequencing data for accurate diagnosis of monogenic diseases. To improve the identification of the variants from HTS, we developed VariantMaster, an original program that accurately and efficiently extracts causative variants in familial and sporadic genetic diseases. The algorithm takes into account predicted variants (SNPs and indels) in affected individuals or tumor samples and utilizes the row (BAM) data to robustly estimate the conditional probability of segregation in a family, as well as the probability of it being de novo or somatic. In familial cases, various modes of inheritance are considered: X-linked, autosomal dominant, and recessive (homozygosity or compound heterozygosity). Moreover, VariantMaster integrates phenotypes and genotypes, and employs Annovar to produce additional information as allelic frequencies in general population and damaging scores.
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