Rapid and accurate interpretation of clinical exomes using Phenoxome: a computational phenotype-driven approach

Abstract

Clinical exome sequencing (CES) has become the preferred diagnostic platform for complex pediatric disorders with suspected monogenic etiologies. Despite rapid advancements, the major challenge still resides in identifying the casual variants among the thousands of variants detected during CES testing, and thus establishing a molecular diagnosis. To improve the clinical exome diagnostic efficiency, we developed Phenoxome, a robust phenotype-driven model that adopts a network-based approach to facilitate automated variant prioritization. Phenoxome dissects the phenotypic manifestation of a patient in concert with their genomic profile to filter and then prioritize variants that are likely to affect the function of the gene (potentially pathogenic variants). To validate our method, we have compiled a clinical cohort of 105 positive patient samples that represent a wide range of genetic heterogeneity. Phenoxome identifies the causative variants within the top 5, 10, or 25 candidates in more than 50%, 71%, or 88% of these exomes, respectively. Furthermore, we show that our method is optimized for clinical testing by outperforming the current state-of-art method. We have demonstrated the performance of Phenoxome using a clinical cohort and showed that it enables rapid and accurate interpretation of clinical exomes. Phenoxome is available at https://phenoxome.chop.edu/.

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

“…Many other HPO-driven variant prioritization tools exist [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], all primarily tested only on (different) simulated datasets with limited software performance comparison. Further work should include a systematic and exhaustive software performance comparison on both simulated and real patient large datasets.…”

“…In order to overcome these difficulties, another generation of (freely available) tools have been developed that incorporate a rare disease patient's phenotype into the interpretation of their sequencing data. These include eXtasy [34], BiERapp [35], Phen-Gen [36], Exomiser [37], Phevor [38], PhenoVar [39], PhenIX [40], OVA [41], Phenolyzer [42], wANNOVAR [43], OMIM Explorer [44], QueryOR [45], GenIO [46], DeepPVP [47], MutationDistiller [48], Phrank [49], Xrare [50], PhenoPro [51], and Phenoxome [52]. Such phenotype-driven prioritization tools leverage existing genotype to phenotype information from various databases in order to prioritize candidate variants in those genes that are likely to be more relevant to the patient's phenotype.…”

Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing

“…A pseudo-score based on the ENCODE mappability track (32) was derived to assess sequence uniqueness of each exon (33) . Variants from computationally inferred pseudo regions were marked in the clinical bioinformatics pipeline.…”

Dominant-negative mutations inCBX1cause a neurodevelopmental disorder

“…Her diagnostic work-up has included a normal brain MRI, normal female karyotype, normal Fragile X molecular testing, normal Prader-Willi/Angelman methylation analysis, and a normal SNP microarray result. Clinical trio exome sequencing was performed [10][11][12], and a de novo heterozygous single nucleotide variant in CBX1 was identified, predicted to result in a missense change, NM_001127228.1: c.169A>G, p.(Asn57Asp). Individual 2 was a 5 year old boy born to non-consanguineous parents of European origins.…”

“…Individual 1: Exome sequencing was performed as previously described [10][11][12]. Briefly, Genomic DNA was extracted from peripheral blood following standard DNA extraction protocols.…”