Mahdi Sarmady

mtDNA Variation and Analysis Using Mitomap and Mitomaster

The Mitomap database of human mitochondrial DNA (mtDNA) information has been an important compilation of mtDNA variation for researchers, clinicians, and genetic counselors for the past 25 years. The Mitomap protocol shows how users may look up human mitochondrial gene loci, search for public mitochondrial sequences, and browse or search for reported general population nucleotide variants as well as those reported in clinical disease. Within Mitomap is the powerful sequence analysis tool for human mitochondrial DNA, Mitomaster. The Mitomaster protocol gives step‐by‐step instructions showing how to submit sequences to identify nucleotide variants relative to the rCRS, determine the haplogroup, and view species conservation. User‐supplied sequences, GenBank identifiers, and single‐nucleotide variants may be analyzed. Curr. Protoc. Bioinform. 44:1.23.1‐1.23.26.

Efficient digest of high-throughput sequencing data in a reproducible report

BackgroundHigh-throughput sequencing (HTS) technologies are spearheading the accelerated development of biomedical research. Processing and summarizing the large amount of data generated by HTS presents a non-trivial challenge to bioinformatics. A commonly adopted standard is to store sequencing reads aligned to a reference genome in SAM (Sequence Alignment/Map) or BAM (Binary Alignment/Map) files. Quality control of SAM/BAM files is a critical checkpoint before downstream analysis. The goal of the current project is to facilitate and standardize this process.ResultsWe developed bamchop, a robust program to efficiently summarize key statistical metrics of HTS data stored in BAM files, and to visually present the results in a formatted report. The report documents information about various aspects of HTS data, such as sequencing quality, mapping to a reference genome, sequencing coverage, and base frequency. Bamchop uses the R language and Bioconductor packages to calculate statistical matrices and the Sweave utility and associated LaTeX markup for documentation. Bamchops efficiency and robustness were tested on BAM files generated by local sequencing facilities and the 1000 Genomes Project. Source code, instruction and example reports of bamchop are freely available from https://github.com/CBMi-BiG/bamchop.ConclusionsBamchop enables biomedical researchers to quickly and rigorously evaluate HTS data by providing a convenient synopsis and user-friendly reports.

Exome sequencing expands the mechanism of SOX5-associated intellectual disability: A case presentation with review of sox-related disorders

The SOX5 haploinsufficiency syndrome is characterized by global developmental delay, intellectual disability, language and motor impairment, and distinct facial features. The smallest deletion encompassed only one gene, SOX5 (OMIM 604975), indicating that haploinsufficiency of SOX5 contributes to neuro developmental delay. Although multiple deletions of the SOX5 gene have been reported in patients, none are strictly intragenic point mutations. Here, we report the identification of a de novo loss of function variant in SOX5 identified through whole exome sequencing. The proband presented with moderate developmental delay, bilateral optic atrophy, mildly dysmorphic features, and scoliosis, which correlates with the previously‐described SOX5‐associated phenotype. These results broaden the diagnostic spectrum of SOX5‐related intellectual disability. Furthermore it highlights the utility of exome sequencing in establishing an etiological basis in clinically and genetically heterogeneous conditions such as intellectual disability.

CORRIGENDUM: Novel findings with reassessment of exome data: implications for validation testing and interpretation of genomic data

PurposeThe objective of this study was to assess the ability of our laboratory’s exome-sequencing test to detect known and novel sequence variants and identify the critical factors influencing the interpretation of a clinical exome test.MethodsWe developed a two-tiered validation strategy: (i) a method-based approach that assessed the ability of our exome test to detect known variants using a reference HapMap sample, and (ii) an interpretation-based approach that assessed our relative ability to identify and interpret disease-causing variants, by analyzing and comparing the results of 19 randomly selected patients previously tested by external laboratories.ResultsWe demonstrate that this approach is reproducible with >99% analytical sensitivity and specificity for single-nucleotide variants and indels <10 bp. Our findings were concordant with the reference laboratories in 84% of cases. A new molecular diagnosis was applied to three cases, including discovery of two novel candidate genes.ConclusionWe provide an assessment of critical areas that influence interpretation of an exome test, including comprehensive phenotype capture, assessment of clinical overlap, availability of parental data, and the addressing of limitations in database updates. These results can be used to inform improvements in phenotype-driven interpretation of medical exomes in clinical and research settings.

Using large sequencing data sets to refine intragenic disease regions and prioritize clinical variant interpretation

Purpose:Classification of novel variants is a major challenge facing the widespread adoption of comprehensive clinical genomic sequencing and the field of personalized medicine in general. This is largely because most novel variants do not have functional, genetic, or population data to support their clinical classification.Methods:To improve variant interpretation, we leveraged the Exome Aggregation Consortium (ExAC) data set (N = ~60,000) as well as 7,000 clinically curated variants in 132 genes identified in more than 11,000 probands clinically tested for cardiomyopathies, rasopathies, hearing loss, or connective tissue disorders to perform a systematic evaluation of domain level disease associations.Results:We statistically identify regions that are most sensitive to functional variation in the general population and also most commonly impacted in symptomatic individuals. Our data show that a significant number of exons and domains in genes strongly associated with disease can be defined as disease-sensitive or disease-tolerant, leading to potential reclassification of at least 26% (450 out of 1,742) of variants of uncertain clinical significance in the 132 genes.Conclusion:This approach leverages domain functional annotation and associated disease in each gene to prioritize candidate disease variants, increasing the sensitivity and specificity of novel variant assessment within these genes.Genet Med advance online publication 22 September 2016

AUDIOME: a tiered exome sequencing–based comprehensive gene panel for the diagnosis of heterogeneous nonsyndromic sensorineural hearing loss

PurposeHereditary hearing loss is highly heterogeneous. To keep up with rapidly emerging disease-causing genes, we developed the AUDIOME test for nonsyndromic hearing loss (NSHL) using an exome sequencing (ES) platform and targeted analysis for the curated genes.MethodsA tiered strategy was implemented for this test. Tier 1 includes combined Sanger and targeted deletion analyses of the two most common NSHL genes and two mitochondrial genes. Nondiagnostic tier 1 cases are subjected to ES and array followed by targeted analysis of the remaining AUDIOME genes.ResultsES resulted in good coverage of the selected genes with 98.24% of targeted bases at >15 ×. A fill-in strategy was developed for the poorly covered regions, which generally fell within GC-rich or highly homologous regions. Prospective testing of 33 patients with NSHL revealed a diagnosis in 11 (33%) and a possible diagnosis in 8 cases (24.2%). Among those, 10 individuals had variants in tier 1 genes. The ES data in the remaining nondiagnostic cases are readily available for further analysis.ConclusionThe tiered and ES-based test provides an efficient and cost-effective diagnostic strategy for NSHL, with the potential to reflex to full exome to identify causal changes outside of the AUDIOME test.

ExomeSlicer: a resource for the development and validation of exome-based clinical panels

Exome-based panels (exome slices) are becoming the preferred diagnostic strategy in clinical laboratories, especially for genetically heterogeneous disorders. The advantages of this approach include enabling frequent updates to gene content without the need for re-designing, reflexing to exome analysis bioinformatically without requiring additional sequencing, and streamlining laboratory operation by using established exome kits and protocols. Despite their increasing use, there are currently no guidelines or appropriate resources to support their clinical implementation. Here, we highlight principles and important considerations for the clinical development and validation of exome-based panels, guided by clinical data from a diagnostic epilepsy panel using this approach. We also present a novel, publically accessible web-based resource, ExomeSlicer, and demonstrate its clinical utility in predicting gene-specific and exome-wide technically challenging regions that are not amenable to Next Generation Sequencing (NGS), and that might significantly lead to increased post hoc Sanger fill in burden. Using this tool, we also characterize > 2000 low complexity, GC-rich and/or high homology, regions across the exome that can be a source of false positive or false negative variant calls thus potentially leading to misdiagnoses in tested patients.

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

Clinical exome sequencing (CES) has become the preferred diagnostic platform for complex pediatric disorders with suspected monogenic etiologies, solving up to 20%-50% of cases depending on indication. Despite rapid advancements in CES analysis, 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 and subsequent classification. Phenoxome dissects the phenotypic manifestation of a patient in conjunction with their genomic profile to filter and then prioritize putative pathogenic variants. To validate our method, we have compiled a clinical cohort of 105 positive patient samples (i.e. at least one reported ‘pathogenic’ variant) that represent a wide range of genetic heterogeneity from The Children’s Hospital of Philadelphia. Our approach identifies the causative variants within the top 5, 10, or 25 candidates in more than 50%, 71%, or 88% of these patient samples respectively. Furthermore, we show that our method is optimized for clinical testing by yielding superior ranking of the pathogenic variants compared to current state-of-art methods. The web application of Phenoxome is available to the public at http://phenoxome.chop.edu/.