Prioritizing Disease-Linked Variants, Genes, and Pathways with an Interactive Whole-Genome Analysis Pipeline

Abstract

Whole genome sequencing (WGS) studies are uncovering disease-associated variants in both rare and non-rare diseases. Utilizing the next-generation sequencing for WGS requires a series of computational methods for alignment, variant detection, and annotation, and the accuracy and reproducibility of annotation results are essential for clinical implementation. However, annotating WGS with up to date genomic information is still challenging for biomedical researchers. Here we present one of the fastest and highly scalable annotation, filtering, and analysis pipeline –gNOME – to prioritize phenotype-associated variants while minimizing false positive findings. Intuitive graphical user interface of gNOME facilitates the selection of phenotype associated variants, and the result summaries are provided at variant-, gene-, and genome-levels. Moreover, the enrichment results of specific variants, genes, and gene sets between two groups or compared to population scale WGS datasets that is already integrated in the pipeline can help the interpretation. We found a small number of discordant results between annotation software tools in part due to different reporting strategies for the variants with complex impacts. Using two published whole exome datasets of uveal melanoma and bladder cancer, we demonstrated gNOME's accuracy of variant annotation and the enrichment of loss of function variants in known cancer pathways. gNOME web-server and source codes are freely available to the academic community.

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

“…DisGeNET platform has been used to study a variety of biomedical problems, which include investigating the molecular basis of specific diseases (33–36), annotating lists of genes produced by different types of omics and sequencing protocols (37–39), validating disease genes prediction methods (40–42), understanding disease mechanisms in the context of protein networks (43,44), gaining insight into drug action (45) and drug adverse reactions mechanisms (46), drug repurposing (47), exploring the molecular basis of disease comorbidities (48,49), assessing the performance of text-mining algorithms (50) and as part of other resources (51–53). …”

Exome sequencing identifies novel and recurrent mutations in GJA8 and CRYGD associated with inherited cataract

“…com) or the gNOME project pipeline (http://gnome.tchlab. org/) [29]. Identified variants in the pre-selected candidate genes (Additional file 1) were then reviewed for presence/ absence and frequency in various websites including dbSNP (http://www.ncbi.nlm.nih.gov/snp/), 1000 genomes (http:// www.1000genomes.org/), and the Exome Variant Server database (http://evs.gs.washington.edu/EVS/).…”

VCF‐Server: A web‐based visualization tool for high‐throughput variant data mining and management

“…Recently, many efforts have been made toward developing graphical tools to process VCF files for researchers with limited bioinformatics background. Tools like SNVerGUI (W. Wang, Hu, Hou, Hu, & Wei, ), database.bio (Ou et al, ), DaMold (Pandey, Pabinger, Kriegner, & Weinhausel, ), mirVAFC (Li et al, ), GAVIN (van der Velde et al, ), and gNOME (Lee et al, ) have been developed to help nonbioinformaticians prioritize variants. However, they share the disadvantage of depending on a well‐defined set of annotations, discarding user‐defined annotations in VCFs and thus limiting the user to predefined features.…”