Molecular Epidemiological Information System to Support Management of Multidrug-Resistant Tuberculosis in Thailand: Abstract.

OBJECTIVE: To support the End TB strategy with an informatics system that integrates genomic data and the geographic information system (GIS) of Mycobacterium tuberculosis (MTB) clinical isolates. We aim to develop a system prototype for implementing genomic data to support multiple drug-resistant tuberculosis (MDR-TB) control. METHODS: A 12-step data value chain was applied to describe the information flow within the system. A prototyping-oriented system development method was utilized to test the feasibility of certain technical aspects of a system, and as specification tools to determine user requirements. A simulated dataset was entered as input for initial system testing. RESULTS: System prototype, namely Integrated MOL Outbreak detection and Joint investigation (iMoji), was established. The data entry modules consisted of (1) patient registration, (2) sample registration, (3) laboratory data entry and data analysis, and (4) verification and approval of the analyzed data. The initial system test demonstrated connectivity among modules without error. The system was able to report integrated genomic data and GIS information of MDR-TB for clustering analysis. CONCLUSION: iMoji provides an interactive model for determining molecular epidemiological links of MDR-TB and corresponding spatial information to guide public health interventions for tuberculosis control.

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

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients' sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein-protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients' HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

An optimized genomic VCF workflow for precise identification of Mycobacterium tuberculosis cluster from cross-platform whole genome sequencing data.

Whole-genome sequencing (WGS) data allow for an inference of Mycobacterium tuberculosis (Mtb) clusters by using a pairwise genetic distance of ≤12 single nucleotide polymorphisms (SNPs) as a threshold. However, a problem of discrepancies in numbers of SNPs and genetic distance measurement is a great concern when combining WGS data from different next generation sequencing (NGS) platforms. We performed SNP variant calling on WGS data of 9 multidrug-resistant (MDR-TB), 3 extensively drug-resistant tuberculosis (XDR-TB) and a standard M. tuberculosis strain H37Rv from an Illumina/NextSeq500 and an Ion Torrent PGM. Variant calls were obtained using four different common variant calling tools, including Genome Analysis Toolkit (GATK) HaplotypeCaller (GATK-VCF workflow), GATK HaplotypeCaller and GenotypeGVCFs (GATK-GVCF workflow), SAMtools, and VarScan 2. Cross-platform pairwise SNP differences, minimum spanning networks and average nucleotide identity (ANI) were analysed to measure performance of the variant calling tools. Minimum pairwise SNP differences ranged from 2 to 14 SNPs when using GVCF workflow while maximum pairwise SNP differences ranged from 7 to 158 SNPs when using VarScan 2. ANI comparison between SNPs data from NextSeq500 and PGM of MDR-TB and XDR-TB showed maximum ANI of 99.7% and 99.0%, respectively, with GVCF workflow while the other SNP calling results showed lower ANI in a range of 98.6% to 95.1%. In this study, we suggest that the GVCF workflow showed the best performing variant caller to avoid cross-platform pairwise SNP differences.