Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-5 (of 5 Records) |
Query Trace: Doho G[original query] |
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VPipe: an Automated Bioinformatics Platform for Assembly and Management of Viral Next-Generation Sequencing Data.
Wagner DD , Marine RL , Ramos E , Ng TFF , Castro CJ , Okomo-Adhiambo M , Harvey K , Doho G , Kelly R , Jain Y , Tatusov RL , Silva H , Rota PA , Khan AN , Oberste MS . Microbiol Spectr 2022 10 (2) e0256421 Next-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens. IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance. |
Web-Based Genome Analysis of Bacterial Meningitis Pathogens for Public Health Applications Using the Bacterial Meningitis Genomic Analysis Platform (BMGAP).
Buono SA , Kelly RJ , Topaz N , Retchless AC , Silva H , Chen A , Ramos E , Doho G , Khan AN , Okomo-Adhiambo MA , Hu F , Marasini D , Wang X . Front Genet 2020 11 601870 Effective laboratory-based surveillance and public health response to bacterial meningitis depends on timely characterization of bacterial meningitis pathogens. Traditionally, characterizing bacterial meningitis pathogens such as Neisseria meningitidis (Nm) and Haemophilus influenzae (Hi) required several biochemical and molecular tests. Whole genome sequencing (WGS) has enabled the development of pipelines capable of characterizing the given pathogen with equivalent results to many of the traditional tests. Here, we present the Bacterial Meningitis Genomic Analysis Platform (BMGAP): a secure, web-accessible informatics platform that facilitates automated analysis of WGS data in public health laboratories. BMGAP is a pipeline comprised of several components, including both widely used, open-source third-party software and customized analysis modules for the specific target pathogens. BMGAP performs de novo draft genome assembly and identifies the bacterial species by whole-genome comparisons against a curated reference collection of 17 focal species including Nm, Hi, and other closely related species. Genomes identified as Nm or Hi undergo multi-locus sequence typing (MLST) and capsule characterization. Further typing information is captured from Nm genomes, such as peptides for the vaccine antigens FHbp, NadA, and NhbA. Assembled genomes are retained in the BMGAP database, serving as a repository for genomic comparisons. BMGAP's species identification and capsule characterization modules were validated using PCR and slide agglutination from 446 bacterial invasive isolates (273 Nm from nine different serogroups, 150 Hi from seven different serotypes, and 23 from nine other species) collected from 2017 to 2019 through surveillance programs. Among the validation isolates, BMGAP correctly identified the species for all 440 isolates (100% sensitivity and specificity) and accurately characterized all Nm serogroups (99% sensitivity and 98% specificity) and Hi serotypes (100% sensitivity and specificity). BMGAP provides an automated, multi-species analysis pipeline that can be extended to include additional analysis modules as needed. This provides easy-to-interpret and validated Nm and Hi genome analysis capacity to public health laboratories and collaborators. As the BMGAP database accumulates more genomic data, it grows as a valuable resource for rapid comparative genomic analyses during outbreak investigations. |
Genomic characterization of Haemophilus influenzae: a focus on the capsule locus.
Potts CC , Topaz N , Rodriguez-Rivera LD , Hu F , Chang HY , Whaley MJ , Schmink S , Retchless AC , Chen A , Ramos E , Doho GH , Wang X . BMC Genomics 2019 20 (1) 733 BACKGROUND: Haemophilus influenzae (Hi) can cause invasive diseases such as meningitis, pneumonia, or sepsis. Typeable Hi includes six serotypes (a through f), each expressing a unique capsular polysaccharide. The capsule, encoded by the genes within the capsule locus, is a major virulence factor of typeable Hi. Non-typeable (NTHi) does not express capsule and is associated with invasive and non-invasive diseases. METHODS: A total of 395 typeable and 293 NTHi isolates were characterized by whole genome sequencing (WGS). Phylogenetic analysis and multilocus sequence typing were used to characterize the overall genetic diversity. Pair-wise comparisons were used to evaluate the capsule loci. A WGS serotyping method was developed to predict the Hi serotype. WGS serotyping results were compared to slide agglutination (SAST) or real-time PCR (rt-PCR) serotyping. RESULTS: Isolates of each Hi serotype clustered into one or two subclades, with each subclade being associated with a distinct sequence type (ST). NTHi isolates were genetically diverse, with seven subclades and 125 STs being detected. Regions I and III of the capsule locus were conserved among the six serotypes (>/=82% nucleotide identity). In contrast, genes in Region II were less conserved, with only six gene pairs from all serotypes showing >/=56% nucleotide identity. The WGS serotyping method was 99.9% concordant with SAST and 100% concordant with rt-PCR in determining the Hi serotype. CONCLUSIONS: Genomic analysis revealed a higher degree of genetic diversity among NTHi compared to typeable Hi. The WGS serotyping method accurately predicted the Hi capsule type and can serve as an alternative method for Hi serotyping. |
Whole genome sequencing to characterize capsule locus and predict serogroup of invasive meningococcal isolates.
Marjuki H , Topaz N , Rodriguez-Rivera LD , Ramos E , Potts CC , Chen A , Retchless AC , Doho GH , Wang X . J Clin Microbiol 2018 57 (3) Invasive meningococcal disease is mainly caused by Neisseria meningitidis (Nm) serogroups A, B, C, X, W and Y. Serogroup is typically determined by slide agglutination serogrouping (SASG) and real-time PCR (rt-PCR). We describe a whole-genome sequencing (WGS)-based method to characterize the capsule polysaccharide synthesis (cps) locus, classify Nm serogroups, and identify mechanisms for nongroupability using 453 isolates from a global strain collection. We identified novel genomic organizations within functional cps loci, consisting of insertion-sequence (IS) elements in unique positions that did not disrupt the coding sequence. Genetic mutations (partial gene deletion, missing genes, IS insertion, internal stop, and phase variable off) that led to nongroupability were identified. WGS and SASG were in 91-100% agreement for all serogroups, while WGS and rt-PCR showed 99-100% agreement. Among isolates determined nongroupable by WGS (31 of 453), all three methods agreed 100% for those without a capsule polymerase gene. However, 61% (WGS vs. SASG) and 36% (WGS vs. rt-PCR) agreements were observed for isolates particularly with phase variations or internal stops in cps loci, which warrant further characterization by additional tests. Our WGS-based serogrouping method provides comprehensive characterization of the Nm capsule, which is critical for meningococcal surveillance and outbreak investigations. |
Gene integrated set profile analysis: a context-based approach for inferring biological endpoints.
Kowalski J , Dwivedi B , Newman S , Switchenko JM , Pauly R , Gutman DA , Arora J , Gandhi K , Ainslie K , Doho G , Qin Z , Moreno CS , Rossi MR , Vertino PM , Lonial S , Bernal-Mizrachi L , Boise LH . Nucleic Acids Res 2016 44 (7) e69 The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an 'integrate by intersection' (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance. |
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