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High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis.

ABSTRACT: The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure.The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed. Database URL: https://hive.biochemistry.gwu.edu.

SUBMITTER: Simonyan V

PROVIDER: S-EPMC4795927 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Project description:Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies. Database URL: BioMuta: http://hive.biochemistry.gwu.edu/tools/biomuta/index.php; CSR: http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr; HIVE: http://hive.biochemistry.gwu.edu.

Project description:BACKGROUND: Mitochondrial disorders can originate from mutations in one of many nuclear genes controlling the organelle function or in the mitochondrial genome (mitochondrial DNA (mtDNA)). The large numbers of potential culprit genes, together with the little guidance offered by most clinical phenotypes as to which gene may be causative, are a great challenge for the molecular diagnosis of these disorders. METHODS: We developed a novel targeted resequencing assay for mitochondrial disorders relying on microarray-based hybrid capture coupled to next-generation sequencing. Specifically, we subjected the entire mtDNA genome and the exons and intron-exon boundary regions of 362 known or candidate causative nuclear genes to targeted capture and resequencing. We here provide proof-of-concept data by testing one HapMap DNA sample and two positive control samples. RESULTS: Over 94% of the targeted regions were captured and sequenced with appropriate coverage and quality, allowing reliable variant calling. Pathogenic mutations blindly tested in patients' samples were 100% concordant with previous Sanger sequencing results: a known mutation in Pyruvate dehydrogenase alpha 1 subunit (PDHA1), a novel splicing and a known coding mutation in Hydroxyacyl-CoA dehydrogenase alpha subunit (HADHA) were correctly identified. Of the additional variants recognized, 90 to 94% were present in dbSNP while 6 to 10% represented new alterations. The novel nonsynonymous variants were all in heterozygote state and mostly predicted to be benign. The depth of sequencing coverage of mtDNA was extremely high, suggesting that it may be feasible to detect pathogenic mtDNA mutations confounded by low level heteroplasmy. Only one sequencing lane of an eight lane flow cell was utilized for each sample, indicating that a cost-effective clinical test can be achieved. CONCLUSIONS: Our study indicates that the use of next generation sequencing technology holds great promise as a tool for screening mitochondrial disorders. The availability of a comprehensive molecular diagnostic tool will increase the capacity for early and rapid identification of mitochondrial disorders. In addition, the proposed approach has the potential to identify new mutations in candidate genes, expanding and redefining the spectrum of causative genes responsible for mitochondrial disorders.

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High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis.

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