The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank((R)) nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. The Entrez system provides search and retrieval operations for most of these data from 37 distinct databases. The E-utilities serve as the programming interface for the Entrez system. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. New resources released in the past year include iCn3D, MutaBind, and the Antimicrobial Resistance Gene Reference Database; and resources that were updated in the past year include My Bibliography, SciENcv, the Pathogen Detection Project, Assembly, Genome, the Genome Data Viewer, BLAST and PubChem. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Abstract Genetic variation analysis plays an important role in elucidating the causes of various human diseases. The drastically reduced costs of genome sequencing driven by next generation sequence technologies now make it possible to analyze genetic variations with hundreds or thousands of samples simultaneously, but with the cost of ever increasing local storage requirements. The tera- and peta-byte scale footprint for sequence data imposes significant technical challenges for data management and analysis, including the tasks of collection, storage, transfer, sharing, and privacy protection. Currently, each analysis group must download all the relevant sequence data into a local file system before variation analysis is initiated. This heavy-weight transaction not only slows down the pace of the analysis, but also creates financial burdens for researchers due to the cost of hardware and time required to transfer the data over typical academic internet connections. To overcome such limitations and explore the feasibility of analyzing control-accessed sequencing data in cloud environment while maintaining data privacy and security, here we introduce a cloud-based analysis framework that facilitates variation analysis using direct access to the NCBI Sequence Read Archive through NCBI SRA Toolkit, which allows the users to programmatically access data housed within SRA with encryption and decryption capabilities and converts it from the SRA format to the desired format for data analysis. A customized machine image (ngs-swift) with preconfigured tools, including NCBI SRA Toolkit and NGS Software Development Kit, and resources essential for variant analysis has been created for instantiating an EC2 instance or instance cluster on Amazon cloud. Performance of this framework has been evaluated using dbGaP study phs000710.v1.p1 (1000Genome Dataset in dbGaP, http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id = phs000710.v1.p1), and compared with that from traditional analysis pipeline, and security handling in cloud environment when dealing with control-accessed sequence data has been addressed. We demonstrate that with this framework, it is cost effective to make variant calls without first transferring the entire set of aligned sequence data into a local storage environment, thereby accelerating variant discovery using control-accessed sequencing data. Citation Format: Chunlin Xiao, Eugene Yaschenko, Stephen Sherry. NGS-SWIFT: A cloud-based variant analysis framework using control-accessed sequencing data from dbGaP/SRA. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5278.

Abstract Variation analysis plays an important role in elucidating the causes of various human diseases. The drastically reduced costs of genome sequencing driven by next generation sequence technologies now make it possible to analyze genetic variations with hundreds or thousands of samples simultaneously, but currently with the cost of ever increasing local storage requirements. The tera- and peta-byte scale footprint for sequence data imposes significant technical challenges for data management and analysis, including the tasks of collection, storage, transfer, sharing, and privacy protection. Currently, each analysis group facing these analysis tasks must download all the relevant sequence data into a local file system before variation analysis is initiated. This heavy-weight transaction not only slows down the pace of the analysis, but also creates financial burdens for researchers due to the cost of hardware and time required to transfer the data over typical academic internet connections. To overcome such limitations and explore the feasibility of analyzing control-accessed sequencing data in cloud environment while maintaining data privacy and security, here we introduce a cloud-based analysis framework that facilitates variation analysis using direct access to the NCBI Sequence Read Archive through NCBI sratoolkit, which allows the users to programmatically access data housed within SRA with encryption and decryption capabilities and converts it from the SRA format to the desired format for data analysis. A customized machine image (swift) with preconfigured tools (including NCBI sratoolkit) and resources essential for variant analysis has been created for instantiating an EC2 instance or instance cluster on Amazon cloud. Performance of this framework has been evaluated and compared with that from traditional analysis pipeline, and security handling in cloud environment when dealing with control-accessed sequence data has been addressed. We demonstrate that it is cost effective to make variant calls using control-accessed SRA sequence data without first transferring the entire set of aligned sequence data into a local storage environment, thereby accelerating variation discovery using control-accessed sequencing data. Citation Format: Chunlin Xiao, Eugene Yaschenko, Stephen Sherry. Cloud-based variant analysis solution using control-accessed sequencing data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4858. doi:10.1158/1538-7445.AM2015-4858

We assembled teams of genomics professionals to assess whether we could rapidly develop pipelines to answer biological questions commonly asked by biologists and others new to bioinformatics by facilitating analysis of high-throughput sequencing data. In January 2015, teams were assembled on the National Institutes of Health (NIH) campus to address questions in the DNA-seq, epigenomics, metagenomics and RNA-seq subfields of genomics. The only two rules for this hackathon were that either the data used were housed at the National Center for Biotechnology Information (NCBI) or would be submitted there by a participant in the next six months, and that all software going into the pipeline was open-source or open-use. Questions proposed by organizers, as well as suggested tools and approaches, were distributed to participants a few days before the event and were refined during the event. Pipelines were published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development (https://github.com/features/). The code was published at https://github.com/DCGenomics/ with separate repositories for each team, starting with hackathon_v001.

Abstract The per-base cost of whole genome sequencing has dropped dramatically due to recent advancement of next generation sequencing (NGS) technologies, and a number of large-scale re-sequencing projects, e.g. 1000 Genomes, ICGC, TCGA, GO-ESP, and CGCI etc., have been initiated to extend our knowledge of single nucleotide polymorphisms (SNPs), short insertions/deletions (INDELs) and structural variations (SVs), and relate these variants to human diseases. To date, various compression technologies, including NCBI's cSRA and EBI's CRAM etc., have been developed to significantly reduce the storage footprints and costs. However, whether or not such compression treatments have any impact on genetic variant callings has not been systematically assessed and validated with large scale NGS datasets as different calling algorithms for different variant classes (SNPs, INDELs, or SVs) may have different underlying data requirements. We have developed an integrated analysis framework (VarPipe) to profile genetic mutations using NGS data generated from various sequencing platforms in a uniform manner. This pipeline is also applied to the official data from 1000Genomes (normal samples) and Cancer Genome Characterization Initiative (tumor samples) projects to evaluate the compressions effects on variation callers. With defined quality metrics, the variant callsets from compression-treated and untreated data are carefully evaluated as well as compared to the official variation releases of the corresponding projects so that optimal compression strategy can be developed and recommended for variation detection using NGS data. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3983. doi:1538-7445.AM2012-3983

As the volume and complexity of data sets archived at NCBI grow rapidly, so does the need to gather and organize the associated metadata. Although metadata has been collected for some archival databases, previously, there was no centralized approach at NCBI for collecting this information and using it across databases. The BioProject database was recently established to facilitate organization and classification of project data submitted to NCBI, EBI and DDBJ databases. It captures descriptive information about research projects that result in high volume submissions to archival databases, ties together related data across multiple archives and serves as a central portal by which to inform users of data availability. Concomitantly, the BioSample database is being developed to capture descriptive information about the biological samples investigated in projects. BioProject and BioSample records link to corresponding data stored in archival repositories. Submissions are supported by a web-based Submission Portal that guides users through a series of forms for input of rich metadata describing their projects and samples. Together, these databases offer improved ways for users to query, locate, integrate and interpret the masses of data held in NCBI's archival repositories. The BioProject and BioSample databases are available at http://www.ncbi.nlm.nih.gov/bioproject and http://www.ncbi.nlm.nih.gov/biosample, respectively.

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Electronic PCR, OrfFinder, Splign, ProSplign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, dbVar, Epigenomics, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Trace Archive, Sequence Read Archive, Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), IBIS, Biosystems, Peptidome, OMSSA, Protein Clusters and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov .

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the web applications is custom implementation of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

The National Center for Biotechnology Information has created the dbGaP public repository for individual-level phenotype, exposure, genotype and sequence data and the associations between them. dbGaP assigns stable, unique identifiers to studies and subsets of information from those studies, including documents, individual phenotypic variables, tables of trait data, sets of genotype data, computed phenotype-genotype associations, and groups of study subjects who have given similar consents for use of their data.

Scientists have dedicated considerable effort to decoding the genomes of an ever-growing list of species, ranging from small viruses, whose genomes may be just a few thousand nucleotides in length, to large mammalian genomes, three billion nucleotides and larger. Many aspects of life science research have benefited by the accumulation of these data, but decoded genomes could be even more valuable if important information about the genome sequence, currently being lost, were preserved. Occasionally, questions arise about a specific position in the sequence—or a variant in the sequence is observed in a new sample. At times like these, it would be helpful to be able to go back to the experimental evidence that underlies the genome sequence at that position, to see if there is any ambiguity or uncertainty about the sequence. As things stand, that's almost impossible. To understand why this is the case, it is necessary to know a bit more about how a genome sequence is put together.