Storing and analyzing a genome on a blockchain.
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ABSTRACT:
SUBMITTER: Gursoy G
PROVIDER: S-EPMC9241283 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationLarge multiple genome alignments and inferred ancestral genomes are ideal resources for comparative studies of molecular evolution, and advances in sequencing and computing technology are making them increasingly obtainable. These structures can provide a rich understanding of the genetic relationships between all subsets of species they contain. Current formats for storing genomic alignments, such as XMFA and MAF, are all indexed or ordered using a single reference genome, however, which limits the information that can be queried with respect to other species and clades. This loss of information grows with the number of species under comparison, as well as their phylogenetic distance.ResultsWe present HAL, a compressed, graph-based hierarchical alignment format for storing multiple genome alignments and ancestral reconstructions. HAL graphs are indexed on all genomes they contain. Furthermore, they are organized phylogenetically, which allows for modular and parallel access to arbitrary subclades without fragmentation because of rearrangements that have occurred in other lineages. HAL graphs can be created or read with a comprehensive C++ API. A set of tools is also provided to perform basic operations, such as importing and exporting data, identifying mutations and coordinate mapping (liftover).AvailabilityAll documentation and source code for the HAL API and tools are freely available at http://github.com/glennhickey/hal.Contacthickey@soe.ucsc.edu or haussler@soe.ucsc.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3654707 | biostudies-literature
Project description:BackgroundNew "next generation" DNA sequencing technologies offer individual researchers the ability to rapidly generate large amounts of genome sequence data at dramatically reduced costs. As a result, a need has arisen for new software tools for storage, management and analysis of genome sequence data. Although bioinformatic tools are available for the analysis and management of genome sequences, limitations still remain. For example, restrictions on the submission of data and use of these tools may be imposed, thereby making them unsuitable for sequencing projects that need to remain in-house or proprietary during their initial stages. Furthermore, the availability and use of next generation sequencing in industrial, governmental and academic environments requires biologist to have access to computational support for the curation and analysis of the data generated; however, this type of support is not always immediately available.ResultsTo address these limitations, we have developed DraGnET (Draft Genome Evaluation Tool). DraGnET is an open source web application which allows researchers, with no experience in programming and database management, to setup their own in-house projects for storing, retrieving, organizing and managing annotated draft and complete genome sequence data. The software provides a web interface for the use of BLAST, allowing users to perform preliminary comparative analysis among multiple genomes. We demonstrate the utility of DraGnET for performing comparative genomics on closely related bacterial strains. Furthermore, DraGnET can be further developed to incorporate additional tools for more sophisticated analyses.ConclusionsDraGnET is designed for use either by individual researchers or as a collaborative tool available through Internet (or Intranet) deployment. For genome projects that require genome sequencing data to initially remain proprietary, DraGnET provides the means for researchers to keep their data in-house for analysis using local programs or until it is made publicly available, at which point it may be uploaded to additional analysis software applications. The DraGnET home page is available at http://www.dragnet.cvm.iastate.edu and includes example files for examining the functionalities, a link for downloading the DraGnET setup package and a link to the DraGnET source code hosted with full documentation on SourceForge.
| S-EPMC3098051 | biostudies-literature
Project description:Two major stumbling blocks exist in high-throughput sequencing (HTS) data analysis. The first is the sheer file size, typically in gigabytes when uncompressed, causing problems in storage, transmission, and analysis. However, these files do not need to be so large, and can be reduced without loss of information. Each HTS file, either in compressed .SRA or plain text .fastq format, contains numerous identical reads stored as separate entries. For example, among 44,603,541 forward reads in the SRR4011234.sra file (from a Bacillus subtilis transcriptomic study) deposited at NCBI's SRA database, one read has 497,027 identical copies. Instead of storing them as separate entries, one can and should store them as a single entry with the SeqID_NumCopy format (which I dub as FASTA+ format). The second is the proper allocation of reads that map equally well to paralogous genes. I illustrate in detail a new method for such allocation. I have developed ARSDA software that implement these new approaches. A number of HTS files for model species are in the process of being processed and deposited at http://coevol.rdc.uottawa.ca to demonstrate that this approach not only saves a huge amount of storage space and transmission bandwidth, but also dramatically reduces time in downstream data analysis. Instead of matching the 497,027 identical reads separately against the B. subtilis genome, one only needs to match it once. ARSDA includes functions to take advantage of HTS data in the new sequence format for downstream data analysis such as gene expression characterization. I contrasted gene expression results between ARSDA and Cufflinks so readers can better appreciate the strength of ARSDA. ARSDA is freely available for Windows, Linux. and Macintosh computers at http://dambe.bio.uottawa.ca/ARSDA/ARSDA.aspx.
| S-EPMC5714481 | biostudies-literature
Project description:SummaryWe introduce ISDB, a set of software tools for the creation and administration of relational databases of viral integration site (IS) data. Using ISDB, investigators can curate a private database from any heterogeneous set of data sources, including previously-published datasets and internal, work-in-progress data. To make data visible and accessible to collaborators with varying degrees of computational expertise, ISDB automatically generates web sites describing database contents and data exports in several common formats. Compared to a public depository database, the ability to build local, private databases makes ISDB suitable for use in testing hypotheses and developing analyses in the long pre-publication phase of most research.Availability and implementationInstallation and usage documentation for ISDB are provided on our website https://mullinslab.microbiol.washington.edu/isdb/. Source code is available under the open source MIT license from https://github.com/MullinsLab/ISDB.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6419909 | biostudies-literature
Project description:Blockchain is a recent revolutionary technology primarily associated with cryptocurrencies. It has many unique features including its acting as a decentralized, immutable, shared, and distributed ledger. Blockchain can store all types of data with better security. It avoids third-party intervention to ensure better security of the data. Deep learning is another booming field that is mostly used in computer applications. This work proposes an integrated environment of a blockchain-deep learning environment for analyzing the Electronic Health Records (EHR). The EHR is the medical documentation of a patient which can be shared among hospitals and other public health organizations. The proposed work enables a deep learning algorithm act as an agent to analyze the EHR data which is stored in the blockchain. This proposed integrated environment can alert the patients by means of a reminder for consultation, diet chart, etc. This work utilizes the deep learning approach to analyze the EHR, after which an alert will be sent to the patient's registered mobile number.
| S-EPMC9122032 | biostudies-literature
Project description:Viable bacterial communities in fresh pumped human milk and their changes during cold storage conditions
| PRJEB76042 | ENA
Project description:BackgroundBlockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine.Basic proceduresWe generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing.Main findingsWe achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.Principal conclusionsWe show the benchmarking results of novel blockchain-based methods for institutions to share patient outcomes related to gene-drug interactions. Our findings support blockchain utilization in healthcare, genomic and biomedical applications. The source code is publicly available at https://github.com/tsungtingkuo/genedrug.
| S-EPMC9933142 | biostudies-literature
Project description:Since the discovery of the presence of biogenic magnetites in living organisms, there have been speculations on the role that these biomagnetites play in cellular processes. It seems that the formation of biomagnetite crystals is a universal phenomenon and not an exception in living cells. Many experimental facts show that features of organic and inorganic processes could be indistinguishable at nanoscale levels. Living cells are quantum "devices" rather than simple electronic devices utilizing only the charge of conduction electrons. In our opinion, due to their unusual biophysical properties, special biomagnetites must have a biological function in living cells in general and in the brain in particular. In this paper, we advance a hypothesis that while biomagnetites are developed jointly with organic molecules and cellular electromagnetic fields in cells, they can record information about the Earth's magnetic vector potential of the entire flight in migratory birds.
| S-EPMC2791810 | biostudies-other
Project description:Reference genome sequences have become key platforms for genetics and breeding of the major crop species. Sugarcane is probably the largest crop produced in the world (in weight of crop harvested) but lacks a reference genome sequence. Sugarcane has one of the most complex genomes in crop plants due to the extreme level of polyploidy. The genome of modern sugarcane hybrids includes sub-genomes from two progenitors Saccharum officinarum and S. spontaneum with some chromosomes resulting from recombination between these sub-genomes. Advancing DNA sequencing technologies and strategies for genome assembly are making the sugarcane genome more tractable. Advances in long read sequencing have allowed the generation of a more complete set of sugarcane gene transcripts. This is supporting transcript profiling in genetic research. The progenitor genomes are being sequenced. A monoploid coverage of the hybrid genome has been obtained by sequencing BAC clones that cover the gene space of the closely related sorghum genome. The complete polyploid genome is now being sequenced and assembled. The emerging genome will allow comparison of related genomes and increase understanding of the functioning of this polyploidy system. Sugarcane breeding for traditional sugar and new energy and biomaterial uses will be enhanced by the availability of these genomic resources.
| S-EPMC5961476 | biostudies-literature
Project description: Not available
| S-EPMC5242601 | biostudies-literature