scholarly journals Norwegian e-Infrastructure for Life Sciences (NeLS)

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 968 ◽  
Author(s):  
Kidane M. Tekle ◽  
Sveinung Gundersen ◽  
Kjetil Klepper ◽  
Lars Ailo Bongo ◽  
Inge Alexander Raknes ◽  
...  
Keyword(s):  
Data Storage ◽  
High Throughput Sequencing ◽  
Life Sciences ◽  
Large Data ◽  
Sequencing Data ◽  
Sensitive Data ◽  
Help Desk ◽  
Web Interfaces ◽  
The Galaxy ◽  
University Of Oslo

The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs).  NeLS has been in production since 2015, with training and support provided by the help desk of ELIXIR Norway. Through collaboration with NorSeq, the national consortium for high-throughput sequencing, an integrated service is offered so that sequencing data generated in a research project is provided to the involved researchers through NeLS. Sensitive data, such as individual genomic sequencing data, are handled using the TSD (Services for Sensitive Data) platform provided by Sigma2 and the University of Oslo. NeLS integrates national e-infrastructure storage and computing resources, and is also integrated with the SEEK platform in order to store large data files produced by experiments described in SEEK.   In this article, we outline the architecture of NeLS and discuss possible directions for further development.

scholarly journals Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yuhua Fu ◽  
Pengyu Fan ◽  
Lu Wang ◽  
Ziqiang Shu ◽  
Shilin Zhu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Target Genes ◽  
Target Prediction ◽  
Large Data ◽  
Sequencing Data ◽  
Regulate Gene Expression ◽  
High Throughput Sequencing Data ◽  
Annotation Information ◽  
Public Data ◽  
Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

scholarly journals PIRD: Pan Immune Repertoire Database

2019 ◽  
Author(s):  
Wei Zhang ◽  
Longlong Wang ◽  
Ke Liu ◽  
Xiaofeng Wei ◽  
Kai Yang ◽  
...  
Keyword(s):  
Data Storage ◽  
High Throughput Sequencing ◽  
Homo Sapiens ◽  
Adaptive Immune System ◽  
B Cell Receptors ◽  
Sequencing Data ◽  
Immune Repertoire ◽  
Adaptive Immune ◽  
Potential Applications ◽  
Antibody Drug

Abstract Motivation T and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database. Results We developed a database, the Pan Immune Repertoire Database (PIRD), located in China National GeneBank (CNGBdb), to collect and store annotated TCR and BCR sequencing data, including from Homo sapiens and other species. In addition to data storage, PIRD also provides functions of data visualization and interactive online analysis. Additionally, a manually curated database of TCRs and BCRs targeting known antigens (TBAdb) was also deposited in PIRD. Availability and implementation PIRD can be freely accessed at https://db.cngb.org/pird.

scholarly journals A Beginner’s Guide on Integrating *Omics Approaches to Study Marine Microbial Communities: Details and Discussions From Sample Collection to Bioinformatics Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Sabrina Natalie Wilms
Keyword(s):  
Single Cell ◽  
Data Storage ◽  
High Throughput Sequencing ◽  
Marine Ecosystem ◽  
Cloud Services ◽  
Small Scale ◽  
Species Differentiation ◽  
Sequencing Data ◽  
The World ◽  
Microbial Organisms

The variety of Earth’s organisms is manifold. However, it is the small-scale marine community that makes the world goes round. Microbial organisms of pro- and eukaryotic origin drive the carbon supply and nutrient cycling, thus are mediating the primary productivity within the world largest ecosystem called ocean. But due to the ocean’s great size and large number of biogeographically habitats, the total of microbial species can hardly be grabbed and therefore their functional roles not fully described. However, recent advances in high-throughput sequencing technologies are revolutionizing our understanding of the marine microbial diversity, ecology and evolution. Nowadays, research questions on species differentiation can be solved with genomic approaches such as metabarcoding, while transcriptomics offers the possibility to assign gene functions even to a single cell, e.g., single-cell transcriptomics. On the other hand, due to the diversified amount of sequencing data, the certainty of a data crisis is currently evolving. Scientists are forced to broaden their view on bioinformatics resources for analysis and data storage in from of, e.g., cloud services, to ensure the data’s exchangeability. Which is why time resources are now shifting toward solving data problems rather than answering the eco-evolutionary questions stated in the first place. This review is intended to provide exchange on *omics approaches and key points for discussions on data handling used to decipher the relevant diversity and functions of microbial organisms in the marine ecosystem.

scholarly journals V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput sequencing data

2020 ◽  
Author(s):  
Susana Posada-Céspedes ◽  
David Seifert ◽  
Ivan Topolsky ◽  
Karin J. Metzner ◽  
Niko Beerenwinkel
Keyword(s):  
Genetic Diversity ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Viral Infections ◽  
Markov Models ◽  
Large Data ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Bioinformatics Pipeline ◽  
The Impact

AbstractHigh-throughput sequencing technologies are used increasingly, not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence, and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations. To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape. V-pipe is freely available at https://github.com/cbg-ethz/V-pipe.

scholarly journals Broom: Application for non-redundant storage of High Throughput Sequencing data

2018 ◽  
Author(s):  
Levent Albayrak ◽  
Kamil Khanipov ◽  
George Golovko ◽  
Yuriy Fofanov
Keyword(s):  
Data Storage ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Data Generation ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Sequencing Quality ◽  
Redundant Storage ◽  
Recent Trends ◽  
The Cost

AbstractMotivationThe data generation capabilities of High Throughput Sequencing (HTS) instruments have exponentially increased over the last few years, while the cost of sequencing has dramatically decreased allowing this technology to become widely used in biomedical studies. For small labs and individual researchers, however, storage and transfer of large amounts of HTS data present a significant challenge. The recent trends in increased sequencing quality and genome coverage can be used to reconsider HTS data storage strategies.ResultsWe present Broom, a stand-alone application designed to select and store only high-quality sequencing reads at extremely high compression rates. Written in C++, the application accepts single and paired-end reads in FASTQ and FASTA formats and decompresses data in FASTA format.AvailabilityC++ code available at https://scsb.utmb.edu/labgroups/fofanov/[email protected]

scholarly journals PEGR: a flexible management platform for reproducible epigenomic and genomic research

2021 ◽  
Author(s):  
Danying Shao ◽  
Gretta Kellogg ◽  
Ali Nematbakhsh ◽  
Prashant Kuntala ◽  
Shaun Mahony ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Genomic Research ◽  
Sequencing Data ◽  
Web Based ◽  
Management Platform ◽  
Significant Challenge ◽  
High Throughput Sequencing Data ◽  
The Galaxy ◽  
Flexible Management

Reproducibility is a significant challenge in (epi)genomic research due to the complexity of experiments composed of traditional biochemistry and informatics. Recent advances have exacerbated this challenge as high-throughput sequencing data is generated at an unprecedented pace. Here we report on our development of a Platform for Epi-Genomic Research (PEGR), a web-based project management platform that tracks and quality controls experiments from conception to publication-ready figures, compatible with multiple assays and bioinformatic pipelines. It supports rigor and reproducibility for biochemists working at the wet bench, while continuing to fully support reproducibility and reliability for bioinformaticians through integration with the Galaxy platform.

scholarly journals PIRD: Pan immune repertoire database

2018 ◽  
Author(s):  
Wei Zhang ◽  
Longlong Wang ◽  
Ke Liu ◽  
Xiaofeng Wei ◽  
Kai Yang ◽  
...  
Keyword(s):  
Data Storage ◽  
High Throughput Sequencing ◽  
Homo Sapiens ◽  
Adaptive Immune System ◽  
B Cell Receptors ◽  
Data Visualisation ◽  
Sequencing Data ◽  
Immune Repertoire ◽  
Adaptive Immune ◽  
Potential Applications

ABSTRACTMotivationT and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database.ResultsWe developed a database, the Pan Immune Repertoire Database (PIRD), located in China National GeneBank (CNGBdb), to collect and store annotated TCR and BCR sequencing data, including from Homo sapiens and other species. In addition to data storage, PIRD also provides functions of data visualisation and interactive online analysis. Additionally, a manually curated database of TCRs and BCRs targeting known antigens (TBAdb) was also deposited in PIRD.Availability and ImplementationPIRD can be freely accessed at https://db.cngb.org/pird.

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

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