scholarly journals Database Resources of the National Genomics Data Center in 2020

2019 ◽  
Author(s):  
◽  
Zhang Zhang ◽  
Wenming Zhao ◽  
Jingfa Xiao ◽  
Yiming Bao ◽  
...  
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Data Center ◽  
Lower Cost ◽  
Value Added ◽  
Omics Data ◽  
The Past ◽  
Sequencing Technologies ◽  
Research Activities ◽  
Genome Databases

Abstract The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

scholarly journals Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021

2020 ◽  
Vol 49 (D1) ◽  
pp. D18-D28
Author(s):  
◽  
Yongbiao Xue ◽  
Yiming Bao ◽  
Zhang Zhang ◽  
Wenming Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Center ◽  
Database Search ◽  
Easy Access ◽  
Circular Rnas ◽  
Omics Data ◽  
Plant Resources ◽  
The Past ◽  
Research Activities ◽  
One Stop

Abstract The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

scholarly journals Database Resources of the BIG Data Center in 2018

2017 ◽  
Vol 46 (D1) ◽  
pp. D14-D20 ◽  
Author(s):  
◽  
Xingjian Xu ◽  
Lili Hao ◽  
Junwei Zhu ◽  
Bixia Tang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Data Center ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Value Added ◽  
Data Repository ◽  
Biological Knowledge ◽  
Research Activities ◽  
Beijing Institute

Abstract The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn.

LEGAL RESEARCH AT AHMAD IBRAHIM KULLIYYAH OF LAWS, INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA: TRENDS AND POLICIES

2012 ◽  
Vol 18 (1) ◽  
Author(s):  
Hunud Abia Kadouf ◽  
Umar Aimhanosi Oseini ◽  
Ainul Jaria Maidin
Keyword(s):  
Research Output ◽  
Academic Staff ◽  
Research Quality ◽  
Research Progress ◽  
Institutions Of Higher Education ◽  
Legal Research ◽  
The Past ◽  
Research Institution ◽  
Research Activities ◽  
The Status

The primary function of Ahmad Ibrahim Kulliyyah (Faculty) of Laws, at the very beginning of its inception, was that of teaching civil law and Sharî’ah subjects. As it matured, its vision has been varied from teaching to that of research with the aim of attaining the status of a full research institution that provides both quality research and best legal education in the region. Similar to other institutions of higher education in Malaysia, the responsibility of research is a shared function of both graduate students and the academic staff. The research output, on the part of the students is mostly composed of either Master Dissertations or PhD Theses. The academic members of the Faculty, however, are involved either in direct research, individually or jointly, supervision, and publications of their findings. By investigating and analyzing factors influencing research activities at AIKOL in the past twenty years, the researchers will be able to identify the general trends and development of research as it unfolded over years. The researchers hope that the policymakers, at both Faculty and University levels, will use the findings to improve research quality by boldly addressing the problems hampering research progress at AIKOL.

scholarly journals Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

2021 ◽  
Author(s):  
Kevin Chappell ◽  
Kanishka Manna ◽  
Charity L. Washam ◽  
Stefan Graw ◽  
Duah Alkam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Integration ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Biological Pathways ◽  
Omics Data ◽  
Insight Into ◽  
Omics Data Integration

Multi-omics data integration of triple negative breast cancer (TNBC) provides insight into biological pathways.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

scholarly journals MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mario Zanfardino ◽  
Rossana Castaldo ◽  
Katia Pane ◽  
Ornella Affinito ◽  
Marco Aiello ◽  
...  
Keyword(s):  
User Interface ◽  
Data Integration ◽  
Graphical User Interface ◽  
Data Science ◽  
Heterogeneous Data ◽  
Biological Information ◽  
Omics Data ◽  
Correlation Clustering ◽  
Downstream Analysis ◽  
Omics Data Integration

AbstractAnalysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.

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