Data Integration in Bioinformatics: Current Efforts and Challenges

Zhang, Zhang; Bajić, Vladimir B.; Yu, Jun; Cheung, Kei‐Hoi; Townsend, Jeffrey P.

doi:10.5772/21654

Cited by 30 publications

(21 citation statements)

References 132 publications

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“…To address the most important and complex biological questions, it is often required to provide researchers with open access to various data resources (2). Nowadays, China has become a powerhouse in generating vast quantities of biological data, but is in the embarrassing situation of lacking a centralized data center that is committed to opening data in this big data world and to making data well-organized and publicly accessible to worldwide scientific communities (3).…”

Section: Introductionmentioning

confidence: 99%

The BIG Data Center: from deposition to integration to translation

2016

Nucleic Acids Res

459

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Biological data are generated at unprecedentedly exponential rates, posing considerable challenges in big data deposition, integration and translation. The BIG Data Center, established at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, provides a suite of database resources, including (i) Genome Sequence Archive, a data repository specialized for archiving raw sequence reads, (ii) Gene Expression Nebulas, a data portal of gene expression profiles based entirely on RNA-Seq data, (iii) Genome Variation Map, a comprehensive collection of genome variations for featured species, (iv) Genome Warehouse, a centralized resource housing genome-scale data with particular focus on economically important animals and plants, (v) Methylation Bank, an integrated database of whole-genome single-base resolution methylomes and (vi) Science Wikis, a central access point for biological wikis developed for community annotations. The BIG Data Center is dedicated to constructing and maintaining biological databases through big data integration and value-added curation, conducting basic research to translate big data into big knowledge and providing freely open access to a variety of data resources in support of worldwide research activities in both academia and industry. All of these resources are publicly available and can be found at http://bigd.big.ac.cn.

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Section: Introductionmentioning

confidence: 99%

The BIG Data Center: from deposition to integration to translation

2016

Nucleic Acids Res

459

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“…In addition to scientific research, academic biomedical laboratories (labs) are expected to use their data and consequent results to support education, knowledge dissemination through publications, and future research questions. However, current traditional informatics approaches used by these labs have helped them little in performing their daily tasks and in fact, jeopardized their productivity [5,6]. Some of the current lab data management methods include handwritten lab notebooks, paper files, homegrown small databases and spreadsheet files.…”

Section: Introductionmentioning

confidence: 99%

Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry–academia collaboration

Myneni

Patel

Bova

et al. 2016

Computer Methods and Programs in Biomedicine

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This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to 1) characterize specific problems faced by biomedical researchers with traditional information management practices, 2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to 3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity.

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“…Even for the same data type, data formats in different sources are often incompatible. In addition, new data formats are being invented along with the development of new technologies (20), such as Sequence Alignment/MAP (SAM) (21) and Genome Variation Format (GVF ) (22).…”

Section: Ontologymentioning

confidence: 99%

Semantic Web technologies for the big data in life sciences

Yamaguchi

2014

BST

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Data Integration in Bioinformatics: Current Efforts and Challenges

Cited by 30 publications

References 132 publications

The BIG Data Center: from deposition to integration to translation

The BIG Data Center: from deposition to integration to translation

Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry–academia collaboration

Semantic Web technologies for the big data in life sciences

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