Emerging importance of big data in neuroimmunological research

Emerging importance of big data in neuroimmunological research With rapid advances in information and communication technology, enormous amount of data can be produced and utilized in research and medicine. For example, from just a small amount of saliva, single‐nucleotide polymorphisms across the genome can be genotyped using extracted DNA and microarray chip, and even whole genome sequencing can be carried out. However, to understand the meaning of this obtained information, the data should be properly processed and annotated. Thus, multiple technologies and tools are required to take advantage of big data. It is expected that big data can help us to overview the fields of our interest from a wider point of view, and grasp subtle but fundamental trends in research using, for example, genetic variants dataset (single‐nucleotide polymorphisms or sequencing), gene expression datasets and medical records.The current issue of Clinical and Experimental Neuroimmunology focuses on “big data in neuroimmunology” and contains three review articles.Ogawa and Okada reviewed statistical genetics and its application to neuroimmunology in this issue. The manuscript consists of two parts. First, the authors introduced past genetic studies carried out in neuroimmunological diseases, such as multiple sclerosis (MS) and myasthenia gravis. They next introduced updated methods for human leukocyte antigen imputation including a Japanese population‐specific panel that they had developed and useful tools to link the obtained genetic findings with potential future treatment by repurposing drugs that were already developed for other diseases. They provide us important insights on the future direction of where genetic studies in MS or other neuroimmunological diseases should go with the latest study methodologies.Another example of big data sources for medical research is electronic medical records (EMR). Originally, clinical data had been documented on paper‐based medical records, and the data of interest that clinical researchers wanted to investigate had to be manually extracted and registered on a database. By the transition to EMR, the data of interest can be easily obtained by text mining techniques. Damotte and Gourraud reviewed EMR in this issue, and gave us an overview on how EMR is efficient for research in the field of neuroimmunological diseases, especially in MS research, and what the current limitations are.In the era of increasingly emphasizing big data, how should we maximize the voluminous data in research on neuroimmunological diseases and what should be the ideal strategy for carrying out research? At the end of a focused review in this issue, I briefly reviewed MS genetics, introducing the rationale for genetic contributions to MS susceptibility and severity, and emphasized the importance of active collaboration with researchers from different professions in carrying out research using large datasets. Additionally, some tools that can be useful for genetic research are also summarized.In this issue, the three review articles introduced above emphasize the importance of maximizing the big data, such as genetic datasets and EMR, to reveal new disease‐associated genetic loci or links between genetic variants and specific disease phenotypes that have been difficult to identify because of the lack of statistical power in traditional datasets. In any aspects, a collaborative work as a team or a consortium would be a key to enforce the challenging, but promising, studies with big data.AcknowledgmentsThis work was supported by grants from JSPS KAKENHI (grant number 17H06938) and Japan Multiple Sclerosis Society.Conflict of interestDr Isobe has received a grant and salary from Tanabe Mitsubishi Pharma, Bayer Yakuhin and Japan Blood Products Organization.ReferencesOgawa K, Okada Y. Statistical genetics and its application to neuroimmunology. Clin Exp Neuroimmunol. 2018; 9: 7–12.Damotte V, Gourraud PA. Electronic medical records in multiple sclerosis research. Clin Exp Neuroimmunol. 2018; 9: 13–8.Isobe N. Genetics in multiple sclerosis: updates in the era of big data. Clin Exp Neuroimmunol. 2018; 9: 19–24. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clinical and Experimental Neuroimmunology Wiley

Emerging importance of big data in neuroimmunological research

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Copyright
Copyright © 2018 Japanese Society for Neuroimmunology
ISSN
1759-1961
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1759-1961
D.O.I.
10.1111/cen3.12451
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Abstract

With rapid advances in information and communication technology, enormous amount of data can be produced and utilized in research and medicine. For example, from just a small amount of saliva, single‐nucleotide polymorphisms across the genome can be genotyped using extracted DNA and microarray chip, and even whole genome sequencing can be carried out. However, to understand the meaning of this obtained information, the data should be properly processed and annotated. Thus, multiple technologies and tools are required to take advantage of big data. It is expected that big data can help us to overview the fields of our interest from a wider point of view, and grasp subtle but fundamental trends in research using, for example, genetic variants dataset (single‐nucleotide polymorphisms or sequencing), gene expression datasets and medical records.The current issue of Clinical and Experimental Neuroimmunology focuses on “big data in neuroimmunology” and contains three review articles.Ogawa and Okada reviewed statistical genetics and its application to neuroimmunology in this issue. The manuscript consists of two parts. First, the authors introduced past genetic studies carried out in neuroimmunological diseases, such as multiple sclerosis (MS) and myasthenia gravis. They next introduced updated methods for human leukocyte antigen imputation including a Japanese population‐specific panel that they had developed and useful tools to link the obtained genetic findings with potential future treatment by repurposing drugs that were already developed for other diseases. They provide us important insights on the future direction of where genetic studies in MS or other neuroimmunological diseases should go with the latest study methodologies.Another example of big data sources for medical research is electronic medical records (EMR). Originally, clinical data had been documented on paper‐based medical records, and the data of interest that clinical researchers wanted to investigate had to be manually extracted and registered on a database. By the transition to EMR, the data of interest can be easily obtained by text mining techniques. Damotte and Gourraud reviewed EMR in this issue, and gave us an overview on how EMR is efficient for research in the field of neuroimmunological diseases, especially in MS research, and what the current limitations are.In the era of increasingly emphasizing big data, how should we maximize the voluminous data in research on neuroimmunological diseases and what should be the ideal strategy for carrying out research? At the end of a focused review in this issue, I briefly reviewed MS genetics, introducing the rationale for genetic contributions to MS susceptibility and severity, and emphasized the importance of active collaboration with researchers from different professions in carrying out research using large datasets. Additionally, some tools that can be useful for genetic research are also summarized.In this issue, the three review articles introduced above emphasize the importance of maximizing the big data, such as genetic datasets and EMR, to reveal new disease‐associated genetic loci or links between genetic variants and specific disease phenotypes that have been difficult to identify because of the lack of statistical power in traditional datasets. In any aspects, a collaborative work as a team or a consortium would be a key to enforce the challenging, but promising, studies with big data.AcknowledgmentsThis work was supported by grants from JSPS KAKENHI (grant number 17H06938) and Japan Multiple Sclerosis Society.Conflict of interestDr Isobe has received a grant and salary from Tanabe Mitsubishi Pharma, Bayer Yakuhin and Japan Blood Products Organization.ReferencesOgawa K, Okada Y. Statistical genetics and its application to neuroimmunology. Clin Exp Neuroimmunol. 2018; 9: 7–12.Damotte V, Gourraud PA. Electronic medical records in multiple sclerosis research. Clin Exp Neuroimmunol. 2018; 9: 13–8.Isobe N. Genetics in multiple sclerosis: updates in the era of big data. Clin Exp Neuroimmunol. 2018; 9: 19–24.

Journal

Clinical and Experimental NeuroimmunologyWiley

Published: Jan 1, 2018

References

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