Progress Towards and Challenges in Biological Big Data

Biological big data represents a vast amount of data in bioinformatics and this could lead to the transformation of the research pattern into large scale. In medical research, a large amount of data can be generated from tools including genomic sequencing machines. The availability of advanced tools and modern technology has become the main reason for the expansion of biological data in a huge amount. Such immense data should be utilized in an efficient manner in order to distribute this valuable information. Besides that, storing and dealing with those big data has become a great challenge as the data generation are tremendously increasing over years. As well, the blast of data in healthcare systems and biomedical research appeal for an immediate solution as health care requires a compact integration of biomedical data. Thus, researchers should make use of this available big data for analysis rather than keep creating new data as they could provide meaningful information with the use of current advanced bioinformatics tools.

[1]  Peter J. Tonellato,et al.  Cloud computing for comparative genomics , 2010, BMC Bioinformatics.

[2]  Andrian Yang,et al.  Scalability and Validation of Big Data Bioinformatics Software , 2017, Computational and structural biotechnology journal.

[3]  S. Schuster.  Next-generation sequencing transforms today's biology , 2008, Nature Methods.

[4]  Yi Pan,et al.  CLOUD COMPUTING FOR NEXT-GENERATION SEQUENCING DATA ANALYSIS , 2016 .

[5]  Ivan Merelli,et al.  Managing, Analysing, and Integrating Big Data in Medical Bioinformatics: Open Problems and Future Perspectives , 2014, BioMed research international.

[6]  Jing Zhang,et al.  The real cost of sequencing: scaling computation to keep pace with data generation , 2016, Genome biology.

[7]  R. Simon.  Interpretation of genomic data: questions and answers. , 2008, Seminars in hematology.

[8]  Borja Sotomayor,et al.  Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses , 2014, J. Biomed. Informatics.

[9]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[10]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[11]  Jie Tan,et al.  Big Data Bioinformatics , 2014, Journal of cellular physiology.

[12]  R. Myers,et al.  Advancements in Next-Generation Sequencing. , 2016, Annual review of genomics and human genetics.

[13]  Erika Check Hayden,et al.  Genome researchers raise alarm over big data , 2015, Nature.

[14]  D. P. Acharjya,et al.  A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools , 2016 .

[15]  Abel N. Kho,et al.  Practical challenges in integrating genomic data into the electronic health record , 2013, Genetics in Medicine.

[16]  Nicholas Evangelopoulos,et al.  The future of big data , 2017 .

[17]  David Meyre,et al.  From big data analysis to personalized medicine for all: challenges and opportunities , 2015, BMC Medical Genomics.

[18]  M. Schatz,et al.  Big Data: Astronomical or Genomical? , 2015, PLoS biology.

[19]  Roy D. Sleator,et al.  'Big data', Hadoop and cloud computing in genomics , 2013, J. Biomed. Informatics.

[20]  Arpad Kelemen,et al.  Big Data Science and Its Applications in Health and Medical Research: Challenges and Opportunities , 2016 .

[21]  Swarup Roy,et al.  Big Data Analytics in Bioinformatics: A Machine Learning Perspective , 2015, ArXiv.

[22]  M. Anusha,et al.  Big Data-Survey , 2016 .

[24]  V. Marx.  Biology: The big challenges of big data , 2013, Nature.

[25]  James Taylor,et al.  Next-generation sequencing data interpretation: enhancing reproducibility and accessibility , 2012, Nature Reviews Genetics.

[26]  Jake Luo,et al.  Big Data Application in Biomedical Research and Health Care: A Literature Review , 2016, Biomedical informatics insights.

[27]  Baowen Xu,et al.  Application of Metamorphic Testing to Supervised Classifiers , 2009, 2009 Ninth International Conference on Quality Software.

[28]  Chi Zhang,et al.  Cloud Computing for Next‐Generation Sequencing Data Analysis , 2017 .

[29]  J. Kozubek,et al.  Modern Prometheus: Editing the Human Genome with Crispr-Cas9 , 2016 .

[30]  Zhen Hua Liu,et al.  Big data management challenges in health research - a literature review , 2019, Briefings Bioinform..

[31]  Yixue Li,et al.  Big Biological Data: Challenges and Opportunities , 2014, Genom. Proteom. Bioinform..