8 Conclusions
Future of medical science is to move toward personalized medicine for enhanced
health care. The high-performance computing along with parallel and better algorithms would be generating volume of data from molecular docking and simulations. Advanced structural biology laboratories and techniques would also be
generating different types of data. The only way which seems to be efficient in
managing and analyzing such an extreme varied data may lie in the application of
big data technologies. Similar kind of extreme data is being generated using
advanced experimentation in life sciences in the area of agriculture for better crop
production and reduced disease susceptibility and in the field of livestock to
understand their genomics as well as protect them from various diseases. Data is
also being generated in the field of microbes for genomics, drug discovery, vaccine
,and better environmental studies. The near future of biology/life sciences seems to
be data-driven hypothesis rather than hypothesis-driven data generation, and newer
computing paradigm of big data technologies may be very useful in this aspect.
References
1. Schmidt B, Hildebrandt A (2017) Next-generation sequencing: big data meets high
performance computing. Drug Discov Today 22:712–717
2. Tripathi R et al (2016) Next-generation sequencing revolution through big data analytics.
Front Life Sci 9(2):119–149
3. Taglang G, Jackson DB (2016) Use of “big data” in drug discovery and clinical trials.
Gynecol Oncol 141(1):17–23
4. Leyens Lada et al (2017) Use of big data for drug development and for public and personal
health and care. Genet Epidemiol 41(1):51–60
5. Richter BG, Sexton DP (2009) Managing and analyzing next-generation sequence data. PLoS
Comput Biol 5(6):e1000369
6. Stephens ZD et al (2015) Big data: astronomical or genomical? PLoS Biol 13(7):e1002195
7. Zhao S et al (2017) Cloud computing for next-generation sequencing data analysis. In: Cloud
computing-architecture and applications. InTech, London
8. Bhuvaneshwar K et al (2015) A case study for cloud based high throughput analysis of NGS
data using the globus genomics system. Comput Struct Biotechnol J 13:64–74
9. da Fonseca RR et al (2016) Next-generation biology: sequencing and data analysis
approaches for non-model organisms. Mar Genomics 30:3–13
10. https://www.rcsb.org/
11. Shaw DE et al (2008) Anton, a special-purpose machine for molecular dynamics simulation.
Commun ACM 51(7):91–97
12. Bernardi RC, Melo MCR, Schulten K (2015) Enhanced sampling techniques in molecular
dynamics simulations of biological systems. Biochimica et Biophysica Acta (BBA) 1850(5):
872–877
13. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein
folding. Chem Phys Lett 314.1:141–151.APA
14. Swinney DC, Anthony J (2011) How were new medicines discovered? Nat Rev Drug Discov
10(7):507–519
Turbo Analytics: Applications of Big Data and HPC in Drug …
371
Future of medical science is to move toward personalized medicine for enhanced
health care. The high-performance computing along with parallel and better algorithms would be generating volume of data from molecular docking and simulations. Advanced structural biology laboratories and techniques would also be
generating different types of data. The only way which seems to be efficient in
managing and analyzing such an extreme varied data may lie in the application of
big data technologies. Similar kind of extreme data is being generated using
advanced experimentation in life sciences in the area of agriculture for better crop
production and reduced disease susceptibility and in the field of livestock to
understand their genomics as well as protect them from various diseases. Data is
also being generated in the field of microbes for genomics, drug discovery, vaccine
,and better environmental studies. The near future of biology/life sciences seems to
be data-driven hypothesis rather than hypothesis-driven data generation, and newer
computing paradigm of big data technologies may be very useful in this aspect.
References
1. Schmidt B, Hildebrandt A (2017) Next-generation sequencing: big data meets high
performance computing. Drug Discov Today 22:712–717
2. Tripathi R et al (2016) Next-generation sequencing revolution through big data analytics.
Front Life Sci 9(2):119–149
3. Taglang G, Jackson DB (2016) Use of “big data” in drug discovery and clinical trials.
Gynecol Oncol 141(1):17–23
4. Leyens Lada et al (2017) Use of big data for drug development and for public and personal
health and care. Genet Epidemiol 41(1):51–60
5. Richter BG, Sexton DP (2009) Managing and analyzing next-generation sequence data. PLoS
Comput Biol 5(6):e1000369
6. Stephens ZD et al (2015) Big data: astronomical or genomical? PLoS Biol 13(7):e1002195
7. Zhao S et al (2017) Cloud computing for next-generation sequencing data analysis. In: Cloud
computing-architecture and applications. InTech, London
8. Bhuvaneshwar K et al (2015) A case study for cloud based high throughput analysis of NGS
data using the globus genomics system. Comput Struct Biotechnol J 13:64–74
9. da Fonseca RR et al (2016) Next-generation biology: sequencing and data analysis
approaches for non-model organisms. Mar Genomics 30:3–13
10. https://www.rcsb.org/
11. Shaw DE et al (2008) Anton, a special-purpose machine for molecular dynamics simulation.
Commun ACM 51(7):91–97
12. Bernardi RC, Melo MCR, Schulten K (2015) Enhanced sampling techniques in molecular
dynamics simulations of biological systems. Biochimica et Biophysica Acta (BBA) 1850(5):
872–877
13. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein
folding. Chem Phys Lett 314.1:141–151.APA
14. Swinney DC, Anthony J (2011) How were new medicines discovered? Nat Rev Drug Discov
10(7):507–519
Turbo Analytics: Applications of Big Data and HPC in Drug …
371