Post on 14-Aug-2020
PERSONALIZED MEDICINE
SYSTEMS BIOLOGY
Models complex biological interactions
Experimental + computational approaches
Multiple dimensions
Vs . observational epidemiology & biology
SYSTEMS BIOLOGY IMPORTANCE
More evolved
technologies
↓
More efficiency
↓
Lower costs
Data generation Data interpretation
• New tech and knowledge (Human Genome Project + biotechnology companies…)
↓↓ time
• Business models
↓↓ €/$
Data generation is no more a bottleneck for most laboratories
OMICS DATA: BOTTLENECKS
OMICS DATA: BOTTLENECKS
Difficulties:
• Biological processes are complex
• Noise of experimental data
• Limitations of statistical analyses
PERSONALIZED MEDICINE NEEDS HYBRID EDUCATION
Big data solutions for:
• Homogenous data
• Heterogeneous data
Biological concepts
↓Analysis algorithms
PERSONALIZED MEDICINE NEEDS HYBRID EDUCATION
Biological expertise Signal/Noise
+
Computer programing skills analyze omics data
90% of scientists are self-taught in developing software
MANAGEMENT OF OMICS DATA
Classic research laboratories ↛ Not enough computational resources
CLOUD COMPUTING
GPUs instead of CPUs
Faster
e.g. MUMmerGPU
High-throughput parallel pairwise local sequence alignment program
≫ 10-fold speedup in alignment
MANAGEMENT OF OMICS DATA
THE CURSE OF DIMENSIONALITY
Measurements ≫ Samples
1. Multiple testing for errors: Bonferroni corrections, Benjamin and Hochberg…
2. Reduce dimensionality via sparse methods: mixOmics, MONA…
3. Co-inertia analyses: omicade4
THE CURSE OF DIMENSIONALITY
MIXING OMICS
Transcriptomics ↮ Proteomics
post-transcriptional & post-translational
regulations
DISTINGUISH TRUE SIGNALS
DISTINGUISH TRUE SIGNALS
New statistical methods: MODMatcher
Importance of Metadata
FUTURE OF BIG GENOMIC DATA
REFERENCES
Alyass, A., Turcotte, M., & Meyre, D. (2015). From big data analysis to personalized medicine for all: challenges and
opportunities. BMC Medical Genomics, 8(1). doi:10.1186/s12920-015-0108-y
Anaxomics Biotech SL - Systems Biology Solutions. (2017). Anaxomics.com. Retrieved6 December 2017, from
http://www.anaxomics.com/
He, K. Y., Ge, D., & He, M. M. (2017). Big Data Analytics for Genomic Medicine. International journal of molecular sciences, 18(2),
412
MUMmerGPU / Wiki / MUMmerGPU. (2017). Sourceforge.net. Retrieved 8 December 2017, from
https://sourceforge.net/p/mummergpu/wiki/MUMmerGPU/
München, H. (2017). MONA. Helmholtz-muenchen.de. Retrieved 5 December 2017, from https://www.helmholtz-
muenchen.de/icb/research/groups/computational-cell-maps/projects/mona/index.html
Yoo, S., Huang, T., Campbell, J. D., Lee, E., Tu, Z., Geraci, M. W., . . . Zhu, J. (2014). MODMatcher: Multi-Omics Data Matcher for
Integrative Genomic Analysis. PLoS Computational Biology, 10(8). doi:10.1371/journal.pcbi.1003790