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