1) Personalized medicine currently faces challenges in processing large-scale genomic data, interpreting the functional effects of genomic variations, integrating systems-level data, and translating discoveries into medical practice. 2) Bioinformatics can help address these challenges through algorithms for mapping and aligning sequencing data, predicting functional effects, prioritizing genes, integrating multi-omics data into networks, and disseminating discoveries through databases to inform medical practice. 3) Fully realizing personalized medicine will require overcoming limitations of current approaches, validating computational predictions, and updating medical practice and education to routinely incorporate genomic information.

1. Victoria López Alonso PhD Medical Bioinformátics Area Instituto de Salud Carlos III Spain Bioinformatics challenges in a personalized medicine pipeline Workshop INBIOMEDvision, MIE 2011

2. Bridging gaps between Bioinformatics and MI BMI deals with the integrative management and synergic exploitation of the wide and inter-related scope of information that is generated and needed in healthcare settings, biomedical research institutions and health-related industry.

3. Overview: Personalized medicine in current practice 1- Processing large-scale genomic data 2- Interpretation of functional effect of genomic variation 3- Integration of systems data 4- Translation into medical practice Bioinformatics challenges for Personalized medicine

4. Personalized medicine in current practice Translational bioinformatics utilizes computational tools for the analysis of large biological databases and to fully comprehend disease mechanisms by not only understanding the genetics and the proteomics but also by associating them with the clinical data.

6. Personalized medicine in current practice Chemotherapy medications trastuzumab and Imatinib (Gambacorti-Passerini, 2008; Hudis, 2007) Targeted pharmacogenetic dosing algorithm is used for warfarin ( International Warfarin Pharmacogenetics Consortium et al., 2009 ) Incidence of adverse events for drugs Abacavir, Carbamazepine and Clozapine (Dettling et al., 2007; Ferrell and McLeod, 2008, 2002). The inclusion of genetics in EHRs will provide risk assesment. Clinical assessment incorporating a personal genome . Ashley et al. Lancet (2010)

13. 2- Interpretation of functional effect After genomic data has been processed, the functional effect and the impact of the genetic variation must be analyzed Genome-wide association studies (GWASs) have been used to assess the statistical associations of SNPs with many important common diseases. GWAS provides new insights but only a limited number of variants have been characterized and understanding the functional relationship between variants and phenotypes. https://www.wtccc.org.uk