1. Trends Biomedical In silico

2. “Omics” a variety of new technologies help explain both normal and abnormal cell pathways, networks, and processes simultaneous monitoring of thousands of molecular components. genomics (the quantitative study of protein-coding genes, regulatory elements and noncoding sequences), transcriptomics (the quantitative study of RNA and gene expression) proteomics (the quantitative study of protein abundance and protein modifications) metabolomics (the quantitative study of metabolites and metabolic networks) pharmacogenomics (the quantitative study of how genetics affects a host’s response to drugs) physiomics (the quantitative study of physiological dynamics and the functions of whole organisms), nutrigenomics (a rapidly growing discipline that focuses on identifying the genetic factors that influence the body’s response to diet and how the bioactive constituents of food affect gene expression) phylogenomics (the analysis of genome data and evolutionary reconstructions, especially phylogenetics) interactomics (the study of molecular interaction networks).

3. Chem Biol. 2013 May 23;20(5):660-6. iPOP goes the world: integrated personalized Omics profiling and the road toward improved health care. Li-Pook-Than, Snyder M. Free access at http://www.ncbi.nlm.nih.gov/pubmed/23706632

4. Integrated personalized Omics profiling (A) Participant tissue sample (e.g. blood cell) is collected, while environment (incl. diet, exercise, etc.), medical history and clinical data are recorded. T1 is the first time point. (B) Selected omic analysis involved in a sample iPOP study. (C) Sample Circos plot of DNA (outer ring), RNA (middle ring) and protein (inner ring) data matching to chromosomes. (D) iPOP performed and integrated at multiple time points: T2, T3, T4 (viral-infected), T5 up to Tn states, including disease- state(s). Grey and green forms represent relative-healthy individual and a disease-state, respectively. (E) Report data back to genetic counsellor and medical practitioner with better informed choices for prevention and/or treatment (matched with pharmacogenetic data), if needed.

5. Structure-guided drug development Copyright © Molecular Profiling Research Center for Drug Discovery, AIST, 2013 All Rights Reserved. http://www.molprof.jp/english/research/iddt2.html

7. Biological networks represent various types of molecular interactions (1)enzyme catalysis, (2)the post-transcriptional control of gene expression by proteins, (3)the effect of metabolites on gene transcription mediated by a protein, (4)protein interactions, (5)the effect of a downstream metabolite on transcription, (6)feedback inhibition/activation of an enzyme by a downstream metabolite, and (7)the exchange of a metabolite outside of the system. The solid lines represent direct interactions, and the discontinuous lines represent possible interactions in tumor cells. Int. J. Mol. Sci. 2012, 13(6), 6561-6581; doi:10.3390/ijms1306656110.3390/ijms13066561 State of the Art in Silico Tools for the Study of Signaling Pathways in Cancer Vanessa Medina Villaamil, Guadalupe Aparicio Gallego, Isabel Santamarina Cainzos, Manuel Valladares-Ayerbes and Luis M. Antón Aparicio

8. Drug Discovery TodayDrug Discovery Today Volume 19, Issue 2, February 2014, Pages 171–182Volume 19, Issue 2 Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery Douglas B. Kell Douglas B. Kell, Royston GoodacreRoyston Goodacre

10. Gene-expression-based drug repositioning Signature reversion (a) the aim is to identify a drug where the effect on transcription is opposite to a disease signature. Association (b) drugs giving similar gene expression signatures are found and thought to share a common mode of action. Many public databases can be queried to generate drug and disease signatures that can be compared to each other and integrated with newly generated experimental data (c). Drug Discovery TodayDrug Discovery Today Volume 18, Issues 7–8, April 2013, Pages 350–357 Transcriptional data: a new gateway to drug repositioning? Francesco Iorio, Timothy Rittman, Hong Ge, Michael Menden, Julio Saez-RodriguezVolume 18, Issues 7–8Francesco IorioTimothy RittmanHong GeMichael MendenJulio Saez-Rodriguez

11. -omeLevelDescriptionSelected resources Genome DNA Complete/whole DNA sequence, chromosomes http://www.personalgenomes.org/ http://www.1000genomes.org/ http://www.personalgenomes.org/ http://www.1000genomes.org/ dbSNP: http://www.ncbi.nlm.nih.gov/snp http://www.ncbi.nlm.nih.gov/snp Exome DNA DNA sequence associated to coding regions http://www.nhlbi.nih.gov/resources/exom e.htm http://www.nhlbi.nih.gov/resources/gene ticsgenomics/programs/mendelian.htm Epigenome DNA/RNA DNA methylation and histone modification, can affect chromatin and gene expression NIH Roadmap Epigenomics Mapping Consortium http://www.roadmapepigenomics.org/ http://www.roadmapepigenomics.org/ Methylome DNA DNA methylation- The -omics which might be included

12. -omeLevelDescriptionSelected resources Regulome DNA binding regions Regulation factors that affect gene expression ENCODE: ENCyclopedia Of DNA Elements http://www.genome.gov/ENCODE/ http://www.genome.gov/ENCODE/ TranscriptomeRNA Gene expression, isoforms, miRNA, allelic specific expression http://www.h-invitational.jp/ http://www.ncbi.nlm.nih.gov/refseq/ Splice-omeRNA Alternative splicing (not the spliceosome complex) http://jbirc.jbic.or.jp/h-dbas/ EditomeRNA RNA edits, variants not present in DNA - miRNomeRNA miRNAshttp://genetrail.bioinf.uni- sb.de/wholemirnomeproject/

13. -omeLevelDescriptionSelected resources Proteomeprotein Protein expression, isoforms http://www.humanproteinpedia.org/ http://www.hprd.org/ Autoantibodyome protein Antibody targeted against one’s own protein(s) - Metabolome metabolites Small molecules of metabolism (eg. nucleotides, amino acids, vitamins) Human Metabolome Database: http://www.hmdb.ca/ http://www.hmdb.ca/

14. -omeLevelDescriptionSelected resources Metagenome DNA Genomes of multiple organisms https://www.ebi.ac.uk/metagenomics/ Microbiome DNA/RNA/ protein Microbial characterization at multiple human body sites http://www.human- microbiome.org/ http://www.human- microbiome.org/ NIH: http://www.hmpdacc.org/http://www.hmpdacc.org/ Interactome All Networks of all – omic interactions http://interactome.dfci.harvard.edu/ Pharmacogenome DNA/RNA/ protein -omic variants reflecting an individual’s response to drugs http://hapmap.ncbi.nlm.nih.gov/ http://www.pharmgkb.org/