Science 316:222 , April 2007

1. http://www.sciencemag.org/cgi/reprint/316/5822/222.pdf Science 316:222 , April 2007 The Big Question: What makes us human ? What genes primates share and what genes are uniquely human ? [98%] [93%] • The rhesus responds to the simian immunodeficiency virus (SIV), therefore is • a good animal model for studying HIV infections. • Nearly 200 genes were identified as human-specific, key players in determining differences among primate species. For example, primates have extra sequences for keratin, a component of hair (many of these genes were duplicated). Metsada Pasmanik-Chor, TAU Bioinforamtics Unit

2. From Classical to Post-Genomic Bioinformatics • Classical: • Information management and data-mining. • Sequence analysis and interpretation • (comparisons and alignments), • evolutionary relationships. • Gene prediction and regulation. • Protein structural biology. • Global (Post-genomic): • Functional genomics. • Protein functional sites prediction, drug design and “personalized medicine”. • Protein networks and systems biology. Metsada Pasmanik-Chor, TAU Bioinforamtics Unit

3. Information management - Biological Databases & Software (e.g. NCBI) Metsada Pasmanik-Chor, TAU Bioinforamtics Unit

4. Leghemoglobin Myoglobin a-Globin Yellow Lupine Whale Human Alignments (pairwise, multiple, structures) Multiple alignment: find protein families and functional domains. conserved How conserved ? Metsada Pasmanik-Chor, TAU Bioinforamtics Unit

5. CTATAAATGC A C CTATAAATGC A C Sequence Conservation Suggests Functionality divergence conserved CTATAAATGC A C AGTTGAAAC GGAGCTGATGGAGC GGTGGGC T 80 million years CTATAAATGC A C TACATTTCG ACTGTATCGCCTCG CAACCCT A potential functional region Conservation scale sequence Metsada Pasmanik-Chor, TAU Bioinforamtics Unit http://www.pgaeducation.org/tutoria/WUSTL/BerkeleyPGACompGeno_Boffelli.2.ppt#325,4,Slide

6. Gene Prediction and Regulation Approaches: Pattern finding: long ORF, splice-junctions, transcription signals: start and stop codons, promoter signals (TATA boxes, transcription factor (TF) binding sites, CpG islands and methylation patterns), Poly(A) signals, etc. Comparative genomics approach: ESTs and cDNA database comparisons, protein family homology. Experimental confirmation of gene predictions: use high-throughput methods. The central dogma of gene expression (eukaryotes) Metsada Pasmanik-Chor, TAU Bioinforamtics Unit http://cbis.anu.edu.au/bioinfosummer-2003/tuesday/13h30-14h30_speed_keynote.ppt#4 http://www.cs.odu.edu/~pothen/Courses/CS791/zhang.pdf

7. Genome Browsers Metsada Pasmanik-Chor, TAU Bioinforamtics Unit

8. The Post Genome Era - using genome scale technologies • Complex system analysis – • Gene regulation, function of non-coding sequences. • Understand wide gene expression sets, protein expression and post- translational modifications, protein networks and complexes. • Identify multi-gene diseases. • Variations among individuals, correlation of SNPs with health and disease, disease susceptibility. • Identify disease causes and design “personal” drugs, new drug design. Metsada Pasmanik-Chor, TAU Bioinforamtics Unit

9. The Post Genome Era–High Throughput Functional Genomics “The greatest challenge, however, is analytical…. Deeper biological insight is likely to emerge from examining datasets with scores of samples”… http://www.blackwell-synergy.com/doi/pdf/10.1111/j.1440-1681.2006.04398.x Metsada Pasmanik-Chor, TAU Bioinforamtics Unit