1. Genome annotation

2. What we have GATCAATGATGATAGGAATTGAAAGTGTCTTAATTACAATCCCTGTGCAATTATTAATAACTTTTTTGTT CACCTGTTCCCAGAGGAAACCTCAAGCGGATCTAAAGGAGGTATCTCCTCAAAAGCATCCTCTAATGTCA GAAGCAAGTGAGCACTGGGAAGAATACTTGAGAAAGTGGCATGCTTACGAAACTGCTAAGGTGCACCCCA GGGAGGTTGCAAAACCTGCATCTAAAGGAAAGCCCAGGCTTCCAAAGGCTTCTCCTAAGGCAACCTCCAA ACCCAAGCACAGGCATAGGAAAGCACAAATCAAGACCCCGGAGACCCTCGGGCCAAATACAAATTCCAAT AACAACATAGAAGATGATCAGGATGTCCATTCCGAACAGCACCCTTCCCAAAAGGATCTCCAGCAGCTTA AGAAAAAGCCCCGGATCGTCCTACCTTGGTGGTGTGTTTATGTTGCATGGTTTTTGGTTTTTGCTACTTC TAGCATATCCTCATTCTTCATTGTATTTTATGGACTGACTTACGGCTATGACAAGTCAATAGAATGGCTC TTTGCATCTTTTTGTTCATTCTGTCAGTCAGTTCTTCTGGTGCAGCCATCTAAAATTATACTCCTGTCAG GCTTCAGAACGAATAAACCCAAGTATTGCAAAAACCTTTCATGGTCAACCAAGTATAAATATACTGAGAT CAGGTTGGATGGAATGCGTATGCATCCAGAAGAAATGCAGAGGATACATGACCAGATCGTCCGAATCCGA GGCACGAGGATGTACCAACCCCTTACAGAAGATGAAATCAGAATATTCAAAAGAAAGAAGAGGATCAAGA GAAGAGCACTCCTGTTTCTGAGTTACATTCTAACTCACTTTATCTTTCTAGCCCTTCTGTTGATCCTTAT CGTCTTACTACGTCACACTGACTGCTTTTACTATAACCAGTTTATTCGTGATCGGTTCTCTATGGATCTT GCTACTGTGACTAAGCTGGAAGACATCTATAGATGGCTAAACAGCGTGCTGTTGCCTTTGTTACACAATG ACCTGAATCCAACATTTCTTCCTGAAAGCTCGTCTAAAATCCTTGGCCTTCCATTGATGAGGCAAGTGAG AGCAAAATCTAGTGAAAAAATGTGTCTACCTGCCGAAAAGTTTGTGCAAAACAGCATCAGAAGAGAAATT CATTGTCACCCCAAATATGGCATTGACCCAGAAGACACAAAAAACTATTCTGGCTTTTGGAATGAAGTTG ATAAGCAGGCTATAGATGAGAGTACCAATGGATTTACTTATAAGCCTCAAGGAACGCAATGGCTATATTA TTCCTATGGACTACTACACACCTATGGATCTGGAGGATATGCACTCTATTTTTTTCCAGAACAGCAGCGG TTTAATTCCACACTGAGGCTCAAAGAACTTCAAGAAAGCAATTGGCTGGATGAGAAGACATGGGCTGTGG TTTTGGAATTAACAACTTTTAATCCAGATATAAATCTGTTCTGTAGCATTTCGGTCATATTTGAAGTCTC TCAGTTAGGAGTTGTCAACACAAGCATATCTCTGCACTCTTTTTCACTTGCTGATTTTGACAGAAAAGCT TCAGCAGAAATCTACTTGTATGTGGCCATTCTCATTTTTTTCTTAGCCTACGTTGTTGATGAGGGTTGTA TCATTATGCAAGAAAGAGCCTCCTATGTGAGAAGTGTGTATAATTTGCTCAACTTTGCTTTAAAGTGCAT ATTTACTGTGTTGATTGTGCTCTTTCTCAGGAAACATTTCCTGGCCACTGGCATAATTCGGTTTTACTTG TCGAACCCAGAAGACTTCATTCCCTTTCATGCAGTTTCTCAGGTAGATCACATTATGAGGATAATTTTGG GTTTCCTGTTATTTCTGACAATTTTGAAGACCCTCAGGTATTCCAGATTCTTCTACGATGTGCGCCTGGC TCAGAGGGCCATCCAGGCTGCCCTCCCTGGCATCTGCCACATGGCATTTGTTGTGTCCGTGTATTTCTTC GTATACATGGCTTTTGGTTACCTGGTGTTTGGTCAGCATGAATGGAACTACAGTAACTTGATTCATTCCA CTCAGACAGTATTTTCCTATTGTGTCTCAGCTTTCCAGAACACTGAATTTTCCAATAACAGGATTCTGGG GGTCCTGTTCCTCTCATCTTTCATGCTGGTGATGATCTGCGTCTTGATCAACTTATTTCAGGCTGTAATT

3. What we want: Annotated sequence GATCAATGATGATAGGAATTGAAAGTGTCTTAATTACAATCCCTGTGCAATTATTAATAACTTTTTTGTT CACCTGTTCCCAGAGGAAACCTCAAGCGGATCTAAAGGAGGTATCTCCTCAAAAGCATCCTCTAATGTCA GAAGCAAGTGAGCACTGGGAAGAATACTTGAGAAAGTGGCATGCTTACGAAACTGCTAAGGTGCACCCCA GGGAGGTTGCAAAACCTGCATCTAAAGGAAAGCCCAGGCTTCCAAAGGCTTCTCCTAAGGCAACCTCCAA ACCCAAGCACAGGCATAGGAAAGCACAAATCAAGACCCCGGAGACCCTCGGGCCAAATACAAATTCCAAT AACAACATAGAAGATGATCAGGATGTCCATTCCGAACAGCACCCTTCCCAAAAGGATCTCCAGCAGCTTA AGAAAAAGCCCCGGATCGTCCTACCTTGGTGGTGTGTTTATGTTGCATGGTTTTTGGTTTTTGCTACTTC TAGCATATCCTCATTCTTCATTGTATTTTATGGACTGACTTACGGCTATGACAAGTCAATAGAATGGCTC TTTGCATCTTTTTGTTCATTCTGTCAGTCAGTTCTTCTGGTGCAGCCATCTAAAATTATACTCCTGTCAG GCTTCAGAACGAATAAACCCAAGTATTGCAAAAACCTTTCATGGTCAACCAAGTATAAATATACTGAGAT CAGGTTGGATGGAATGCGTATGCATCCAGAAGAAATGCAGAGGATACATGACCAGATCGTCCGAATCCGA GGCACGAGGATGTACCAACCCCTTACAGAAGATGAAATCAGAATATTCAAAAGAAAGAAGAGGATCAAGA GAAGAGCACTCCTGTTTCTGAGTTACATTCTAACTCACTTTATCTTTCTAGCCCTTCTGTTGATCCTTAT CGTCTTACTACGTCACACTGACTGCTTTTACTATAACCAGTTTATTCGTGATCGGTTCTCTATGGATCTT GCTACTGTGACTAAGCTGGAAGACATCTATAGATGGCTAAACAGCGTGCTGTTGCCTTTGTTACACAATG ACCTGAATCCAACATTTCTTCCTGAAAGCTCGTCTAAAATCCTTGGCCTTCCATTGATGAGGCAAGTGAG AGCAAAATCTAGTGAAAAAATGTGTCTACCTGCCGAAAAGTTTGTGCAAAACAGCATCAGAAGAGAAATT CATTGTCACCCCAAATATGGCATTGACCCAGAAGACACAAAAAACTATTCTGGCTTTTGGAATGAAGTTG ATAAGCAGGCTATAGATGAGAGTACCAATGGATTTACTTATAAGCCTCAAGGAACGCAATGGCTATATTA TTCCTATGGACTACTACACACCTATGGATCTGGAGGATATGCACTCTATTTTTTTCCAGAACAGCAGCGG TTTAATTCCACACTGAGGCTCAAAGAACTTCAAGAAAGCAATTGGCTGGATGAGAAGACATGGGCTGTGG TTTTGGAATTAACAACTTTTAATCCAGATATAAATCTGTTCTGTAGCATTTCGGTCATATTTGAAGTCTC TCAGTTAGGAGTTGTCAACACAAGCATATCTCTGCACTCTTTTTCACTTGCTGATTTTGACAGAAAAGCT TCAGCAGAAATCTACTTGTATGTGGCCATTCTCATTTTTTTCTTAGCCTACGTTGTTGATGAGGGTTGTA TCATTATGCAAGAAAGAGCCTCCTATGTGAGAAGTGTGTATAATTTGCTCAACTTTGCTTTAAAGTGCAT ATTTACTGTGTTGATTGTGCTCTTTCTCAGGAAACATTTCCTGGCCACTGGCATAATTCGGTTTTACTTG TCGAACCCAGAAGACTTCATTCCCTTTCATGCAGTTTCTCAGGTAGATCACATTATGAGGATAATTTTGG GTTTCCTGTTATTTCTGACAATTTTGAAGACCCTCAGGTATTCCAGATTCTTCTACGATGTGCGCCTGGC TCAGAGGGCCATCCAGGCTGCCCTCCCTGGCATCTGCCACATGGCATTTGTTGTGTCCGTGTATTTCTTC GTATACATGGCTTTTGGTTACCTGGTGTTTGGTCAGCATGAATGGAACTACAGTAACTTGATTCATTCCA CTCAGACAGTATTTTCCTATTGTGTCTCAGCTTTCCAGAACACTGAATTTTCCAATAACAGGATTCTGGG GGTCCTGTTCCTCTCATCTTTCATGCTGGTGATGATCTGCGTCTTGATCAACTTATTTCAGGCTGTAATT Exon 1 Exon 2 Exon 3 Exon 4

4. Making sense of genomic seqs HMM analysis Compare genomes to each other Compare to other kind of supprting data –Which kinds of data can you think of?

5. Other kinds of data 1.mRNA sequences (and ESTs) 2.Protein sequences

6. -OMEs Genome Transcriptome Proteome Interactome Metabolome Phenome

7. -OMEs Technologies Genome Transcriptome Proteome Interactome Metabolome Phenome Sequencing Microarray Computer (ORFs), Mass-spec Y2H, Mass-spec Mass-spec Phenotype Biochemical Disease

8. Transcript databases RefSeq contains full length sequences of mRNAs, carefully reviewed –Currently 20.000 human sequences dbEST contains 5’ and 3’ reads of random cDNAs –Currently 3.7 mio. human seqs

9. What are ESTs?

11. UniGene UniGene: Merge (cluster) any two ESTs when >100 bp are identical 4 mio -> 104.214 clusters

12. ESTs UniGene: total # clusters 104.214 Cluster size Number of clusters 1 (singletons)37503 2 14605 3-4 15912 5-8 10798 9-16 5978 17-32 4143 33-64 3658 65-128 4117 129-256 4109 257-512 2317 513-1024 743 1025-2048 227 2049-4096 68 4097-8192 29 8193-16384 6 16385-32768 1

13. Transcripts: what can we learn? Comparing genome sequences to transcripts allows: –Confirmation of gene predictions –Experimental identification of Exons/Introns, 5’ UTRs, 3’ UTRs –Alternative splicing Asses the relative abundance of transcripts: Digital differential display.

15. Annotation example

16. 18 exons 623 AA

18. Regulation of Gene Expression

19. Cells respond to environment Heat Food Supply Responds to environmental conditions Various external messages

20. Where gene regulation takes place Opening of chromatin Transcription Translation Protein stability Protein modifications

21. Transcriptional Regulation Strongest regulation happens during transcription Best place to regulate: No energy wasted making intermediate products However, slow response time After a receptor notices a change: 1.Cascade message to nucleus 2.Open chromatin & bind transcription factors 3.Recruit RNA polymerase and transcribe 4.Splice mRNA and send to cytoplasm 5.Translate into protein

22. Transcription Factors Binding to DNA Transcription regulation: Certain transcription factors bind DNA Binding recognizes DNA substrings: Regulatory motifs

23. RNA Polymerase TBP Promoter and Enhancers Promoter necessary to start transcription Enhancers can affect transcription from afar Enhancer 1 TATA box Gene X DNA binding sites Transcription factors

24. Example: A Human heat shock protein TATA box: positioning transcription start TATA, CCAAT: constitutive transcription GRE: glucocorticoid response MRE: metal response HSE: heat shock element TATASP1 CCAAT AP2 HSE AP2CCAAT SP1 promoter of heat shock hsp70 0 --158 GENE Motifs:

25. The Cell as a Regulatory Network AB Make D C If C then D If B then NOT D If A and B then D D Make B D If D then B C gene D gene B B Promoter D Promoter B

26. The Cell as a Regulatory Network (2)

27. DNA Microarrays Measuring gene transcription in a high- throughput fashion

28. What is a microarray

29. What is a microarray (2) A 2D array of DNA sequences from thousands of genes Each spot has many copies of same gene Allow mRNAs from a sample to hybridize Measure number of hybridizations per spot

30. How to make a microarray Method 1: Printed Slides (Stanford) –Use PCR to amplify a 1 kb portion of each gene / EST –Apply each sample on glass slide Method 2: DNA Chips (Affymetrix) –Grow oligonucleotides (20bp) on glass –Several words per gene (choose unique words) If we know the gene sequences, Can sample all genes in one experiment!

32. Microarray for Yeast Figure from DeRisi et al. (See next slide).

33. cDNA Microarrays Use robot to spot glass slides at precise points with complete gene/EST sequences Gene expression levels measured by fluorescence hybridisation

34. Microarray Experiment RT-PCR LASER DNA “Chip” High glucose Low glucose

35. Raw data – images Red (Cy5) dot – overexpressed or up-regulated Green (Cy3) dot – underexpressed or down-regulated Yellow dot –equally expressed Intensity - “absolute” level cDNA plotted microarray

36. Bioinformatics in microarray data Array design Data extraction (Pixel to matrix) Background correction Data normalization Data analysis