1. Genomics: READING genome sequences ASSEMBLY of the sequence ANNOTATION of the sequence carry out dideoxy sequencing connect seqs. to make whole chromosomes find the genes! For Bioinformatics, Start with:

2. Genomics: READING genome sequences ASSEMBLY of the sequence ANNOTATION of the sequence carry out dideoxy sequencing connect seqs. to make whole chromosomes find the genes! For Bioinformatics, Start with:

3. 2 ways to annotate eukaryotic genomes: -ab initio gene finders: -Genes based on previous knowledge….EVIDENCE of message 2 ways to annotate eukaryotic genomes: -ab initio gene finders: Work on basic biological principles: Open reading frames Consensus splice sites Met start codons ….. -Genes based on previous knowledge….EVIDENCE of message

7. 2 ways to annotate eukaryotic genomes: -ab initio gene finders: Work on basic biological principles: Open reading frames Consensus splice sites Met start codons ….. -Genes based on previous knowledge….EVIDENCE of message cDNA sequence of the gene’s message cDNA of a closely related gene’ message sequence Protein sequence of the known gene Same gene’s Same gene’s from another species Related gene’s protein…….

8. Homology based exon predictions Consensus gene structure (both strands) start and stop site predictions Splice site predictions computational exon predictions Tracking information Unique identifiers Information for Ab initio gene finding

9. Automatically generated annotation

10. A zebrafish hit shows a gene model protein encoded by a 6 exon gene. This gene structure (intron/exon) is seen in other species, as is the protein size. The proteins, if corresponding to MSP in S. gal., must be heavily glycosylated (likely). At least some have a signal peptide.

11. The zebrafish hit can be viewed at higher resolution, and…

12. The zebrafish hit can be viewed down to nucleotide resolution GO LIVE!

13. Sarin et al

15. Is there linkage between a mutant gene/phenotype and a SNP? USE standard genetic mapping technique, with SNP alternative sequences as “phenotype” B= bad hair, Dominant SNP1..ACGTC.. SNP1’..ACGCC.. SNP2..GCTAA.. SNP2’..GCAAA.. SNP3..GTAAC.. SNP3’..GTCAC.. X X SNP1’..ACGCC.. SNP2’..GCAAA.. SNP3’..GTCAC.. SNP1..ACGTC.. SNP2..GCTAA.. SNP3..GTAAC.. F1 START with Inbred lines- SNPs are homozygosed B

16. Is there linkage between a mutant gene/phenotype and a SNP? USE standard genetic mapping technique, with SNP alternative sequences as “phenotype” B= bad hair, Dominant X B/b b/b B/b b/b 1’/1 25% 1/1 25% 1’/1 25% 1/1 25% 1’/1 1/1 SNP1..ACGTC.. SNP1’..ACGCC.. SNP2..GCTAA.. SNP2’..GCAAA.. SNP3..GTAAC.. SNP3’..GTCAC.. 2’/2 47% 2/2 3% 2’/2 3% 2/2 47% 2’/2 2/2 3’/3 25% 3/3 25% 3’/3 25% 3/3 25% 3’/3 3/3 SO…B is 6 cM from SNP2, and is unlinked to SNP 1 or 3 B 2’ / b 2

17. Is there linkage between a mutant gene/phenotype and a SNP? USE standard genetic mapping technique, with SNP alternative sequences as “phenotype” B= bad hair, Dominant X B/b b/b 1/1’ 1/1 SNP1..ACGTC.. SNP1’..ACGCC.. SNP2..GCTAA.. SNP2’..GCAAA.. SNP3..GTAAC.. SNP3’..GTCAC.. 2/2’ 2/2 3/3’ 3/3 SO…B is 6 cM from SNP2, and is unlinked to SNP 1 or 3 We have the ENTIRE genome sequence of mouse, so we know where the SNPs are Now-do this while checking the sequence of THOUSANDS of SNPs

18. Genomics: READING genome sequences ASSEMBLY of the sequence ANNOTATION of the sequence carry out dideoxy sequencing connect seqs. to make whole chromosomes find the genes! But Bioinformatics is more…

20. TRANSCRIPTOMICS: cDNAs RNA target sample End Reads (Mates) SEQUENCE Primer cDNA Library Each cDNA provides sequence from the two ends – two ESTs & ESTs: Expressed Sequence Tags

22. !!AA_SEQUENCE 1.0 ab025413 peptide tenm4.pep Length: 2771 May 12, 1999 09:34 Type: P Check: 2254.. 1 MDVKERKPYR SLTRRRDAER RYTSSSADSE EGKGPQKSYS SSETLKAYDQ 51 DARLAYGSRV KDMVPQEAEE FCRTGTNFTL RELGLGEMTP PHGTLYRTDI 101 GLPHCGYSMG ASSDADLEAD TVLSPEHPVR LWGRSTRSGR SSCLSSRANS 151 NLTLTDTEHE NTETDHPSSL QNHPRLRTPP PPLPHAHTPN QHHAASINSL 201 NRGNFTPRSN PSPAPTDHSL SGEPPAGSAQ EPTHAQDNWL LNSNIPLETR 251 NLGKQPFLGT LQDNLIEMDI LSASRHDGAY SDGHFLFKPG GTSPLFCTTS 301 PGYPLTSSTV YSPPPRPLPR STFSRPAFNL KKPSKYCNWK CAALSAILIS 351 ATLVILLAYF VAMHLFGLNW HLQPMEGQMQ MYEITEDTAS SWPVPTDVSL 401 YPSGGTGLET PDRKGKGAAE GKPSSLFPED SFIDSGEIDV GRRASQKIPP Protein sequence: from peptide sequencing, or from translation of sequenced nucleic acids

24. Structural proteomics: Coordinates, rather than 1D sequence, Saved

25. /TRANSCRIPTOMICS (Arrays)

26. RNA for ALL C. elegans genes Where? When? Who? are the RNAs

27. Where? When? Who? are the RNAs

28. Where? When? Who? are the RNAs

29. MICROARRAY ANALYSIS Where? When? Who? are the RNAs

30. /TRANSCRIPTOMICS (Arrays)

31. Figure 4.15 Microarray Technique Where? When? Who? are the RNAs

32. Figure 4.15 Microarray Technique Where? When? Who? are the RNAs

33. Array analysis: see animation from Griffiths Where? When? Who? are the RNAs

34. Figure 4.16(1) Microarray Analysis of Those Genes Whose Expression in the Early Xenopus Embryo Is Caused by the Activin-Like Protein Nodal-Related 1 (Xnr1) Where? When? Who? are the RNAs

35. Figure 4.16(2) Microarray Analysis of Those Genes Whose Expression in the Early Xenopus Embryo Is Caused by the Activin-Like Protein Nodal-Related 1 (Xnr1) Where? When? Who? are the RNAs

36. Where? When? Who? are the RNAs

37. Where? When? Who? are the RNAs

38. RNAi for every C. elegans gene too! -results on the web Projects to systematically Knock-out (or pseudo-knockout) every gene, in order to establish phenotype of each gene -> function of each gene

39. Figure 4.23(1) Use of Antisense RNA to Examine the Roles of Genes in Development (here fly)

40. Figure 4.23(2) Use of Antisense RNA to Examine the Roles of Genes in Development (here fly)

41. RNAi for ALL C. elegans genes

42. Figure 4.24 Injection of dsRNA for E-Cadherin into the Mouse Zygote Blocks E-Cadherin Expression

43. MODENCODE

52. MODENCODE was from the Drosophila paper: Nature. 2011 Mar 24;471(7339):527-31. doi: 10.1038/nature09990. A cis-regulatory map of the Drosophila genome. Nègre N et al.

53. Followed by INVERSE PCR to recover seqeunce adjacent to insertion. Then compare to the complete Drosophila genome sequence to know which ORF “Hit” KNOCK-OUTS OF ALL ESSENTIAL GENES – RANDOM MUTAGENESIS ATTEMPT – using transposon mobilization

54. About 10% of All Assumed genes “Hit” (~10/100 per interval) on Drosophila X chromosome. 1 series of random insertion experiments. ALL inset sites know, thanks to INVERSE PCR

55. Figure 1 The two-hybrid assay carried out by screening a protein array. a, The array of 6,000 haploid yeast transformants plated on medium lacking leucine, which allows growth of all transformants. Each transformant expresses one of the yeast ORFs expressed as a fusion to the Gal4 activation domain. b, Two-hybrid positives from a screen of the array with a Gal4 DNA-binding domain fusion of the Pcf11 protein, a component of the pre-mRNA cleavage and polyadenylation factor IA, which also consists of four other polypeptides36. Diploid colonies are shown after two weeks of growth on medium lacking tryptophan, leucine and histidine and supplemented with 3 mM 3-amino-1,2,4-triazole, thus allowing growth only of cells that express the HIS3 two-hybrid reporter gene. Three other components of factor IA, Rna14, Rna15 and Clp1, were identified as Pcf11 interactors. Positives that do not appear in Table 2 were either not reproducible or are false positives that occurred in many screens.Table 2 2-hybrid reaction between one protein and all 6000+ potential interactors in Yeast Genome

56. Figure 2 Visualization of combined, large-scale interaction data sets in yeast. A total of 14,000 physical interactions obtained from the GRID database were represented with the Osprey network visualization system (see http://biodata.mshri.on.ca/grid). Each edge in the graph represents an interaction between nodes, which are coloured according to Gene Ontology (GO) functional annotation. Highly connected complexes within the data set, shown at the perimeter of the central mass, are built from nodes that share at least three interactions within other complex members. The complete graph contains 4,543 nodes of 6,000 proteins encoded by the yeast genome, 12,843 interactions and an average connectivity of 2.82 per node. The 20 highly connected complexes contain 340 genes, 1,835 connections and an average connectivity of 5.39 http://biodata.mshri.on.ca/grid Osprey: integrate all 2-hybrid interactions between all 6000+ proteins in Yeast Genome (Proteome)

60. a