1. Dark matters in the genomes Shin-Han Shiu Plant Biology / Genetics / EEBB / QBMI

2. About myself

6. Cell, nucleus, and chromosomes

7. DNA AGGCGTAGAGAGATCCTTGAT TCCGCAACTCTCAAGGAACAA

8. DNA and Genome  Genome is all the DNA in a cell made up of A, T, G, C...  How many A's, T's, G's, and C's are there in the human genome? 3,200,000,000 letters  A sizable book, say, Lord of the Ring: Fellowship of the Ring 764,470 characters in 410 pages ~2,000 characters per page  The book of our life 1,600,000 pages 4,186 Fellowship of the ring

9. Genome sequencing chronology YearOrganismSignificance Genome size (bp) Number of genes 1977 Bacteriophage fX174 First genome ever! 5,38611 1981 Human mitochondria First organelle 16,50037 1995 Haemophilus influenzae Rd First free-living organism 1,830,137~3,500 1996 Saccharomyces cerevisiae First eukaryote 12,086,000~6,000

10. Genome sequencing chronology YearOrganismSignificance Genome size (bp) Number of genes 1998 Caenorhab-ditis elegans First multi- cellular organism 97,000,000~19,000 1999 Human chromosome 22 First human chromosome 49,000,000673 2000 Drosophila melanogaster First insect 150,000,000~14,000 2000 Arabidopsis thaliana First plant genome 150,000,000~25,000

11. Now, 1366 genomes are sequenced or being sequenced

12. Between human and other animals  How much do our and chimp genomes differ?  0.1%  1%  10%  50%  90%  How many genes do you think we share with worm?  1%  10%  50%  75%  99%

13. Genome and better food

14. Basic understanding of science & environment

15. Our research interest TTGGCTATCCTTTATATTTTAAGGGTTATTAGGATATTTTTTATTATGACTACATGGGATAAATGTTTAAAAAAAATAAAAAAAAACCTTTCTACGTTTGAGTATAAGACGTGGATAAAGCCTATCCATGTGGAGCAAAA TAGTAACTTATTCACAGTTTACTGTAACAATGAATATTTCAAAAAACATATAAAATCTAAGTATGGAAATCTTATTTTATCAACAATCCAAGAGTGTCATGGTAATGATTTAATTATTGAATATTCTAATAAAAAATTCT CTGGCGAAAAAATTACTGAGGTTATCACAGCTGGACCACAAGCTAATTTTTTTAGCACAACAAGTGTTGAGATAAAAGATGAATCAGAAGATACAAAAGTAGTACAAGAACCTAAAATATCAAAGAAGTCTAATAGTAAA GACTTTTCTTCATCACAAGAGTTATTCGGTTTTGACGAAGCTATGCTAATTACAGCAAAAGAAGATGAGGAATACTCTTTTGGTTTACCGTTAAAAGAAAAATATGTTTTTGATAGTTTTGTTGTTGGAGATGCTAACAA AATTGCTAGAGCAGCGGCTATGCAGGTATCGATAAATCCAGGTAAATTACATAACCCTTTATTCATTTATGGTGGTAGTGGTTTAGGTAAAACTCACTTAATGCAAGCAATAGGTAATCATGCAAGAGAAGTTAATCCTA ATGCCAAAATTATTTATACAAATTCAGAACAATTTATTAAAGATTATGTAAATTCTATTCGTTTACAAGATCAAGATGAGTTTCAAAGAGTTTATAGATCTGCGGATATACTTTTGATTGATGATATTCAATTTATCGCT GGTAAAGAGGGTACTGCTCAGGAGTTTTTCCATACTTTTAATGCATTGTATGAAAATGGTAAACAGATAATTCTAACTAGTGATAAGTATCCAAATGAAATAGAAGGGCTTGAAGAAAGACTAGTTTCGCGTTTTGGTTA TGGTTTAACAGTTTCTGTTGATATGCCAGATTTAGAAACCAGAATTGCTATCTTGCTCAAAAAAGCTCATGATTTAGGTCAGAAATTACCTAACGAAACAGCAGCTTTTATTGCTGAGAATGTACGTACTAATGTCAGAG AACTAGAAGGTGCTCTAAATAGGGTTCTTACTACCTCTAAATTTAATCATAAAGATCCTACTATCGAAGTAGCACAAGCTTGCTTAAGAGATGTTATAAAAATACAAGAAAAGAAAGTAAAAATAGATAATATCCAAAAG GTTGTTGCTGATTTTTATAGAATCAGGGTAAAAGATTTAACTTCTAATCAAAGAAGTAGAAATATAGCTAGACCAAGACAGATAGCAATGAGTTTAGCACGTGAACTAACATCACATAGTTTGCCAGAAATAGGCAATGC TTTTGGTGGTAGAGACCATACGACAGTTATGCATGCTGTCAAAGCTATAACTAAATTAAGACAAAGCAATACTTCAATATCGGATGATTATGAGTTGCTTTTAAATAAAATTTCTCGTTAAATAAAATTAGTAACTTTAT CAAAGGGGTTTTAAAAAATGAATTTTGTACTAAATAGAGATGACTTACTAAAGCCTTTGCAATCTATGCTCTCAGTTGCAAATAGTAAGAGTACAATGCCTTTATTATCATGTATCTTATTTGATATTGATAATAATAAT CTCAAAATTACGGCTTCGGATCTTGATACAGAGATATCATGCAATATAGCAGTTAGTTGTAACACAACTATTAAGTTAGCATTAAATGCTGACAAAATTTATAACATTGTCAGAAGCTTAAATGAAAATTCAATGATTGA TTTTAGAATTAATGAAAATAAGGTAACTATTGTTTCTAATAATAGTACTTTTAACCTTATATCACTAAATGCTGACAACTATCCTCTTATTGATAGTAATATCAATGAGCAAGCAAGTTTTGATCTTTCTCAACAAGATT TTCATCATATTATTTCAAAAGTAGATTTCTCAATGGCTAATGATGATACTCGATATTTCTTAAATGGGATGTTTTGGGAAATCAACGCAAATCTACTAAGAGCAGTATCTACAGATGGTCATAGAATGTCTATCACAGAG GCTATAATTGATAGTAAAGTGTTAGATAGTGCTTCTCAGTCGATAATTCCAAAAAAAGCGATTTTAGAGCTTAAAAAGATAGTTGGCAAAACAGAAGAAAATATCAAAATTTGTCTTGGCAAAAATTATCTAAAAGCGAT TTTTGGTAATTATGCTTTTATATCAAAGCTTATAGATGGTCGCTATCCTGATTACCAAAAAGTAATCCCTAAAAATAATACAAAACTATTAGCAGTTGATAAGCAGTTTTTCAAAAATTCATTATTAAGAACATCAATAC TTGCTAATGATAAATATAAAGGTGTTCGTCTTAACATATCTCAAAATCAATTACTTCTATCAGCTAATAACCCTGATAATGAAAAGGCTGAAGATAAAATCGAAGTTCAATATAATGATCAACCAATGGAAATTTGTTTT AATTACAAATATCTTTTGGATATTATAAATGTACTTAGTGAAGAAACTATGTCTATCTACCTTGATAATCCAAATATGAGTGCTTTAGTTAAAGATGAGAAAGATAATAGTTTGTTTATTATTATGCCAATGAAAATTTA AGTAATAAGTAGTTTTAGGAAATAACTATTTTTATAAGCCTTTTGGAATGAATAATAAAGCAATAAAAAAAGGTATGCATAAAAACATTATATAGAAAGCTGGGATTAGATAATTTCCAGTAGTAGTAATTAATAAAGTC ATAAGAAAGGCAACAGTACCTCCAAATAAAGAAACGCTTATATTAAAACTTATTCCAAAACCAGTATTTCTATTTCTAACAGGAAATAGCCCTGCCGTATTTGCAAATATAGGTCCTATTACAGCACCACTTATAATAGC AAGAGAAAAAATAGCTATAGATACTAATTGATGATTTTTTATAATAATTTGGTATATTGGTAAAACAGCTATAAATAAGACTATACAAGAATACATCAGAACTTTTTTACCACCAATTCTATCAGCAATATATCCAAATA TAATTGAAGAAAGCATTAATACTATAGTTAATCCGAGAGTATTTTGTTAAACACATAAGAGATAGCATCACAAAATTTTTCAAAACTATTATTCACTTTTCTAAATATTTTTTTAAAGTTAGCCCAAACCTTTTCAATAG GATTTAAATCTGGAGAATACGGAGGTAGATATAATATTTGTACATCAAATTTATTGGCTATTTCAATCAGCTTAGAGGATTTATGGAAACTAGCATTATCCATTACTATAGTAGTTTTAGGTTTTAATGATGGGCATAAG TGTTCCTCAAACCATTGATTAAAAATTTCAGTATTGGTATATCCACTGTACTCTAATGGAGCTATAATCTTTTTATCTGCATAATTATATCCAGCAACAATACTTCTTCTTTGTGTTTGATATGCTAAAACCTCACCATA ACTAGGCTCACCAATTAGTGACCATCCTCTTAGGATAGAAAGCTTATTGTCACACCCCATCTCATCTATATAAAATAACAAGTTTTGAGCTATTTCTTTTAGTTTTTCTATATACTCCAACCTTTCATGTTCTTTTCTTT GCTTATATTTTGGAGTCTTTTTTTAAAACTAAAACCAAGTCTATTAAGACAATCATAAAATGTACTTCTTGGAATATCAGGGGCTAATGCTTCTTTTATATCTAATGCACTTGCATCTGGATGATCTATCAAATACTGTT CAATCAATGTTTTATCGGTAAAGCTAGCGACTCTGCCACAACCAACTCCTTGCTTTGAACTATAATCTCCGGTTCTTTTATAAAACTCTATCCATGAAACAACTGTACGCTTATCTATGTTAAAAAACTTACTCAGCTCG AACTCCGTCATACCTTCTTCATATTTATTAATTACGATGTCTCTAAAATATTGGCTATATGATGGCATTTTTATTAGACATTATAACATTTCTACAAATATCTTTTTCTACAAATATCTTTCGGATTAACTATATAAGTA GAGTCAACAACCATCCAAATCACCCAATTATCTATAATTTTCTGCTTGCTAAAAAAACGCATACCAATGATGCTACACTTGTAAAACCATCCATATATGGCGTTGTTGAATCAGTATAAAATATAAGTAGTTGCGAAACT AGTAACCCAAATACTACAATGCTTACTAGAACTTTTAACCAACCAATGATTTTAAGTCTATGAACAACTATCTTTTTGTGACTAAAGTTGGGTTGCCAACTATACCAACCGTATCCAAAGCTAAATAAAAGAATCATCTG CAATATAGCATCGGCATATAGTCCACTAACAGAAAATAAACCCGCACTCATGATCAAACCAACTATCTCCACAGGCCAACCAATGACATAAAGCCTTGCCAGCAAAAAGGTACACAAAAGATTAACAATCATTGTACAAA AATCAAAAATATGCAGCATATTTATTTTACTAATCAAAGTATTATAAATATTATAATAACTTTGAAGTTGGCGTATTAAAGCCATAAACTTTAGTAGGTTAGTGTTTATACCAATATTTTGAGATGCTTTCTGCAAGCTA ATAACATTTAGCTATCTAGCCTAAATAATTAATATACAAAACTTTCAAGCTTATTGAATTTTTCAACAGATACAGCGCGTTATAACAAATAAGTAATTGACTAAATTAAAAAGCAAGTATAATATCGATTGTGTTTATTA CATAATATAAAACGAGGATAAAAAAAATATGAAATTAAGAAAAGTATTAATCGCGACATTATTAGGAGCTTCTGCTTTATCTTTAAGTAGTTGTTGGTTACTTGTTGGTGCAGCTGTTGGTGGTGGAACTGCTGCGTATA TTTCTGGTGAGTATTCAATGAATATGAGTGGCAGTGTAAAAGATATTTACAATGCTACTTTAAAAGCTGTTCAAAGCAATGATGATTTTGTAATTACTAAAAAATCTATTACTTCTGTTGATGCAGTTGTTGATGGTAGT ACTAAGGTAGACTCAACAAGTTTCTATGTTAAAATAGAAAAACTTACTGATAATGCTTCAAAAGTTACAATTAAGTTTGGTACTTTTGGTGACCAAGCAATGTCAGCAACATTAATGGATCAAATCCAAAAGAATCTTTA ATTAAATAGGTAATTACTATAATGACTTTTCTAAAGAAAGCTTTTATTGCAACTATAGTTTCTATTTCAGCATTAGTTCTAAATAGTTGTATTGTTGCAGCAATAGCTGTTGGTGGTGGAACAGTTGCCTATATTGATGG AAATTATTTTATGAATATAGAAGGCAACTATAAAGCTGTCTATAAAGCTACTCTTAAAGCTATTAATGATAATAATGACTTTGTTCTAGTATCAAAAGATCTTGATCAAACAAAGCAAAATGCCGACATTGAAGGTGCTA CTAAAATTGATAGTACGAGTTTTAGTGTCAAAATTGAAAGACTGACAGATCAGGCTACTAAAGTGACAATCAAATTTGGTACTTTTGGCGATCAAGCAATGTCATCAACATTAATGGATCAGATCCAGGCAGCTGTACAT AAAGCTTCTTAGAAATGTACAAAAAACTCTACTTAATTATATTATCCACAATAATCGCAATCTCTCTTAATAGTTGTGTTGTTGCCGCTGTTTTAATTGGTACAGCAGTTGTTGCTGGAGGTACAGTATATTACATCAAT GGTAACTATATAATCGAAGTCCCTAAAGATATTAGAAGTGTATACAATGCTACAATCAAGACTATACAGATGGATAGTCAAAATAAACTAATAAGTCAAACCTATAATACTAAATCTGCTATAATTAAAGCTTTACAAAA AGGTGAAAAAATTAGTATAGATTTAAGCAATATTGATAGTCGTTCAACAGAGATAAAAATTCGTATAGGTGTACTTGGCGATGAGAAAAAATCTGCTGATTTAGCAAACTCAATAACAAAAAATATCACCTAAGCAATAT TTCTCGAACTTTGGTTAACTTTTTCTTTTTAAAAACTTTCAAAAATGTATAATTTGTGTTAGTTTGCAAACTACCCTTATATCCATAATGAGTAATAAGGTATTAGATACATATTATAAAAACAATCGACATATTTGGGT GCTAGTACTATCTGGTGCTGTTATAGGCACAATGATTGGTCTTCTAGCAACAGCATTTCAGCTACTCCTAGACTTTATTTTTAAAATTAAGCTGGCTCTTTTTTCTTTCAGTGGTGGTAATCTTTTTATCGAAATCGCTA TGTCAATATCATTAAGTATTGTGATGGTATTAATTTCGATTTTTATTGTTAAAAAATTTGCGAAAGAGGCTGGTGGTAGCGGTATCCAAGAGGTTGAGGGTGCTTTAAAAGGCTGCCGCAAAATACGTAAAAGAGTTATG CCCGTGAAGTTTATAAGTGGACTTTTTTCGTTAGGCTCAGGTTTAAGTTTAGGTAAAGAGGGACCATCAATTCATATGGCTGCTGCATTAGCGCAGTTTTTTGTTGATAAATTTAAACTTACTACAAAATATGCTAATGC GGTTATCTCTGCTGGGGCTGGAGCTGGACTAGCAGCTGCTTTTAATACCCCACTTTCTGGGATTATCTTTGTTATTGAAGAGATGAATAGAAAGTTTAGATTTAGTGTTTCGGCAATAAAGTGTGTGCTAGTAGCATGTA TCATGAGTACAGTTATCTCTAGAGCTATTATGGGTAATCCTCCAGCAATACGCGTAGAAACTTTCAGCTCAGTACCACAAAATACTCTTTGGTTATTTATGGTATTAGGGATTATATTTGGTTATTTTGGTTTACTATTT AACAAATCCTTAATCAAAGTGGCAAACTTTTTCTCAGAAGGCTCCAAGAAGAGGTATTGGACTTTAGTTATAATTGTTTGCATAATTTTTGGTATTGGTGTTGTTCTATCTCCAAATGCTGTTGGCGGTGGCTATATTGT CATAGCAAATACTCTTGATTATAACTTATCAATCAAGATGCTTTTAGTGCTTTTTGTACTTCGTTTTGCTGGAGTTATTTTCTCATATGGCACCGGCGTTACTGGTGGGATATTCGCACCAATGATTGCGCTTGGTACTG

16. Evolution of genome sizes  C-value: 1pg ~= 1.02Gb  Thale cress (Arabidopsis thaliana): 0.16 pg  Fruit fly (Drosophila melanogaster): 0.18 pg  Pufferfish (Takifugu rubripes): 0.4 pg  Human (Homo sapiens): 3.5 pg  Onion (Allium cepa): 16.75 pg  Tiger salamander (Ambystoma tigrinum): 32 pg  Marbled lungfish (Protopterus aethiopicus): 132 pg http://www.rbgkew.org.uk/

17. Genic region and genome size Dan Graur

18. What's in the genome Genome Annotated genes Exon UTR Intron Cis-regulatory elements Selfish elements Novel genes Dead genes (pseudogenes)

19. "Non-genic": repetitive elements  E.g. Human genome  Exons take up?  Introns account for?  Repetitive elements occupy?  Unknown? Venter et al. (2001) Science 291:1304 ABC 1%24%25% 24%1% 25% 35%60% 45% 40%15% 5%

20. What are in the unknown regions?  Investigate with tiling array cDNA array Tiling array Gap size: 10bp Probe size: 25bp  Number of features:  Arabidopsis, 135Mb, 1 chip, ~6x106 features  Human, 3Gb, 7 chips, ~4.2x107 features

21. "Non-genic": unannotated genes Kapranov et al., 2002. Science  Tiling array analysis of human Chr 21, 22

22. Tiling array analysis of human transcriptome Kapranov et al., 2002. Science  Human Chr 21, 22  What do you think these expressed regions represent??

23. Difficulties for coding gene prediction  Training data  You need to know something...  “Biased” toward the properties of the majority.  Real genes that are shorter tend to be much harder to predict. Table 3 Accuracy of GISMO, Glimmer and CRITICA in predicting short genes (<300 bp) Gene finder Cor Sn Sn fk (%) Sp GISMO 0.64 63.0 86.4 69.0 Glimmer 0.54 72.0 83.7 44.0 CRITICA 0.60 46.0 67.4 84.0 Sn fk denotes the sensitivity in detecting function-known genes. Krause et al., 2006. Nucleic Acid Res. 35:540

24. Novel coding sequence identification  Arabidopsis thaliana as an example  135Mb, ~50% occupied by annotated genes.  Focus on coding sequences 90-300bp long.  What would you do next to eliminate ORFs that are likely false predictions? 133,090 sORFs

25. Criterion 1: Codon usage bias  Some codons are used more frequently than others http://www.cbs.dtu.dk/services/GenomeAtlas/

26. Criterion 1: Codon usage bias  For example: codons for proline  Suppose you have the following 2 sequences both code for poly-leucine, which one is more likely to be real coding sequence? NCDSCDS CCT0.250.12 CCC0.250.49 CCA0.250.06 CCG0.250.33 Seq1CCT CCA CCT Seq2CCC CCG CCC

27. Novel CDS identification  Posterior probability calculation  Bayes' theorm

28. Non-coding sequences Coding sequences Novel CDS identification  Determine base composition probabilities  Feature tables Coding sequences Non-coding sequences CDS parameters NCDS parameters c1c2c3 c4c5c6 n ATGC AAA0.010.310.030.02 AAT0.030.170.010.15 AAG0.020.000.02 AAC0.150.02 0.05 ATA0.040.030.050.00 ATT0.060.010.070.02 ATC0.01 0.050.29...

29. Posterior probability of coding sequence  Compare known non-coding and coding sequences Hanada et al., 2007. Genome Res.

30. Posterior probability of coding sequence  Scanning Arabidopsis genome Hanada et al., 2007. Genome Res.

31. After applying the first criterion 7,442 coding sORFs

32. How good is the CDS finding measure  For the training data  For 18 Arabidopsis small protein genes  All 18 are predicted as CDS.  For 84 yeast small protein genes  All 84 are predicted as CDS.

33. So what does this mean?  If a sequence is a true coding sequence  Our approach can predict them with high accuracy.  So, the sensitivity is very good.  Is this good enough??  What about specificity?  Namely, how good is the criteria in excluding false positives?

34. Criterion 2: Expression  What would be the expression level you would expect for true CDS compared to false CDS? Tiling array Gap size: 10bp Probe size: 25bp Expression level Frequency

35. Comparison of expression levels A: Exon B: Intron C: Prediceted novel CDS D: tRNA E: rRNA  Exon, intron, tRNA, rRNA, our predictions

36. Applying the second criterion  Prediction significantly enriched in expressed sequences 2,996 transcribed sORFs

37. Criterion 3: Purifying selection  Compare known coding and non-coding sequences

38. Criterion 3: Purifying selection  Compare known coding and non-coding sequences

39. Our research interests 30,000 25,000 10,000 6,000 45,000 17,000

40. Duplication Mechanism and Loss Rate Gene Duplications MechanismsConsequences Preferential retention Preferential retention Consequences

41. Duplication mechanisms +  Whole genome duplication  Tandem duplication  Segmental duplication  Duplicative transposition

42. Differences in Duplicability CategoryArabidopsisHuman Defense response Proteolysis Transport Ion channel activity Metabolism Development Protein kinase activity Transcription factor activity  Duplicability  The propensity for the retention of a duplicate gene  Computational analysis of genome-wide trend

43. Functional Consequences of Duplication  Functional divergence and conservation  Is it because of changes in cis-regulatory elements or coding sequences  How are duplicates retained, subfunctionalization or neofunctionalization

44. Acknowledgement  Lab members Kousuke Hanada Melissa Lehti-Shiu Cheng Zou  TIGR  Chris Town  Hank Wu  University of Chicago  Wen-Hsiung Li  Justin O. Borevitz  Xu Zhang  Funding