Sequence Analysis with Artemis and Artemis Comparison Tool (ACT) Carribean Bioinformatics Workshop

1. Sequence Analysis with Artemis and Artemis Comparison Tool (ACT) Carribean Bioinformatics Workshop 18th-29th January , 2010

2. atcttttacttttttcatcatctatacaaaaaatcatagaatattcatcatgttgtttaaaataatgtattccattatgaactttattacaaccctcgttatcttttacttttttcatcatctatacaaaaaatcatagaatattcatcatgttgtttaaaataatgtattccattatgaactttattacaaccctcgtt tttaattaattcacattttatatctttaagtataatatcatttaacattatgttatcttcctcagtgtttttcattattatttgcatgtacagtttatca tttttatgtaccaaactatatcttatattaaatggatctctacttataaagttaaaatctttttttaattttttcttttcacttccaattttatattccg cagtacatcgaattctaaaaaaaaaaataaataatatataatatataataaataatatataataaataatatataatatataataaataatatataatat ataatatataataaataatatataatatataatatataataaataatatataataaataatatataatatataatatataatactttggaaagattattt atatgaatatatacacctttaataggatacacacatcatatttatatatatacatataaatattccataaatatttatacaacctcaaataaaataaaca tacatatatatatataaatatatacatatatgtatcattacgtaaaaacatcaaagaaatatactggaaaacatgtcacaaaactaaaaaaggtattagg agatatatttactgattcctcatttttataaatgttaaaattattatccctagtccaaatatccacatttattaaattcacttgaatattgttttttaaa ttgctagatatattaatttgagatttaaaattctgacctatataaacctttcgagaatttataggtagacttaaacttatttcatttgataaactaatat tatcatttatgtccttatcaaaatttattttctccatttcagttattttaaacatattccaaatattgttattaaacaagggcggacttaaacgaagtaa ttcaatcttaactccctccttcacttcactcattttatatattccttaatttttactatgtttattaaattaacatatatataaacaaatatgtcactaa taatatatatatatatatatatatatatatatattataaatgttttactctattttcacatcttgtccttttttttttaaaaatcccaattcttattcat taaataataatgtattttttttttttttttttttttttattaattattatgttactgttttattatatacactcttaatcatatatatatatttatatat atatatatatatatatatatattattcccttttcatgttttaaacaagaaaaaaaactaaaaaaaaaaaaaataataaaatatatttttataacatatgt attattaaaatgtatatataaaaatatatattccatttattattatttttttatatacattgttataagagtatcttctcccttctggtttatattacta ccatttcactttgaacttttcataaaaattaatagaatatcaaatatgtataatatataacaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaata tatatatatatatatatacatataatatatatttcatctaatcatttaaaattattattatatattttttaaaaaatatatttatgataacataaaaaga atttaattttaattaaatatatataattacatacatctaatattattatatatatataataagttttccaaatagaatacttatatattatatatatata tatatatatatatattcttccataaaaagaataaaataaaataaaaacaccttaaaagtatttgtaaaaaattccccacattgaatatatagttgtattt ataaaattaaagaaaaagcataaagttaccatttaatagtggagattagtaacattttcttcattatcaaaaatatttatttcctaattttttttttttg taaaatatatttaaaaatgtaatagattatgtattaaataatataaatatagcaaaatgttcaattttagaaatttgcctctttttgacaaggataattc aaaagatacaggtaaaaaaaaaaaaataaagtaaaacaaaacaaaacaaaaaacaaaaaaaaaaaaaaaaaaaaaaatgacatgttataatataatataa taaataaaaattatgtaatatatcataatcgaagaaacatatatgaaaccaaaaagaaacagatcttgatttattaatacatatataactaacattcata tctttatttttgtagatgatataaaaaattttataaactcttatgaagggatatatttttcatcatccaataaatttataaatgtatttctagacaaaat tctgatcattgatccgtcttccttaaatgttattacaataaatacagatctgtatgtagttgatttcctttttaatgagaaaaataagaatcttattgtt ttagggtaatgaaatatatatagatttatatttttatttatttattatatattattttttaatttttcttttatatatttattttatttagtgtataaaa tgatatcctttatatttatatttacatgggatattcaaataataacaaaaatgagtatacacatatatatatatatatatatatatatgtatattttttt tttttttttatgttcctataggaaagggaagaattcactgatttgtagtgtttacaatattagggaatgcaactttacacttttgaaaaaaattcagtta agcaaaaatattaataacattaaaaagacactgatagcaaaatgtaatgaatatataataacattagaaaataagaaaattactttttatttcttaaata aagattatagtataaatcaaagtgaattaatagaagacggaaaagaacttattgaaaatatctatttgtcaaaaaatcatatcttgttagtaataaaaaa ttcatatgtatatatataccaattagatattaaaaattcccatattagttatacacttattgatagtttcaatttaaatttatcctacctcagagaatct ataaataataaaaaaaagcatataaataaaataaatgatgtatcaaataatgacccaaaaaaggataataatgaaaaaaatacttcatctaataatataa

3. atcttttacttttttcatcatctatacaaaaaatcatagaatattcatcatgttgtttaaaataatgtattccattatgaactttattacaaccctcgttatcttttacttttttcatcatctatacaaaaaatcatagaatattcatcatgttgtttaaaataatgtattccattatgaactttattacaaccctcgtt tttaattaattcacattttatatctttaagtataatatcatttaacattatgttatcttcctcagtgtttttcattattatttgcatgtacagtttatca tttttatgtaccaaactatatcttatattaaatggatctctacttataaagttaaaatctttttttaattttttcttttcacttccaattttatattccg cagtacatcgaattctaaaaaaaaaaataaataatatataatatataataaataatatataataaataatatataatatataataaataatatataatat ataatatataataaataatatataatatataatatataataaataatatataataaataatatataatatataatatataatactttggaaagattattt atatgaatatatacacctttaataggatacacacatcatatttatatatatacatataaatattccataaatatttatacaacctcaaataaaataaaca tacatatatatatataaatatatacatatatgtatcattacgtaaaaacatcaaagaaatatactggaaaacatgtcacaaaactaaaaaaggtattagg agatatatttactgattcctcatttttataaatgttaaaattattatccctagtccaaatatccacatttattaaattcacttgaatattgttttttaaa ttgctagatatattaatttgagatttaaaattctgacctatataaacctttcgagaatttataggtagacttaaacttatttcatttgataaactaatat tatcatttatgtccttatcaaaatttattttctccatttcagttattttaaacatattccaaatattgttattaaacaagggcggacttaaacgaagtaa ttcaatcttaactccctccttcacttcactcattttatatattccttaatttttactatgtttattaaattaacatatatataaacaaatatgtcactaa taatatatatatatatatatatatatatatatattataaatgttttactctattttcacatcttgtccttttttttttaaaaatcccaattcttattcat taaataataatgtattttttttttttttttttttttttattaattattatgttactgttttattatatacactcttaatcatatatatatatttatatat atatatatatatatatatatattattcccttttcatgttttaaacaagaaaaaaaactaaaaaaaaaaaaaataataaaatatatttttataacagatgt attattaaaatgtatatataaaaatatatattccatttattattatttttttatatacattgttataagagtatcttctcccttctggtttatattacta ccatttcactttgaacttttcataaaaattaatagaatatcaaatatgtataatatataacaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaata tatatatatatatatatacatataatatatatttcatctaatcatttaaaattattattatatattttttaaaaaatatatttatgataacataaaaaga atttaattttaattaaatatatataattacatacatctaatattattatatatatataataagttttccaaatagaatacttatatattatatatatata tatatatatatatattcttccataaaaagaataaaataaaataaaaacaccttaaaagtatttgtaaaaaattccccacattgaatatatagttgtattt ataaaattaaagaaaaagcataaagttaccatttaatagtggagattagtaagtttttcttcattatcaaaaatatttatttcctaattttttttttttg taaaatatatttaaaaatgtaatagattatgtattaaataatataaatatagcaaaatgttcaattttagaaatttgcctctttttgacaaggataattc aaaagatacaggtaaaaaaaaaaaaataaagtaaaacaaaacaaaacaaaaaacaaaaaaaaaaaaaaaaaaaaaaatgacatgttataatataatataa taaataaaaattatgtaatatatcataatcgaagaaacatatatgaaaccaaaaagaaacagatcttgatttattaatacatatataactaacattcata tctttatttttgtagatgatataaaaaattttataaactcttatgaagggatatatttttcatcatccaataaatttataaatgtatttctagacaaaat tctgatcattgatccgtcttccttaggtgttattacaataaatacagatctgtatgtagttgatttcctttttaatgagaaaaataagaatcttattgtt ttagggtaatgaaatatatatagatttatatttttatttatttattatatattattttttaatttttcttttatatatttattttatttagtgtataaaa tgatatcctttatatttatatttacatgggatattcaaataataacaaaaatgagtatacacatatatatatatatatatatatatatgtatattttttt tttttttttatgttcctataggaaagggaagaattcactgatttgtagtgtttacaatattagggaatgcaactttacacttttgaaaaaaattcagtta agcaaaaatattaataacattaaaaagacactgatagcaaaatgtaatgaatatataataacattagaaaataagaaaattactttttatttcttaaata aagattatagtataaatcaaagtgaattaatagaagacggaaaagaacttattgaaaatatctatttgtcaaaaaatcatatcttgttagtaataaaaaa ttcatatgtatatatataccaattagatattaaaaattcccatattagttatacacttattgatagtttcaatttaaatttatcctacctcagagaatct ataaataataaaaaaaagcatataaataaaataaatgatgtatcaaataatgacccaaaaaaggataataatgaaaaaaatacttcatctaataatataa • Sequencingisjustthebeginning of theprocess • Extractinginformation & interpreting • What´sthere • where are the genes • which genes • howtofindthem? • SEQUENCE ANNOTATION

4. Strategies for sequence annotation • Predictive methods Interpretation of the DNA sequence into genes according to rules • Comparative methods • Experimental methods

7. Strategies for sequence annotation • Predictive methods Interpretation of the DNA sequence into genes according to rules Interpretation of the DNA sequence into genes according to similarities with other sequences • Comparative methods • Experimental methods

9. Strategies for sequence annotation • Predictive methods Interpretation of the DNA sequence into genes according to rules Interpretation of the DNA sequence into genes according to similarities with other sequences • Comparative methods Interpretation of the DNA sequence into genes according to experimental results (e.g. cDNA) • Experimental methods

10. EST Blast Hit

11. Gene prediction programs: ORFs and CDSs ORFs are not equivalent to CDSs Not all open reading frames are coding sequences

12. Gene prediction Orpheus PHAT GeneMark Glimmer Gene finder

13. Gene finding programs • Genefinding software packages use Hidden Markov Models. • Predict coding, intergenic and intron sequences • Need to be trained on a specific organism. • Never perfect!

14. Gene prediction programs: Problems • ORFs are not equivalent to CDSs • Gene prediction programs find new genes that share properties with a given set of genes. • They can be confounded by: • Sequence constraints (ribosomal proteins etc.) • Sequence biases • Different sets of genes • Horizontal gene transfer • Non-coding DNA

15. Gene prediction programs: Problems Different gene training sets: Plasmodium falciparum Original annotation Updated annotation

16. Gene prediction programs: Problems Non-protein coding regions: S. typhi ribosomal RNA genes final genefinder orpheus glimmer glimmer orpheus genefinder final

17. Gene prediction programs: Problems Non-protein coding regions: N. meningitidis DNA repeats final orpheus glimmer glimmer orpheus final

18. Gene prediction programs: Problems Pseudogenes M. leprae

19. Gene prediction programs: Problems Pseudogenes: M. leprae Glimmer

20. Gene prediction programs: Problems Pseudogenes: M. leprae Pseudogenes: M. leprae ORPHEUS

21. Gene prediction programs: Problems Pseudogenes: M. leprae WUBLASTX vs. M. tuberculosis

22. Gene prediction programs: Problems Pseudogenes: M. leprae Final annotation

23. The Gene Prediction Process ESTs FASTA BlastX DNA SEQUENCE Usefull CDS Prediction ANNALYSIS SOFTWARE Gene finders Codon Usage AT content Annotator

24. EST Eukaryotic gene stop Exon III Exon II Exon I intron 3’UTR 5’UTR ATGGT AG GT AG CAP AAAAAAAAAA mRNA CAP AAAAAAAAAA TTTTTTTTT cDNA TTTTTTTTT EST

25. AT content • Coding regions have higher GC content in AT rich genomes

26. AT content

27. CODON USAGE • Codon bias is different for each organism. • DNA content in coding regions is restricted • but it is not restricted in non coding regions. • The codon usage for any particular gene can influence expression.

28. Codon usage • All organisms have a preferred set of codons. MalariaTrypanosoma GUU 0.41 GUU 0.28 GUC 0.06 GUC 0.19 GUA 0.42 GUA 0.14 GUG 0.11 GUG 0.39

29. Codon Usage • http://www.kazusa.or.jp/codon/

30. Codon Usage in Artemis Forward frames Reverse frames

31. Codon usage & gene finding in : Leishmania

32. GC frame plot • Plots the third position GC content of each frame of a DNA sequence. • In coding DNA the GC content of the 3rd base is often higher. • Good prediction of coding in malaria and trypanosomes.

33. GC frame plot of tubulin gene cluster on T. brucei Chr 1

34. Homology Data • Coding regions are more conserved than non coding regions due to selective pressure. • Comparing all possible translations against all known proteins will give clues to known genes. • Blastx

35. Gene finding: using ACT P. yoelii TBLASTX comparisons P. falciparum P. knowlesi

36. Gene finding by RNA-Seq (Transcriptional landscape of Neosporacaninum Tachyzoites Day 3 Tachyzoites (RNAseq) Day 4 Tachyzoites (RNAseq)

37. Transcriptome sequencing in Neospora (RNAseq is useful for predicting/confirming UTR boundaries) Day 3 Tachyzoites (RNAseq) Day 4 Tachyzoites (RNAseq) N. caninum Chr08 TBLASTX matches visualised in ACT T. gondii Chr08 3’ UTR 5’ UTR

38. RNA-Seq: correcting gene models After Before __16hr, __32hr, __48hr %GC %GC