1. Wellcome Trust Workshop Working with Pathogen Genomes Module 2 Gene Prediction

2. The Annotation Process DNA SEQUENCE ANNALYSIS SOFTWARE Useful Information Annotator

3. Gene finding Accurately predict sample set of genes Sequence base composition sequence alignment to related gene (e.g. orthologue) sequence alignment transcript data (e.g. EST) training set Gene finding software Full gene set

4. AT content Forward translations Reverse Translations DNA and amino acids DNA in Artemis

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

6. GC content Coding regions have higher GC content in AT-rich genomes

7. GC content

8. 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.

9. Codon usage All organisms have a preferred set of codons. Malaria Trypanosoma GUU 0.41 GUU 0.28 GUC 0.06GUC 0.19 GUA 0.42 GUA 0.14 GUG 0.11 GUG 0.39

10. Codon Usage http://www.kazusa.or.jp/codon/

11. Codon Usage Table UUU 34.3( 26847) UCU 15.3( 11956) UAU 45.6( 35709) UGU 15.3( 11942) UUC 7.3( 5719) UCC 5.3( 4141) UAC 5.5( 4340) UGC 2.4( 1872) UUA 49.2( 38527) UCA 18.2( 14239) UAA 1.0( 813) UGA 0.2( 188) UUG 10.1( 7911) UCG 2.8( 2154) UAG 0.2( 123) UGG 5.2( 4066) CUU 8.7( 6776) CCU 9.1( 7148) CAU 19.5( 15287) CGU 3.3( 2561) CUC 1.7( 1354) CCC 2.5( 1982) CAC 3.9( 3020) CGC 0.5( 354) CUA 5.4( 4217) CCA 13.1( 10221) CAA 25.1( 19650) CGA 2.4( 1878) CUG 1.3( 1044) CCG 0.9( 742) CAG 3.3( 2598) CGG 0.2( 184) AUU 34.0( 26611) ACU 12.8( 10050) AAU105.5( 82591) AGU 21.6( 16899) AUC 5.9( 4636) ACC 5.5( 4312) AAC 18.5( 14518) AGC 3.8( 2994) AUA 44.7( 34976) ACA 22.8( 17822) AAA 90.5( 70863) AGA 16.9( 13213) AUG 20.9( 16326) ACG 3.8( 2951) AAG 19.2( 15056) AGG 3.9( 3091) GUU 18.1( 14200) GCU 12.5( 9811) GAU 55.5( 43424) GGU 16.6( 12960) GUC 2.6( 2063) GCC 3.2( 2541) GAC 8.6( 6696) GGC 1.6( 1269) GUA 18.2( 14258) GCA 12.6( 9871) GAA 65.8( 51505) GGA 16.7( 13043) GUG 4.9( 3806) GCG 1.1( 890) GAG 10.1( 7878) GGG 2.9( 2243)

12. Codon Usage in Artemis Forward frames Reverse frames

13. Gene prediction: Amino acid usage: Correlation scores Within each window, plots correlation between amino acid usage in window and global amino-acid usage in EMBL “Magic number” = 52.7 Arbitrary units

14. Gene prediction: Correlation scores M. tuberculosis NADH dehydrogenase operon

15. Gene prediction: Positional base preference (FramePlot) Plots the GC content in each position of each reading frame of the DNA sequence. In G+C-rich organisms the GC content of the 3rd base is often higher; in A+T rich organisms it is lower. Good prediction of coding in malaria and trypanosomes and G+C-rich prokaryotes. G+C content of chromosome Frame- specific G+C content 1 2 3

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

18. What is an HMM A statistical model that represents a gene. Similar to a “weight matrix” but one that can recognise gaps and treat them in a systematic way. Has a different “states” that represent introns, exons, intergenic regions, etc Considers the “state” of preceding sequence

19. A typical HMM http://linkage.rockefeller.edu/wli/gene/krogh98.pdf

20. 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 –Sequence quality –Different sets of genes –Horizontal gene transfer –Non-coding DNA

21. Gene prediction programs: Problems Sequence composition variation Y. pestis ribosomal proteins glimmer orpheus final

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

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

24. Gene prediction programs: Problems Pseudogenes M. leprae

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

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

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

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

29. Gene prediction programs: Statistics Krogh+Larson pers comm 5 http://pedant.gsf.de/orpheus/ 3 http://www.tigr.org/softlab/glimmer/glimmer.html 1 Programgenessame start and stop same stop only total sharing stop false negative false positive Glimmer 2 1 67723101 56.2% 2310 41.8% 5411 98.0% 108 1.9% 1361 24.7% GeneMark 2 57623987 72.2% 1413 25.6% 5400 97.8% 119 2.2% 362 6.6% Glimmer 3 3 56993569 64.7% 1793 32.5% 5362 97.1% 157 2.8% 337 6.1% EasyGene 4 53574427 80.2% 772 14.0% 5199 94.2% 320 5.8% 158 2.9% Orpheus 5 51532736 49.6% 1799 32.6% 4535 82.2% 984 17.8% 618 11.2% Mycobacterium marinum; 6,636,827 bp, 65.7% G+C compared to manually curated gene set: 5519 genes (incl 46 pseudogenes) http://cbcb.umd.edu/software/glimmer/ 4 2 http://opal.biology.gatech.edu/GeneMark/

30. Gene prediction programs: Problems splicing Plasmodium falciparum Original annotation Updated annotation

31. 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

32. BLASTX

33. Blastx on frame lines

34. EST sequencing AAAAAAAAAA CAP AAAAAAAAAA CAP TTTTTTTTT intron exon 5’UTR M stop 3’UTR EST cDNA mRNA

35. ESTs

36. Showing Multiple Evidence

37. Schistosoma mansoni expression

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

39. highlightedmanually reviewed gene structure pale brownhit to H. contortus EST cluster in Nembase found using PASA brown-greenhit to H.contortus individual ESTs in NCBI database found using PASA pink/red blockshits to Uniprot bright greentwinscan prediction (homology based) pale pink snap prediction (ab initio) yellowhmmgene prediction (ab initio) pale bluegenscan prediction (ab initio) redgenefinder (ab initio) dark bluefgenesh prediction (ab initio) jade greenaugustus hints prediction (homology based) orangeaugustus prediction (ab initio) purplegenewise prediction (homology based) Gene prediction in eukaryotes: HMMs

40. A B P. falciparum gene predictions (PlasmoDB)

41. Gene prediction in eukaryotes: HMMs Dictyostelium discoideum gene predictions Bartfinder hmmgene geneid Phat EST (contig) combined prediction

42. Manual refinement P. falciparum P. knowlesi

43. Ongoing manual annotation e.g. PF14_0021, PF14_0022 P. falciparum P. vivax Revised annotation (back to Two genes!)

44. Using FASTA Results FASTA is a global alignment tool BLAST FASTA Reduces sensitivity increases specificity