1. Sequence & course material repository http://gfx.dnalc.org/files/evidence Annotation (sequences & evidence) Manuals (DNA, Subway, Apollo, JalView) Presentations (.ppt files) Prospecting (sequences) Readings (Bioinformatics tools, splicing, etc.) Worksheets (Word docs, handouts, etc.) BCR-ABL (temporary; not course-related)

2. Manifestations of a Code Genes, genomes, bioinformatics and cyberspace – and the promise they hold for biology education

3. Plants are amazing – and so are their genomes Largest flower (~ 1m)Oldest plant (> 5000 years) Tallest organism (> 100m) Slide: ASPB, 2009

4. A GENOME is all of a living thing’s genetic material. The genetic material is DNA (DeoxyriboNucleic Acid) DNA, a double helical molecule, is made up of four nucleotide “letters”: A----G T----C What is a genome? Slide: JGI, 2009

5. Just as computer software is rendered in long strings of 0s and 1s, the GENOME or “ software ” of life is represented by a string of the four nucleotides, A, G, C, and T. To understand the software of either - a computer or a living organism - we must know the order, or sequence, of these informative bits. What is sequencing? Slide: JGI, 2009

6. Exciting? >mouse_ear_cress_1080 GAAATAATCAATGGAATATGTAGAGGTCTCCTGTACCTTCACAGAGATTCTAGGCTGAGAGCAGTGCATATAGATATCTTT CGTACTCATCTGCTTTTTCTGGTCTCCATCACAAAAGCCAACTAGGTAATCATATCAATCTCTCTTTACCGTTTACTCGAC CTTTTCCAATCAGGTGCT TCTGGTGTGTCTACTACTATCAGTTTTAGGTCTTTGTATACCTGATCTTATCTGCTACTG AGGCTTGTAAAAGTGATTAAAACTGTGACATTTACTCTAAGAGAAGTAACCTGTTTGATGCATTTCCCTAATATACCGGTG TGGAAAAGTGTAGGTATCTGTACTCAGCTGAAATGGTGGACGATTTTGAAGAAGATGAACTCTCATTGACTGAAAGCGGGT TGAAGAGTGAAGATGGCGTTATTATCGAGATGAATGTCTCCTGGATGCTTTTATTATCATGTTTGGGAATTTACCAAGGGA GAGGTATCAGAATCTATCTTAGAAGGTTACATTTAGCTCAAGCTTGCATCAACATCTTTACTTAGAGCTCTACGGGTTTTA GTGTGTTTGAAGTTTCTTAACTCCTAGTATAATTAGAATCTTCTGCAGCAGACTTTAGAGTTTTGGGATGTAGAGCTAACC AGAGTCGGTTTGTTTAAACTAGAATCTTTTTATGTAGCAGACTTGTTCAGTACCTGAATACCAGTTTTAAATTACCGTCAG ATGTTGATCTTGTTGGTAATAATGGAGAAACGGAAGAATAATTAGACGAAACAAACTCTTTAAGAACGTATCTTTCAGTTT TCCATCACAAATTTTCTTACAAGCTACAAAAATCGAACTATATATAACTGAACCGAATTTAAACCGGAGGGAGGGTTTGAC TTTGGTCAATCACATTTCCAATGATACCGTCGTTTGGTTTGGGGAAGCCTCGTCGTACAAATACGACGTCGTTTAAGGAAA GCCCTCCTTAACCCCAGTTATAAGCTCAAAGTTGTACTTGACCTTTTTAAAGAAGCACGAAACGAAAAACCCTAAAATTCC CAAGCAGAGAAAGAGAGACAGAGCAAGTACAGATTTCAACTAGCTCAAGATGATCATCCCTGTTCGTTGCTTTACTTGTGG AAAGGTTGATATTTTCCCCTTCGCTTTGGTCTTATTTAGGGTTTTACTCCGTCTTTATAGGGTTTTAGTTACTCCAAATTT GGCTAAGAAGAGATCTTTACTCTCTGTATTTGACACGAATGTTTTTAATCGGTTGGATACATGTTGGGTCGATTAGAGAAA TAAAGTATTGAGCTTTACTAAGCTTTCACCTTGTGATTGGTTTAGGTGATTGGAAACAAATGGGATCAGTATCTTGATCTT CTCCAGCTCGACTACACTGAAGGGTAAGCTTACAATGATTCTCACTTCTTGCTGCTCTAATCATCATACTTTGTGTCAAAA AGAGAGTAATTGCTTTGCGTTTTAGAGAAATTAGCCCAGATTTCGTATTGGGTCTGTGAAGTTTCATATTAGCTAACACAC TTCTCTAATTGATAACAGAAGCTATAAAATAGATTTGCTGATGAAGGAGTTAGCTTTTTATAATCTTCTGTGTTTGTGTTT TACTGTCTGTGTCATTGGAAGAGACTATGTCCTGCCTATATAATCTCTATGTGCCTATCTAGATTTTCTATACAATTGATA TTTGATAGAAGTAGAAAGTAAGACTTAAGGTCTTTTGATTAGACTTGTGCCCATCTACATGATTCTTATTGGACTAATCAT TCTTTGTGTGAAAATAGAATACTTTGTCTGAACATGAGAGAATGGTTCATAATACGTGTGAAGTATGGGATTAGTTCAACA ATTTCGCTATTGGAGAAGCAAACCAAGGGTTAATCGTTTATAGGGTTAAGCTAATGCTCTGCTCTTTATATGTTATTGGAA CAGACTATTGTTGTGCCTATCTTGTTTAGTTGTAGATTCTATCTCGACTGTTATAAGTATGACTGAAGGCTTGATGACTTA TGATTCTCTTTACACCTGTAGAAGGATTTAAGCTTGGTGTCTAGATATTCAATCTGTGTTGGTTTTGTCTTTCTTTTGGCT CTTAGTGTTGTTCAATCTCCTCAATAGGTATGAAGTTACAATATCCTTATTATTTTGCAGGGACGCACTTGATGCACTCCA GCTAGTCAGATACTGCTGCAGGCGTATGCTAATGACCTTGCATCAACATCTTTACTTAGAGCTCTACGGGTTTTAGTGTGT

7. Much better

8. Find Gene Families Generate mathematical evidence Analyze large data amounts Browse in context Build gene models Gather biological evidence Annotation workflow Get DNA sequence

9. Walk or…

10. …take DNA Subway

11. Molecular biology and bioinformatics concepts RepeatMasker Eukaryotic genomes contain large amounts of repetitive DNA. Transposons can be located anywhere. Transposons can mutate like any other DNA sequence. FGenesH Gene Predictor Protein-coding information begins with start, is followed by codons, ends with stop. Codons in mRNA (AUG, UAA,…) have sequence equivalents in DNA (ATG, TAA,…). Most eukaryotic introns have “canonical splice sites,” GT---AG (mRNA: GU---AG). Gene prediction programs search for patterns to predict genes and their structure. Different gene prediction programs may predict different genes and/or structures. Multiple Gene Predictors The protein coding sequence of a mRNA is flanked by untranslated regions (UTRs). UTRs hold information for the half-lives of mRNAs and regulatory purposes. Gene > mRNA > CDS. BLAST Searches Gene or protein homologs share similarities due to common ancestry. Biological evidence is needed to curate gene models predicted by computers. mRNA transcripts and protein sequence data provide “hard” evidence for genes.

12. How do we find genes? Search for them Look them up

13. How do I get to this…

14. From this… >mouse_ear_cress_1080 GAAATAATCAATGGAATATGTAGAGGTCTCCTGTACCTTCACAGAGATTCTAGGCTGAGAGCAGTGCATATAGATATCTTT CGTACTCATCTGCTTTTTCTGGTCTCCATCACAAAAGCCAACTAGGTAATCATATCAATCTCTCTTTACCGTTTACTCGAC CTTTTCCAATCAGGTGCT TCTGGTGTGTCTACTACTATCAGTTTTAGGTCTTTGTATACCTGATCTTATCTGCTACTG AGGCTTGTAAAAGTGATTAAAACTGTGACATTTACTCTAAGAGAAGTAACCTGTTTGATGCATTTCCCTAATATACCGGTG TGGAAAAGTGTAGGTATCTGTACTCAGCTGAAATGGTGGACGATTTTGAAGAAGATGAACTCTCATTGACTGAAAGCGGGT TGAAGAGTGAAGATGGCGTTATTATCGAGATGAATGTCTCCTGGATGCTTTTATTATCATGTTTGGGAATTTACCAAGGGA GAGGTATCAGAATCTATCTTAGAAGGTTACATTTAGCTCAAGCTTGCATCAACATCTTTACTTAGAGCTCTACGGGTTTTA GTGTGTTTGAAGTTTCTTAACTCCTAGTATAATTAGAATCTTCTGCAGCAGACTTTAGAGTTTTGGGATGTAGAGCTAACC AGAGTCGGTTTGTTTAAACTAGAATCTTTTTATGTAGCAGACTTGTTCAGTACCTGAATACCAGTTTTAAATTACCGTCAG ATGTTGATCTTGTTGGTAATAATGGAGAAACGGAAGAATAATTAGACGAAACAAACTCTTTAAGAACGTATCTTTCAGTTT TCCATCACAAATTTTCTTACAAGCTACAAAAATCGAACTATATATAACTGAACCGAATTTAAACCGGAGGGAGGGTTTGAC TTTGGTCAATCACATTTCCAATGATACCGTCGTTTGGTTTGGGGAAGCCTCGTCGTACAAATACGACGTCGTTTAAGGAAA GCCCTCCTTAACCCCAGTTATAAGCTCAAAGTTGTACTTGACCTTTTTAAAGAAGCACGAAACGAAAAACCCTAAAATTCC CAAGCAGAGAAAGAGAGACAGAGCAAGTACAGATTTCAACTAGCTCAAGATGATCATCCCTGTTCGTTGCTTTACTTGTGG AAAGGTTGATATTTTCCCCTTCGCTTTGGTCTTATTTAGGGTTTTACTCCGTCTTTATAGGGTTTTAGTTACTCCAAATTT GGCTAAGAAGAGATCTTTACTCTCTGTATTTGACACGAATGTTTTTAATCGGTTGGATACATGTTGGGTCGATTAGAGAAA TAAAGTATTGAGCTTTACTAAGCTTTCACCTTGTGATTGGTTTAGGTGATTGGAAACAAATGGGATCAGTATCTTGATCTT CTCCAGCTCGACTACACTGAAGGGTAAGCTTACAATGATTCTCACTTCTTGCTGCTCTAATCATCATACTTTGTGTCAAAA AGAGAGTAATTGCTTTGCGTTTTAGAGAAATTAGCCCAGATTTCGTATTGGGTCTGTGAAGTTTCATATTAGCTAACACAC TTCTCTAATTGATAACAGAAGCTATAAAATAGATTTGCTGATGAAGGAGTTAGCTTTTTATAATCTTCTGTGTTTGTGTTT TACTGTCTGTGTCATTGGAAGAGACTATGTCCTGCCTATATAATCTCTATGTGCCTATCTAGATTTTCTATACAATTGATA TTTGATAGAAGTAGAAAGTAAGACTTAAGGTCTTTTGATTAGACTTGTGCCCATCTACATGATTCTTATTGGACTAATCAT TCTTTGTGTGAAAATAGAATACTTTGTCTGAACATGAGAGAATGGTTCATAATACGTGTGAAGTATGGGATTAGTTCAACA ATTTCGCTATTGGAGAAGCAAACCAAGGGTTAATCGTTTATAGGGTTAAGCTAATGCTCTGCTCTTTATATGTTATTGGAA CAGACTATTGTTGTGCCTATCTTGTTTAGTTGTAGATTCTATCTCGACTGTTATAAGTATGACTGAAGGCTTGATGACTTA TGATTCTCTTTACACCTGTAGAAGGATTTAAGCTTGGTGTCTAGATATTCAATCTGTGTTGGTTTTGTCTTTCTTTTGGCT CTTAGTGTTGTTCAATCTCCTCAATAGGTATGAAGTTACAATATCCTTATTATTTTGCAGGGACGCACTTGATGCACTCCA GCTAGTCAGATACTGCTGCAGGCGTATGCTAATGACCTTGCATCAACATCTTTACTTAGAGCTCTACGGGTTTTAGTGTGT

15. Meaning?

16. Mathematical Tools (Code; statistics)

17. Comparative Tools (Database searches)

18. What do we know about genes? Expressed (Transcribed) – Transcriptional start & termination sites (TXSS, TXTS) – Transcription artefacts (cDNA & ESTs) Regulated – Promoters (TATAAA) – Transcription Factor Binding Sites – CpG (Cytosin methylation) Meaningful (Translated) – 3n basepairs – Codon usage – Translational start & stop/termination codons (TLSS, TLTS) – Translation artefacts (proteins) Spliced – Splice sites (GT-AG) Derived (Homology: Paralogy/Orthology) – Search for known genes, proteins (BLAST)

19. How might this knowledge help to find genes? Predict genes – Look for potential starts and stops. – Connect them into open reading frames (ORFs). – Filter for “correct’ length & codon usage. Search databases – Known genes: UniGene – Known proteins: UniProt Use transcript evidence – cDNA – ESTs – proteins

20. Operating computationally Go to beginning of sequence  start SCAN If ATG  register putative TLSS; then – Move in 3-steps & count steps (=COUNTS) – If 3-step = (TAA or TAG or TGA),  register putative TLTS – If register  evaluate COUNTS (= triplets) If COUNTS < minimum  discard; then go behind ATG above and start SCAN If COUNTS > maximum  discard; then go behind ATG above and start SCAN If minimum < COUNTS < maximum  record as GENE with TLSS, TLTS; then go behind ATG above and start SCAN. Arrive at end of sequence  stop SCAN

21. Find gene families Mathematical evidence Analyze large data sets Browse in ccontext Construct gene models Annotation workflow Biological evidence Browse results Get/Generate sequence

22. Annotation Cheat Sheet Open existing project or generate new (Red square) Run RepeatMasker Generate evidence (Predictions, BLAST searches) Synthesize evidence into gene models (Apollo) Browse results locally and in context (Phytozome) Conduct functional analysis (link from Browser) Prospect for gene family (Yellow Line from Browser) Select region that holds biological gene evidence Optimize work space and zoom to region (View tab) Expand all tiers (Tiers tab) Drag evidence item(s) onto workspace (mouse) Edit to match biol. evidence (right-click item for tools) Record what was done in Annotation Info Editor Assess necessity to build alternative model(s) Upload model(s) to DNA Subway (File tab) A. DNA Subway B. Apollo

23. Predictors (mathematical evidence) Utilize predominantly mathematical methods (statistical). Search for patterns – Some score starts, stops, splice sites (GenScan). – Some score nucleotides (Augustus, FGenesH). Few incorporate EST data and/or known genes/proteins. Require optimization for each new species (training). Accuracy: – False positives (scoring non-genes as genes):5% - 50%. – False negatives (missed genes): 5%-40%. – Weak or unable in determining first and last exons, and UTRs. Specific for gene models (spliced genes, non-spliced genes). Specialty predictors (tRNA Scan, RepeatMasker).

24. Search tools (biological evidence) Search sequence databases: – Known genes – Known proteins – cDNAs & ESTs Utilize alignment methods (BLAST, BLAT). Reliability: – Good in determining gene locations and general gene structures. – Weak in exactly determining exon/intron borders. – Unlikely to correctly determine TXSS and TXTS. – Should be used with cDNA/EST from same species.

25. mRNA Splicing During RNA processing internal segments are removed from the transcript and the remaining segments spliced together. Internal RNA segments that are removed are named introns; the spliced segments are defined as exons. Causes mRNA to be “missing” segments present in DNA template and primary transcript. Most transcripts in eukaryotes spliced. Erosion: 1-exon genes (no exons without introns).

26. Exon Intron Pre-mRNA 5’ Splice Site 3’ Splice Site Reddy, S.N. Annu. Rev. Plant Biol. 2007 58:267-94 Of 1588 examined predicted splice sites in Arabidopsis 1470 sites (93%) followed the canonical GT…AG consensus. (Plant (2004) 39, 877–885) Canonical splice sites

27. Multiple splice variants = multiple proteins from the same gene Alternative Splicing Not a rare event!!! -Alternative splice sites C’ and D’ lead to different splice variants -JAZ10.3: premature stop codon in D exon, intact JAS domain -JAZ10.4: truncated C exon, protein lacks JAS domain -JAZ 10 encoded by At5G13220

28. Example: Jasmonate signaling in Arabidopsis -Plant hormone; affects cell division, growth, reproduction and responses to insects, pathogens, and abiotic stress factors. -Jasmonate Signaling Repressor Protein JAZ 10 splice variants JAZ 10.1, JAZ 10.3 and JAZ 10.4 differ in susceptibility to degradation. -Phenotypic consequences include male sterility and altered root growth.

29. Example: Disease resistance in tobacco -Nicotiana tabacum resistance gene N involved in resistance to TMV. -Alternative splicing required to achieve resistance. -Alternative transcripts N s (short) and N L (long). -N S encodes full-length, N L a truncated protein. -Splicevariants produced by alternative splicing confer resistance (D). -Splicevariants produced by cDNAs do not confer resistance (A, B, C).

30. Molecular biology and bioinformatics concepts RepeatMasker Eukaryotic genomes contain large amounts of repetitive DNA. Transposons can be located anywhere. Transposons can mutate like any other DNA sequence. FGenesH Gene Predictor Protein-coding information begins with start, is followed by codons, ends with stop. Codons in mRNA (AUG, UAA,…) have sequence equivalents in DNA (ATG, TAA,…). Most eukaryotic introns have “canonical splice sites,” GT---AG (mRNA: GU---AG). Gene prediction programs search for patterns to predict genes and their structure. Different gene prediction programs may predict different genes and/or structures. Multiple Gene Predictors The protein coding sequence of a mRNA is flanked by untranslated regions (UTRs). UTRs hold information for the half-lives of mRNAs and regulatory purposes. Gene > mRNA > CDS. BLAST Searches Gene or protein homologs share similarities due to common ancestry. Biological evidence is needed to curate gene models predicted by computers. mRNA transcripts and protein sequence data provide “hard” evidence for genes.

31. …take DNA Subway