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TAIR: Bringing together data for the global plant biology community

TAIR: Bringing together data for the global plant biology community. Philippe Lamesch Kate Dreher The Arabidopsis Information Resource www.arabidopsis.org contact us: curator@arabidopsis.org. Outline. Philippe Lamesch Introducing TAIR and PMN TAIR10 genome annotation

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TAIR: Bringing together data for the global plant biology community

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  1. TAIR: Bringing together data for the global plant biology community Philippe Lamesch Kate Dreher The Arabidopsis Information Resource www.arabidopsis.org contact us: curator@arabidopsis.org

  2. Outline • Philippe Lamesch • Introducing TAIR and PMN • TAIR10 genome annotation • TAIR gene confidence ranking • TAIR tools • Kate Dreher • Ee • Rr

  3. TAIR: The Arabidopsis Information Resource • collect, curate and distribute information on Arabidopsis • information freely available from arabidopsis.org

  4. Slides available from TAIR www.arabidopsis.org

  5. TAIR is used worldwide Visits per month (source: Google Analytics)

  6. TAIR usage worldwide : July 2009-July 2010

  7. What TAIR does: (1) Arabidopsis genome annotation

  8. What TAIR does: (2) manual literature curation • Controlled vocabulary annotations Gene Ontology (GO) http://www.geneontology.org/ Plant Ontology (PO) http://www.plantontology.org/ • Gene name, symbol • Allele, phenotype • Summary statement composition

  9. Who we partner with: PMN (Plant Metabolic Network) and PlantCyc A comprehensive plant biochemical pathway database, containing curated information from the literature and computational analyses about the genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism

  10. Who we partner with: ABRCDistribution of biological research materials

  11. Arabidopsis genome annotation • A new approach for improving the Arabidopsis genome annotation for TAIR10 • The Arabidopsis gene structure confidence ranking

  12. Arabidopsis genome annotation • Arabidopsis genome sequenced almost 10 years ago • High quality sequence with few gaps • TIGR did initial genome annotation • TAIR took over responsibility in 2005 • Current TAIR9 stats: 27,379 protein coding genes 4827 pseudogenes or transposable elements 1312 ncRNAs

  13. Genome annotation at TAIR Add novel genes Update exon/intron structures of existing genes Delete mispredicted genes Merge and split genes Change gene types Add splice-variants

  14. Genome annotation at TAIR Add novel genes Update exon/intron structures of existing genes Delete mispredicted genes Merge and split genes Change gene types Add splice-variants Annotate ‘atypical’ gene classes Short protein-coding genes Transposable element genes Trans. element Pseudogenes * * * ** * * uORFs (genes within UTR of other genes)

  15. Arabidopsis gene structure annotation A new approach TAIR6-TAIR9: Use ESTs and cDNAs and a assembly tool called PASA to improve gene structures TAIR10: Use new experimental data and new prediction tools to further improve gene structure predictions TAIR10

  16. Genome annotation TAIR6-TAIR9 Using PASA and ESTs/cDNAs NCBI Clustered transcripts

  17. Genome annotation TAIR6-TAIR9 Using PASA and ESTs/cDNAs NCBI Clustered transcripts Resulting gene model

  18. Genome annotation TAIR6-TAIR9 Using PASA and ESTs/cDNAs NCBI Clustered transcripts Resulting gene model comparison Previous gene model Novel genes New Splice-variants Gene structure updates

  19. Manual annotation at TAIR: Apollo Short MS peptide Radish sequence alignments Eugene prediction dicot sequence alignments monocot sequence alignments Aceview gene predictions cDNAs ESTs 2 gene isoforms

  20. TAIR10: using proteomics and RNA-seq data to improve genome annotation • 4-step process: • Mapping RNA seq & Peptides • Assembly/Gene built • Manual review • Integration (genome release/Gbrowse)

  21. Mapping and Assembly • Mapping • RNA-seq sequences (Tophat (C. Trapnell), Supersplat(T.C. Mockler)) • Peptides (6-frame translation, spliced exon graph) • Assembly approaches • Augustus (M. Stanke) • Uses spliced RNA seq reads, peptides • Aim: Identify additional splice-variants, update existing genes • TAU (T.C. Mockler) • Uses spliced RNA seq reads • Aim: Identify additional splice-variants • Cufflinks (C. Trapnell) • Uses spliced and unspliced RNA seq data • Aim: Identify novel genes

  22. Augustus RNA-seq datasets (Mockler Lab, Ecker Lab) TopHat, SuperSplat 200 Million aligned RNA-seq reads 203,000 clustered spliced RNA-seq junctions (spliced RNA-seq junction) 145,000 RNA-seq junctions based on >1 read

  23. Augustus 145,000 RNA-seq junctions based on >1 read 260,000 peptides (Baerenfaller et al, Castellana et al) + ESTs & cDNAs + AGI models Augustus gene prediction 11% of RNA-seq junctions incorporated into Augustus models 64% of peptide sequences incorporated into Augustus models Predicted Augustus models: 5461 distinct models 1596 novel models

  24. Categorisation/Review Incorrect junction in TAIR model Unsupported exon TAIR confidence rank TAIR Model Augustus Model (correction) TAU Models (Splice variants, NMD targets) RNA-seq Junctions (colour reflects matching model) Peptides

  25. Example Augustus update

  26. Example Augustus splice variant

  27. Example 2 August splice variant

  28. Augustus/TAU/Cufflinks Augustus • Incorporate 64% of peptides not contained in TAIR, 11 % for RNA-seq junctions • 5461 potential updated genes • 1596 potential novel genes TAU • 30,083 junctions distinct to Augustus or TAIR models • 10,902 junctions incorporated into 10,491 TAU models Cufflinks • 367 novel assemblies which fall above the 100 bp #TE-filter applied to AUG and cufflinks models

  29. Preliminary TAIR 10 Results Novel genes Updated genes Splice-variants B-list Rejects

  30. Preliminary TAIR 10 Results Novel genes 126 Updated genes 1182 Splice-variants 5885 (18% of all loci) B-list 1586 Rejects 2318

  31. Gene Confidence Rank • Attributes confidence scores to all exons and gene models based on different types of experimental and computational evidence

  32. Assigning A Confidence Rank E1 E4

  33. Full support No support

  34. New and updated tools at TAIR • N-Browse • GBrowse • Synteny viewer

  35. New and updated tools at TAIR • N-Browse (in collaboration wit the Kris Gunsalus Lab, NYU) • > 7,000 experimental interactions • Interactions curated by TAIR, IntAct & BioGrid • Tutorial at http://www.arabidopsis.org/tools/nbrowse.jsp#nb-tut

  36. N-Browse

  37. N-Browse: Finding information about edges (interactions)

  38. N-Browse: How to select and move nodes

  39. N-Browse: How to visualize GO terms from a selected set of nodes

  40. N-Browse: How to load your own file and overlay it with the curated interaction data

  41. N-Browse: How to save your session and export your data

  42. New Tools at TAIR • N-Browse • GBrowse • Synteny viewer

  43. GBrowse Header Main Browser Window Track Menu

  44. Alternative gene annotations • Eugene (transcript, proteins +) Thierry-Mieg (NCBI) • Gnomon (transcript, proteins) Souvorov (NCBI) • Aceview (transcript) SebastienAubourg • Hanada et al 2007 (3633 predicted genes)

  45. Incorrect start codon Proteomic Data • High-density Arabidopsis proteome map(Baerenfaller. 2008)

  46. VISTA plot Gbrowse track

  47. Transcriptome data

  48. Orthologs and Gene Families

  49. Variation

  50. Promoter Elements

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