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CACAO Biocurator Training

CACAO Biocurator Training. CACAO Fall 2011. CACAO. Syllabus What is CACAO & why is it important? Training Examples. Mutualistic Relationship. We want you to get experience with: CRITICALLY reading scientific papers Bioinformatics resources Collaborating with other biocurators

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CACAO Biocurator Training

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  1. CACAO Biocurator Training CACAO Fall 2011

  2. CACAO • Syllabus • What is CACAO & why is it important? • Training • Examples

  3. Mutualistic Relationship • We want you to get experience with: • CRITICALLY reading scientific papers • Bioinformatics resources • Collaborating with other biocurators • Synthesizing functional annotations • We want to get high quality functional annotations to contribute back to the GO Consortium and other biological databases

  4. What is an annotation? Hint: try looking for a definition on Wikipedia.

  5. What is a functional annotation? • Process of attaching information from the scientific literature to proteins

  6. Growing need for functional annotations • Advances in DNA sequencing mean lots of new genomes & metagenomes

  7. Classic MODel Literature Database Curators (rate limiting) Datasets

  8. Classic MODel is Expensive YIKES!

  9. Growing need for high quality functional annotations • High quality annotations allow us to infer the function of genes • Which allows us to understand the capabilities of genomes and understand the patterns of gene expression

  10. Two problems meet How can we incorporate more critical analysis into undergraduate education? How can we get more curators with finite budgets?

  11. What does a functional annotation have to do with this course? • Process of attaching information from the scientific literature to proteins • CACAO will teach you to become a biocurator • you will be adding functional annotations to the biological database GONUTS (http://gowiki.tamu.edu)

  12. CACAO Community Assessment - How well can Community - you (with our coaching) Annotation with - assign gene functions Ontologies - using GO?

  13. Can students become biocurators? YES! 1340 GO annotations in 2 & 1/2 semesters!

  14. Functional annotation with Gene Ontology • Controlled vocabulary with • Term identifiers • GO:0000075 • Name • cell cycle checkpoint • Definitions • "A point in the eukaryotic cell cycle where progress through the cycle can be halted until conditions are suitable for the cell to proceed to the next stage." [GOC:mah, ISBN:0815316194] • Relationships • is_a GO:0000074 ! regulation of progression through cell cycle • Terms arranged in a Directed Acyclic Graph (DAG)

  15. Why use Ontologies? • Standardization • facilitate comparison across systems • facilitate computer based reasoning systems • Good for data mining! • leading functional annotation ontology = Gene Ontology (GO)

  16. What is GO? Who is the GO Consortium (GOC)? • GO = ~30,000 terms for gene product attributes • Molecular Function (enzyme activity) • Biological Process (pathways) • Cellular Component (parts of the cell) • GO Consortium - set of biological databases that are involved in developing GO and contributing GO annotations

  17. Cellular Component • where a gene product acts

  18. Molecular Function • activities or “jobs” of a gene product glucose-6-phosphate isomerase activity figure from GO consortium presentations

  19. Biological Process • a commonly recognized series of events cell division Figure from Nature Reviews Microbiology 6, 28-40 (January 2008)

  20. Where can we find GO terms? GONUTS http://gowiki.tamu.edu

  21. Search for GO terms on GONUTS http://gowiki.tamu.edu

  22. Which subontology (MF, BP or CC) would the following terms fit in? GO:0003909 DNA ligase activity GO:0071705 Nitrogen compound transport GO:0007124 Pseudohyphal growth GO:0015123 Acetate transmembrane transporter activity GO:0071514 Genetic imprinting GO:0005773 Vacuole GO:0000312 Plastid small ribosomal subunit

  23. What do we know so far? Questions? 1. You will be making functional (GO) annotations using GO terms. 2. You can search for GO terms on GONUTS.

  24. Where are we adding GO annotations? GONUTS http://gowiki.tamu.edu

  25. Why are we using GONUTS? • Students can add functional annotations to proteins. • It has all the GO terms in it, too. • Some of the GO terms have usage notes. • It works a lot like Wikipedia, so it’s familiar. • It has the ability to keep track of each student’s and team’s annotations. • We run it. http://gowiki.tamu.edu

  26. REQUIRED parts of a GO annotation http://gowiki.tamu.edu/wiki/index.php/ECOLI:LPOB GO ** I will cover this again!!

  27. Parts of a GO annotation (cont) Evidence code

  28. Parts of a GO annotation (cont) Reference Notes (about evidence)

  29. What do we know so far? Questions? 1. You will be making functional (GO) annotations using GO terms. 2. You can search for GO terms on GONUTS. 3. You will be adding your GO annotations to GONUTS. 4. There are 4 required parts to a GO annotation. 5. You have to base your annotation on an experiment published in a scientific paper.

  30. Next week • Review of GO & GO annotations • More biocurator training • lots of examples • lots of practice BICH 485 & 689 students - please stick around to talk about these courses!

  31. Plan for training • Synthesizing GO annotations • Refinements • Judging & Assessment • Individual & Team tracking

  32. Part 1: Synthesizing GO annotations

  33. What can you annotate? • Proteins. • Any protein with a record in UniProt (Universal Protein Resource - http://uniprot.org) • How can you find proteins to annotate? • Think of ways to identify a protein or paper to annotate

  34. Choosing a protein to annotate 1. randomly 2. topics of interest (ie efflux pump proteins, biofilms, marine biology) 3. papers you have come across while doing other stuff 4. methods you know or want to learn 5. phenotypes and mutants you are interested in 6. by author 7. by pathway or regulon 8. suggested by another - high ratio of IEA:manual annotations in GONUTS - mentioned in another class 9. current paper mentions another gene product 10. review papers (ie Annual Reviews are excellent sources) 11. Uniprot, GONUTS, WikiPathways, PubMed searches 12. protein annotated by other teams 13. ask a coach

  35. Search for GO terms on GONUTS http://gowiki.tamu.edu

  36. Practice http://gowiki.tamu.edu 1. What is the GO term for GO:0004713? 2. What is the GO identifier for mitosis? 3. How many results (ballpark) do you get when you search for cell division using the Go, Search or G buttons? 4. How many child terms are there for plasma membrane? How many grandchildren? 5. What term is the parent of GO:006825?

  37. Finding a scientific paper on a certain protein • Has to be a scientific paper with experimental data in it. • Anything else is a valid reason to challenge! • PubMed, PubMed Central, GoogleScholar… • No review articles • no books, textbooks, wikipedia articles, class notes… • You will need the PMID number

  38. Practice - searching PubMed http://pubmed.org • How many papers do you get when you search for “coli”? • How many of those papers are reviews? • What is the title of the oldest paper when you search for “coli AND RNA polymerase”? • How many results are there when you search for “GTPase activity and Gene Ontology”? • What is the PMID of the paper when you search for “Hu JC AND coli AND lysR AND 2010”?

  39. Why do we annotate on GONUTS? • UniProt (Universal Protein Resource) will not let us annotate protein records on their site. • They are a professionally-curated & closed database. • GONUTS will. • GONUTS pulls the info from the UniProt record when it makes a page for you to edit.

  40. Making a protein page on GONUTS requires a UniProt accession • UniProt - http://www.uniprot.org • UniProt is not community edited, so we can’t add annotations directly to their database

  41. Practice - Searching UniProt Find the UniProt accessions for: • Mouse Lsr protein • Diptheria toxin from Corynebacterium • mutS from E. coli K-12 http://uniprot.org

  42. How do you make a new gene page in GONUTS? 2 1 • Use a UniProt accession to make a page on GONUTS that you can add your own annotations to. • GoPageMaker will: - Check if the page exists in GONUTS & take you there if it does. - Make a page & pull all of the annotations from UniProt into a table that you can edit.

  43. Practice http://gowiki.tamu.edu • How many annotations are on the page for the p53 protein from humans? • How many different evidence codes are there on the page for the Bub1a protein from mice? • Give one of the paper identifiers for an annotation for the LpxK protein from E. coli.

  44. What do we know so far? Questions? 1. You will be making functional (GO) annotations using GO terms. 2. You can search for GO terms on GONUTS. 3. You will be adding your GO annotations to GONUTS. 4. There are 4 required parts to a GO annotation. 5. You have to base your annotation on an experiment published in a scientific paper. You can annotate any protein with a record in UniProt. You have to make a page in GONUTS for your protein using the UniProt accession.

  45. What are evidence codes? • Describe the type of work or analysis done by the authors • 5 general categories of evidence codes: • Experimental • Computational • Author Statement • Curator Assigned • Automatically assigned by GO

  46. What are the evidence codes? • Describe the type of work or analysis done by the authors • 5 general categories of evidence codes: • Experimental • Computational • Author Statement • Curator Assigned • Automatically assigned by GO • CACAO biocurators may only use certain experimental and computational evidence codes

  47. Experimental Evidence Codes • IDA: Inferred from Direct Assay • IMP: Inferred from Mutant Phenotype • IGI: Inferred from Genetic Interaction • IEP: Inferred from Expression Pattern • IPI: Inferred from Physical Interaction • EXP: Inferred from Experiment

  48. Experimental Evidence Codes • IDA: Inferred from Direct Assay • IMP: Inferred from Mutant Phenotype • IGI: Inferred from Genetic Interaction • IEP: Inferred from Expression Pattern • IPI: Inferred from Physical Interaction • EXP: Inferred from Experiment http://geneontology.org/GO.evidence.shtml

  49. Computational Evidence Codes • ISS: Inferred from Sequence or Structural Similarity • ISO: Inferred from Sequence Orthology • ISA: Inferred from Sequence Alignment • ISM: Inferred from Sequence Model • IGC: Inferred from Genomic Context • IBA: Inferred from Biological Aspect of Ancestor • IBD: Inferred from Biological Aspect of Descendant • IKR: Inferred from Key Residues • IRD: Inferred from Rapid Divergence • RCA: Inferred from Reviewed Computational Analysis http://geneontology.org/GO.evidence.shtml

  50. Computational Evidence Codes • ISS: Inferred from Sequence or Structural Similarity • ISO: Inferred from Sequence Orthology • ISA: Inferred from Sequence Alignment • ISM: Inferred from Sequence Model • IGC: Inferred from Genomic Context • IBA: Inferred from Biological Aspect of Ancestor • IBD: Inferred from Biological Aspect of Descendant • IKR: Inferred from Key Residues • IRD: Inferred from Rapid Divergence • RCA: Inferred from Reviewed Computational Analysis http://geneontology.org/GO.evidence.shtml

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