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Chibucos annot go_final

Chibucos annot go_final



Gene Ontology

Gene Ontology



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  • In this report, we describe the cloning and expression of a Deinococcusradiodurans DNA ligase in Escherichia coli. This enzyme efficiently catalyses DNA ligation in the presence of Mn(II) and NAD+ as cofactors…

Chibucos annot go_final Chibucos annot go_final Document Transcript

  • Marcus C. Chibucos, Ph.D.
    Arabidopsis thaliana ATPase
    HMA4 zinc binding domain
    GO:0006829 : zinc ion transport (BP)
    GO:0005886 : plasma membrane (CC)
    GO:0005515 : protein binding (MF)
    Gene Annotation And Ontology
  • Outline of this talk
    • Background: the language of biology
    • Gene Ontology: overview, terms & structure
    • Annotating with GO and Evidence
    • Using annotation to facilitate your research
  • About screenshots in this talk
    AmiGO web-based ontology browser
    OBO-Edit stand-alone editor
  • What is annotation? Who is involved?
    Term confusion (what’s in a name?)
    Scale: the sea of data
    Controlled vocabularies & ontologies
    The Gene Ontology Consortium
    Background: the language of biology
  • Annotation
    annotate – to make or furnish critical or explanatory notes or comment.
    (Merriam-Webster dictionary)
    genome annotation – the process of taking the raw DNA sequence produced by the genome-sequencing projects and adding the layers of analysis and interpretation necessary to extract its biological significance and place it into the context of our understanding of biological processes.
    (Lincoln Stein, PMID 11433356)
    Gene Ontology annotation – the process of assigning GO terms to gene products… according to two general principles: first, annotations should be attributed to a source; second, each annotation should indicate the evidence on which it is based.
  • Diverse parties involved
    End-users, including various researchers
    Small-scale laboratory projects
    Whole genome sequencing projects
    From reading papers to computational analysis
    Ontology developers
    Create terms that reflect scientific knowledge
    Make interoperable ontologies, database links
    Developers of tools & resources
    Standards for storing & sharing data
    Web interfaces for data analysis & sharing
    Many areas of expertise
    Laboratory sciences – biology, chemistry, medicine, and many other disciplines
    Computational science – bioinformatics, genomics, statistics
    Software development & web design
    Philosophy – ontology & logic
  • Term confusion: synonyms
    Do biologists use precise & consistent language?
    Mutually understood concepts – DNA, RNA, or protein
    Synonym (one thing known by more than one name) – translation and protein synthesis
    Enzyme Commission reactions
    Standardized id, official name & alternative names
  • Term confusion: homonyms
    Homonyms common in biology – different things known by the same name
    Vascular (plant vasculature, i.e. xylem & phloem, or vascular smooth muscle, i.e. blood vessels?)
    Endospore formation Bacillus anthracis
    Reproductive sporulation
    Asci & ascospores, Morchellaelata(morel)
    ©L Stauffer 2003 (accessed 17-Sep-09)
    ©PG Warner 2008 (accessed 17-Sep-09)
  • Term confusion: homonyms and biological complexity
    AmiGO query “vascular”  51 terms
    In biology, many related phenomena are described with similar terminology
  • The problem of scale
    • Small data sets, small experiments & isolated scientific communities?
    • Enormous data sets
    • Microarray experiments
    • Whole genome sequencing projects
    • Comparative genomics of multiple diverse taxa
    • Computers don’t understand nuance
    • Millions of proteins to annotate
    • How to effectively search?
    • How to draw meaningful comparisons?
    (accessed 17-Sep-09)
  • The Gene Ontology (GO)
    Way to address the problems of synonyms, homonyms, biological complexity, increasing glut of data
    GO provides a common biological language for protein functional annotation
  • Controlled vocabulary (CV)
    An official list of precisely defined terms that can be used to classify information and facilitate its retrieval
    Think of flat list like a thesaurus or catalog
    Benefits of CVs
    Allow standardized descriptions of things
    Remedy synonym & homonym issues
    Can be cross-referenced externally
    Facilitate electronic searching
    A CV can be “…used to index and retrieve a body of literature in a bibliographic, factual, or other database. An example is the MeSH controlled vocabulary used in MEDLINE and other MEDLARS databases of the NLM.”
  • Ontology is a type of CV with defined relationships
    Ontology – formalizes knowledge of a subject with precise textual definitions
    Networked terms where child more specific (“granular”) than parent
    Less specific
    GO terms describe biological attributes of gene products…
    More granular
  • How GO works
    GO Consortium develops & maintains:
    Ontologies and cross-links between ontologies and different resources
    Tools to develop and use the ontologies
    SourceForge tracker for development
    People studying organisms at databases annotate gene products with GO terms
    Groups share files of annotation data about their respective organisms
    Because a common language was used to describe gene products and this information was shared amongst databases…
    We can search uniformly across databases
    Do comparative genomics of diverse taxa
  • GO on SourceForgesourceforge.net/projects/geneontology
  • The Gene Ontology Consortium
    Collaboration began 1998 among model organism databases mouse (MGI), fruit fly (FlyBase) and baker’s yeast (SGD)
    Michael Ashburner of FlyBase contributed the base vocabulary
    Today > 20 members & associates
    First publication 2000 (PMID 10802651)
    Today, PubMed query “gene ontology” yields 3,347 papers (27-Jun-2011)
    Organisms represented by GO annotations from every kingdom of life
    Many groups use GO in many different ways for their research
    Among eight OBO-Foundry ontologies
  • OBO Foundry ontologieswww.obofoundry.org
    Collaboration among developers of science-based ontologies
    Establish principles for ontology development
    Goal of creating a suite of orthogonal interoperable reference ontologies in the biomedical domain.
    many others…
  • What the GO is not
    GO comprises three ontologies
    Anatomy & storage of GO terms
    Ontology structure
    Detail of a term in AmiGO
    True path rule
    Gene Ontology:overview, terms & structure
  • Caveats – what GO is not
    Not gene naming system or gene catalog
    GO describes attributes of biological objects – “oxidoreductase activity” not “cytochromec”
    The three ontologies have limitations
    No sequence attributes or structural features
    No characteristics unique to mutants or disease
    No environment, evolution or expression
    No anatomy features above cellular component
    Not dictated standard or federated solution
    Databases share annotations as they see fit
    Curators evaluate differently
    GO is evolving as our knowledge evolves
    New terms added on daily basis
    Incorrect/poorly defined terms made obsolete
    Secondary ids – terms with same meaning merged
  • GO comprises three ontologies
    Cellular component ontology (CC)
    Molecular function ontology (MF)
    “protein binding”
    “peptidase activity”
    “cysteine-type endopeptidase activity”
    Biological process ontology (BP)
    Terms describe attributes of gene products (GPs)
    Any protein or RNA encoded by a gene
    Species-independent context, e.g. “ribosome”
    Could describe GPs found in limited taxa, e.g. “photosynthesis” or “lactation”
    One GP can be associated with ≥ 1 CC, BP, MF
    Example: Caspase-6 from Bostaurus
  • Cellular component ontology
    Describes location at level of subcellular structure & macromolecular complex
    GP subcomponent of or located in particular cellular component, with some exceptions:
    No individual proteins or nucleic acids
    No multicellular anatomical terms
    For annotation purposes, a GP can be associated with or located in ≥ one cellular component
    • Multi-subunit enzyme or protein complex
    • ribosome
    • proteasome
    • ubiquitinligase complex
    • Anatomical structure
    • rough endoplasmic reticulum
    • nucleus
    • nuclear inner membrane
  • Molecular function ontology
    Describe gene product activity at molecular level
    Describes attributes of entities
    Adenylate cyclase (E.C.
    Catalyzes a specific reaction:
    ATP = 3',5'-cyclic AMP + diphosphate
    Described by the Gene Ontology term:
    “adenylate cyclase activity” (GO:0004016)
    [accessed 4-Feb-2010]
    • Usually single GP, sometimes a complex
    • “ferritin receptor activity”
    • Definition: “combining with ferritin, an iron-storing protein complex, to initiate a change in cell activity”
    • Broad functions
    • “catalytic activity”
    • “transporter activity”
    • “binding”
    • Specific functions
    • “adenylatecyclase activity”
    • “protein-DNA complex transmembrane transporter activity”
    • “Fc-gamma receptor I complex binding”
  • Biological process ontology
    Describes recognized series of events or molecular functions with a defined beginning and end
    “GO does not try to represent the dynamics or dependencies that would be required to fully describe a pathway” (from GO documentation)
    Mutant phenotypes often reflect disruptions in BP
    • Specific process
    • “pyrimidine metabolism”
    • “α-glucosidase transport
    General considerations
    The Cell Cycle
    The Development Node
    Multi-Organism Process
    Detection of and Response to Stimuli
    Sensory Perception
    Signaling Pathways
    Transport and Localization
    Transporter activity (molecular function)
    Other Misc. Standard Defs
    • Broad process
    • “cellular physiological process”
    • “signal transduction”
  • Anatomy of a GO term
    Term name
    goid (unique numerical identifier)
    Synonyms (broad or narrow) for searching, alternative names, misspellings…
    Precise textual definition with reference stating source
    GO slim
    Ontology placement
  • Storage and cross referencing of GO terms
    • Storage in flat file (text)
    • Database cross reference for mappings to GO
    • GO term identical to object in other database
  • Ontology structure:parent-child relationship
    Parent term (broader)
    Child term (specialized)
    hexose metabolism
    monosaccharide biosynthesis
    hexose biosynthesis
    • Up in the tree is more general; down in the tree is more specific:
    • Annotation of genes
    • Start with terms denoting broad functional categories
    • Use more specific term as knowledge warrants
  • Ontology structure:terms arranged in DAGs
    GO terms structured as hierarchical-like directed acyclic graphs (DAGs)
    Tree-like, but each term can have more than one parent (pseudo-hierarchy)
    Each term may have one or more child terms (“siblings” share same parent)
    child term
    child terms
  • GO has three term relationships
    is_a - child is instance of parent (“A is_a B”)
    Class-subclass relationship
    part_of - child part of parent (“C part_of D”)
    When C present, part of D; but C not always present
    Nucleus always part_of cell; not all cells have nuclei
    Child term regulates parent term
    (Zoomed in view of biological process ontology depicted here.)
  • AmiGO for viewing terms
    Open source HTML-based application developed by the GO Consortium
    Interface for browsing, querying and visualizing OBO data
    Users can search GO terms or annotations
    Available via website or download for local install
    Example query with
    keyword “hemolysis” or goid GO:0019836
  • AmiGO search results
  • Term information in AmiGO
    Webpage continues…
  • AmiGO view continued
    Several informative views
    Number of gene products in GO annotation collection annotated to that term or one of its child terms
    Relationship between term and its parent
    Our term is much further down…
  • Graph view
    • Alternative view of network of terms
  • A term with two parents
    amine group
    carboxylic acid group
    generic amino acid
    • Name: amino acid transmembrane transporter activity
    • ID number: GO:0015171
    • Definition: Catalysis of the transfer of amino acids from one side of a membrane to the other. Amino acids are organic molecules that contain an amino group and a carboxyl group. [source: GOC:ai, GOC:mtg_transport, ISBN:0815340729]
    • parent term: amine transmembrane transporter activity (GO:0005275)
    • relationship to parent: “is_a”
    • parent term: carboxylic acid transmembrane transporter activity (GO:0046943)
    • relationship to parent: “is_a”
  • Multiple paths to root:graphical view in OBO-Edit
  • “True path rule”
    The pathway from a term all the way up to its top-level parent(s) must always be true for any gene product that could be annotated to that term (“if true for the child, then true for the parent”)
    Incorrect for Bacteria
    proton-transporting ATP synthase complex
    Correct for Bacteria (and Eukaryotes)
    proton-transporting ATP synthase complex
    plasma membrane proton-transporting ATP synthase complex
    mitochondrial proton-transporting ATP synthase complex
    plasma membrane
    plasma membrane proton-transporting ATP synthase complex
    mitochondrial inner membrane
    mitochondrial proton-transporting ATP synthase complex
    (Abbreviated versions of the actualtrees)
  • What is GO annotation?
    Literature curation at model organism databases
    The annotation file
    Evidence – critical for annotation
    Sequence similarity-based annotation
    Annotation specificity
    Annotating with GO and Evidence
  • GO annotation overview
    Associating a GO term with a gene product
    Goal is to select GO terms from all three ontologies to represent what, where, and how
    Linking a GO term to a gene product asserts that it has that attribute
    For example, 6-phosphofructokinase
    Molecular function
    GO:0003872 6-phosphofructokinase activity
    Biological process
    GO:0006096 glycolysis
    Cellular component
    GO:0005737 cytoplasm
    Annotation, whether based on literature or computational methods, always involves:
    Learning something about a gene product
    Selecting an appropriate GO term
    Providing an appropriate evidence code
    Citing a [preferably open access] reference
    Entering information into GO annotation file
  • Chaperone DnaK, one protein/multiple annotations
    Molecular function
    ATP binding (GO:0005524)
    ATPase activity (GO:0016887)
    unfolded protein binding (GO:0051082)
    misfolded protein binding (GO:0051787)
    denatured protein binding (GO:0031249)
    Biological process
    protein folding (GO:0006457)
    protein refolding (GO:0042026)
    protein stabilization (GO:0050821)
    response to stress (GO:0006950)
    Cellular component
    cytoplasm (GO:0005737)
  • Literature curation performed at model organism databases
    From the abstract:
  • Results section indicates a “direct assay” annotation
    They document the findings of a direct assay performed on purified protein:
    They further document the methods used, and evaluate the findings in the Discussion section…
  • Query AmiGO with “DNA ligase” & “DNA ligation”
    All “ligation” in biological process ontology
  • Resulting annotations
    Name: DNA ligase (stated in paper)
    Gene symbol: ligA (stated in paper)
    EC: (queried enzyme for “DNA ligase”)
  • Gene annotation file captures annotations
  • Evidence
    Essential to base annotation on evidence
    Conclusions more robust and traceable
    With evidence, a GO annotation is standard operating procedure (SOP)-independent
    Many types of evidence exist
    For example, experiment described in literature
    What method (e.g. direct assay, mutant phenotype, et cetera) was used?
    Did author cite references?
    Did author provide details of analyses?
    Perhaps you used a sequence-based method
    What were the methods of manual curation?
    Give accession numbers of similar sequences
    Provide any references describing methods
    Controlled vocabularies help here, too!
  • GO standard references
    A Hidden Markov Model (HMM) is a statistical representation of patterns found in a data set. When using HMMs with proteins, the HMM is a statistical model of the patterns of the amino acids found in a multiple alignment of a set of proteins called the "seed". Seed proteins are chosen based on sequence similarity to each other. Seed members can be chosen with different levels of relationship to each other...
    A Hidden Markov Model (HMM) is a statistical representation of patterns found in a data set. When using HMMs with proteins, the HMM is a statistical model of the patterns of the amino acids found in a multiple alignment of a set of proteins called the "seed". Seed proteins are chosen based on sequence similarity to each other. Seed members can be chosen with different levels of relationship to each other. They can be members of a superfamily (ex. ABC transporter, ATP-binding proteins), they can all share the same exact specific function (ex. biotin synthase) or they could share another type of relationship of intermediate specificity (ex. subfamily, domain). New proteins can be scored against the model generated from the seed according to how closely the patterns of amino acids in the new proteins match those in the seed. There are two scores assigned to the HMM which allow annotators to judge how well any new protein scores to the model. Proteins scoring above the "trusted cutoff" score can be assumed to be part of the group defined by the seed. Proteins scoring below the "noise cutoff" score can be assumed to NOT be a part of the group. Proteins scoring between the trusted and noise cutoffs may be part of the group but may not. One of the important features of HMMs is that they are built from a multiple alignment of protein sequences, not a pairwise alignment. This is significant, since shared similarity between many proteins is much more likely to indicate shared functional relationship than sequence similarity between just two proteins. The usefulness of an HMM is directly related to the amount of care that is taken in chosing the seed members, building a good multiple alignment of the seed members, assessing the level of specificity of the model, and choosing the cutoff scores correctly. In order to properly assess what functional relevance an above-trusted scoring HMM match has to a query, one must carefully determine what the functional scope of the HMM is. If the HMM models proteins that all share the same function then it is likely possible to assign a specific function to high-scoring match proteins based on the HMM. If the HMM models proteins that have a wide variety of functions, then it will not be possible to assign a specific function to the query based on the HMM match, however, depending on the nature of the HMM in question, it may be possible to assign a more general (family or subfamily level) function. In order to determine the functional scope of an HMM, one must carefully read the documentation associated with the HMM. The annotator must also consider whether the function attributed to the proteins in the HMM makes sense for the query based on what is known about the organism in which the query protein resides and in light of any other information that might be available about the query protein. After carefully considering all of these issues the annotator makes an annotation.
  • GO evidence codeswww.geneontology.org/GO.evidence.shtml
    EXP - inferred from experiment
    IDA - inferred from direct assay
    IEP inferred from expression pattern
    IGI - inferred from genetic interaction
    IPI - inferred from physical interaction
    IMP - inferred from mutant phenotype
    ISS - inferred from sequence or structural similarity
    ISA - inferred from sequence alignment
    ISO - inferred from sequence orthology
    ISM - inferred from sequence model
    IGC - inferred from genomic context
    ND - no biological data available
    IC - inferred by curator
    TAS - traceable author statement
    NAS - non-traceable author statement
    IEA - inferred from electronic annotation
    GO codes are a subset of yet another ontology!
  • Types of sequence similarity-based annotations
    Find similarity between gene product & one that is experimentally characterized
    BLAST-type alignments
    Shared synteny to establish orthology of genomic regions between species
    Find similarity between gene product and defined protein family
    HMMs (Pfam, TIGRFAMS)
    Find motifs in gene product with prediction tools
    Many (most?) information you find is based on transitive annotation and much of it has never been looked at by a human being!
  • Evaluation of sequence similarity-based information
    Visually inspect alignments & criteria
    Length & identity
    Conservation of catalytic sites
    Check HMM scores with respect to cutoff
    Look at available metabolic analysis
    Pathways, complexes?
    Information from neighboring genes
    Gene in an operon (common prokaryotes) can supplement weak similarity evidence
    Sequence characteristics
    Signal peptide?
    Known motifs that give a clue to function?
    Paralogous family member
  • An example: HI0678, a protein from H. influenzae…
    ...high quality alignment to experimentally characterized triosephosphateisomerase from Vibrio marinus
  • Information from Swiss-Prot database on experimentally characterized match protein
    further down the page
  • High quality…..
    …. full-length match, high percent identity (67.8%), conserved active and binding sites (boxed in red).
  • Resulting annotations
    gene symbol:tpiA
    (This, and the following annotations, came from the match protein.)
  • KEGG pathway for glycolysis core
  • KEGG pathway for glycolysis core
  • Resulting annotations
    name: triosephosphateisomerase
    gene symbol: tpiA
  • And another annotation
    The biologist knows that glycolysis takes place in the cytoplasm in bacteria, and so infers a cytoplasmic location for that protein (“inferred by curator” evidence code).
  • Annotation specificity should reflect knowledge
    GO trees
    (very abbreviated)
    catalytic activity
    kinase activity
    carbohydrate kinase activity
    ribokinase activity
    glucokinase activity
    fructokinase activity
    carbohydrate metabolism
    monosaccharide metabolism
    hexose metabolism
    glucose metabolism
    fructose metabolism
    pentose metabolism
    ribose metabolism
    Available evidence for three genes
    -good match to an HMM for “kinase”
    -good match to an HMM for “kinase”
    -a high-quality BER match to an experimentally characterized “glucokinase’ AND a ‘fructokinase’
    -good match to an HMM specific for “ribokinase”
    -a high-quality BER match to an experimentally characterized ribokinase
  • Using shared annotations
    Search for GO terms at databases
    Slims for broad classification
    GO tools
    Working with GO-limited data sets
    Using annotation to facilitate your research
  • Sharing annotations
    Annotation file sent to GO, put in repository
    All these data free to anyone
    Hundreds of thousands of GP annotations
    Annotation files all in same format
    Facilitates easy use of data by everyone
    Most of your favorite organism databases use these annotation files
  • Searching for GO terms at EuPathDB
  • 62
    Ontology slim
    Slim is a distilled (reduced) ontology
    Made by manually pruning low-level terms with an ontology editor
    Selected high-level terms remain
    Slims reduce ontology complexity
    Reduce clutter & see general trends
    Microarray experiments
    Comparative whole genome analyses
    Remove irrelevant terms
    Looking at specific taxa, such as yeast or plant
    Go offers script to bin more granular annotations up to higher levels
  • Comparing genomes with a GO slim
    • High-levelbiological process terms used to compare Plasmodium and Saccharomyces
    MJ Gardner, et al. (2002) Nature 419:498-511
  • GO slim: manual/orthology-based gene annotations
    Nucleic Acids Res. 2010 January; 38(Database issue): D420–D427.
  • GO toolswww.geneontology.org/GO.tools.shtml
    The real challenge is finding the right one for your needs
    For example, statistical representation of GO terms:
  • GO & analysis of RNA-seqdata
    Young et al. Genome Biology 2010, 11:R14 http://genomebiology.com/2010/11/2/R14
    We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.
  • When GO is limited
    Food for thought: what happens when we have limited GO (or other)annotation data?
    New and interesting genomes often see this problem
  • Comparative analysis of orthologs in syntenic blocks
    The more genomes we have at our disposal, the better
    Structural rearrangements, absence of intron, gene duplication, intron structure, gene deletion/creation
    Nucleic Acids Res. 2010 January; 38(Database issue): D420–D427.
  • Summary GO analyses
    GO remedies problems of synonyms & homonyms in biological nomenclature
    Queries based on IDs linked to precise definitions, not less reliable text-matching
    GO can help you to:
    Find all genes that share a particular function regardless of sequence
    Do comparisons across any species annotated with GO
    Summarize major classes of genes in a newly sequenced genome
    Characterize expressed genes is a study
    Drive hypotheses to test in the laboratory
    GO is not a panacea but it should be a valuable tool in your genomics toolbox
  • The title slide revisited…
    Arabidopsis thaliana ATPase
    HMA4 zinc binding domain
    GO:0006829 : zinc ion transport (BP)
    GO:0005886 : plasma membrane (CC)
    GO:0005515 : protein binding (MF)
    Thank you.