http://www.geneontology.org/index.shtml

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http//www.geneontology.org/index.shtml

• An Introduction to the
• Gene Ontology
• (GO)

The Gene Ontology project provides a controlled
vocabulary to describe gene and gene product
attributes in any organism.
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http//www.geneontology.org/index.shtml

• Search the Gene Ontology DatabaseSearch for
  genes, proteins or GO terms using AmiGOgene or
  protein name GO term or IDAmiGO is the official
  GO browser and search engine. Browse the Gene
  Ontology with AmiGO.

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• What does the Gene Ontology Consortium do?Terms
  in the Gene OntologySpecies-specific
  termsObsolete termsThe OntologiesCellular
  componentBiological processMolecular
  functionOntology structureWhat GO is
  NOTAnnotation and toolsDownloadsBeyond
  GOCross-productsMappings to other
  classification systemsContributing to GO

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What does the Gene Ontology Consortium do?

• Biologists currently waste a lot of time and
  effort in searching for all of the available
  information about each small area of research.
• This is hampered further by the wide variations
  in terminology that may be common usage at any
  given time, which inhibit effective searching by
  both computers and people.
• For example, if you were searching for new
  targets for antibiotics, you might want to find
  all the gene products that are involved in
  bacterial protein synthesis, and that have
  significantly different sequences or structures
  from those in humans. If one database describes
  these molecules as being involved in
  'translation', whereas another uses the phrase
  'protein synthesis', it will be difficult for you
  - and even harder for a computer - to find
  functionally equivalent terms.
• The Gene Ontology (GO) project is a collaborative
  effort to address the need for consistent
  descriptions of gene products in different
  databases.
• The project began as a collaboration between
  three model organism databases, FlyBase
  (Drosophila), the Saccharomyces Genome Database
  (SGD) and the Mouse Genome Database (MGD), in
  1998.
• Since then, the GO Consortium has grown to
  include many databases, including several of the
  world's major repositories for plant, animal and
  microbial genomes. See the GO Consortium page for
  a full list of member organizations.

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• The GO project has developed three structured
  controlled vocabularies (ontologies) that
  describe gene products in terms of their
  associated biological processes, cellular
  components and molecular functions in a
  species-independent manner.
• There are three separate aspects to this effort
• first, the development and maintenance of the
  ontologies themselves
• second, the annotation of gene products, which
  entails making associations between the
  ontologies and the genes and gene products in the
  collaborating databases
• and third, development of tools that facilitate
  the creation, maintenance and use of ontologies.
• The use of GO terms by collaborating databases
  facilitates uniform queries across them.
• The controlled vocabularies are structured so
  that they can be queried at different levels
• for example, you can use GO to find all the gene
  products in the mouse genome that are involved in
  signal transduction,
• or you can zoom in on all the receptor tyrosine
  kinases.
• This structure also allows annotators to assign
  properties to genes or gene products at different
  levels, depending on the depth of knowledge about
  that entity.

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Terms in the Gene Ontology

• The building blocks of the Gene Ontology are the
  terms, so what makes up a GO term?
• Each entry in GO has a unique numerical
  identifier of the form GOnnnnnnn, and a term
  name, e.g. cell, fibroblast growth factor
  receptor binding or signal transduction.
• Each term is also assigned to one of the three
  ontologies, molecular function, cellular
  component or biological process.
• The majority of terms have a textual definition,
  with references stating the source of the
  definition.
• If any clarification of the definition or remarks
  about term usage are required, these are held in
  a separate comments field.
• Many GO terms have synonyms GO uses 'synonym' in
  a loose sense, as the names within the synonyms
  field may not mean exactly the same as the term
  they are attached to. Instead, a GO synonym may
  be broader or narrower than the term string it
  may be a related phrase it may be alternative
  wording, spelling or use a different system of
  nomenclature or it may be a true synonym. This
  flexibility allows GO synonyms to serve as
  valuable search aids, as well as being useful for
  applications such as text mining and semantic
  matching. The relationship of the synonym to the
  term is recorded within the GO file.

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• The scope of the Gene Ontology overlaps with a
  number of other databases, and in cases where a
  GO term is identical in meaning to an object in
  another database, a database cross reference is
  added to the term. These cross references can
  also be downloaded from the mappings to GO page.
• Species-specific termsThe Gene Ontology aims to
  provide a controlled vocabulary that can be used
  to describe any organism nevertheless, many
  functions, processes and components are not
  common to all life forms. The convention is to
  include any term that can apply to more than one
  taxonomic class of organism. To specify the class
  of organisms to which a term is applicable, GO
  uses the designator sensu, 'in the sense of' for
  example, trichome differentiation (sensu
  Magnoliophyta) represents the differentiation of
  plant hair cells (trichomes).
• Obsolete termsOccasionally, a term is found that
  is outside the scope of GO, is misleadingly named
  or defined, or describes a concept that would be
  better represented in another way. Rather than
  delete the term, it is deprecated or made
  obsolete. The term and ID still exist in the GO
  database, but the term is marked as obsolete, and
  a comment added, giving a reason for the
  obsoletion and recommending alternative terms
  where appropriate.

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The Ontologies

• The three organizing principles of GO are
  cellular component, biological process and
  molecular function. A gene product might be
  associated with or located in one or more
  cellular components it is active in one or more
  biological processes, during which it performs
  one or more molecular functions. For example, the
  gene product cytochrome c can be described by the
  molecular function term oxidoreductase activity,
  the biological process terms oxidative
  phosphorylation and induction of cell death, and
  the cellular component terms mitochondrial matrix
  and mitochondrial inner membrane.

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Cellular component

• A cellular component is just that, a component of
  a cell, but with the proviso that it is part of
  some larger object this may be an anatomical
  structure (e.g. rough endoplasmic reticulum or
  nucleus) or a gene product group (e.g. ribosome,
  proteasome or a protein dimer). See the
  documentation on the cellular component ontology
  for more details.

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Biological process

• A biological process is series of events
  accomplished by one or more ordered assemblies of
  molecular functions. Examples of broad biological
  process terms are cellular physiological process
  or signal transduction.
• Examples of more specific terms are pyrimidine
  metabolism or alpha-glucoside transport.
• It can be difficult to distinguish between a
  biological process and a molecular function, but
  the general rule is that a process must have more
  than one distinct steps.A biological process is
  not equivalent to a pathway at present, GO does
  not try to represent the dynamics or dependencies
  that would be required to fully describe a
  pathway.Further information can be found in the
  process ontology documentation.

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Molecular function

• Molecular function describes activities, such as
  catalytic or binding activities, that occur at
  the molecular level.
• GO molecular function terms represent activities
  rather than the entities (molecules or complexes)
  that perform the actions, and do not specify
  where or when, or in what context, the action
  takes place.
• Molecular functions generally correspond to
  activities that can be performed by individual
  gene products, but some activities are performed
  by assembled complexes of gene products.
• Examples of broad functional terms are catalytic
  activity, transporter activity, or binding
• examples of narrower functional terms are
  adenylate cyclase activity or Toll receptor
  binding.
• It is easy to confuse a gene product name with
  its molecular function, and for that reason many
  GO molecular functions are appended with the word
  "activity". The documentation on gene products
  explains this confusion in more depth. The
  documentation on the function ontology explains
  more about GO functions and the rules governing
  them.

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Ontology structure

• The terms in an ontology are linked by two
  relationships, is_a and part_of. is_a is a simple
  class-subclass relationship,
• where A is_a B means that A is a subclass of B
  for example, nuclear chromosome is_a chromosome.
• part_of is slightly more complex C part_of D
  means that whenever C is present, it is always a
  part of D, but C does not always have to be
  present. An example would be nucleus part_of
  cell nuclei are always part of a cell, but not
  all cells have nuclei.
• The ontologies are structured as directed acyclic
  graphs,
• which are similar to hierarchies but differ in
  that
• a child, or more specialized, term can have many
  parents, or less specialized, terms.
• For example, the biological process term hexose
  biosynthesis has two parents, hexose metabolism
  and monosaccharide biosynthesis. This is because
  biosynthesis is a subtype of metabolism, and a
  hexose is a type of monosaccharide. When any gene
  involved in hexose biosynthesis is annotated to
  this term, it is automatically annotated to both
  hexose metabolism and monosaccharide
  biosynthesis,
• because every GO term must obey the true path
  rule if the child term describes the gene
  product, then all its parent terms must also
  apply to that gene product.

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What GO is NOT

• It is important to clearly state the scope of GO,
  and what it does and does not cover. The
  ontologies section explains the domains covered
  by GO the following areas are outside the scope
  of GO, and terms in these domains would not
  appear in the ontologies.Gene products e.g.
  cytochrome c is not in the ontologies, but
  attributes of cytochrome c, such as
  oxidoreductase activity, are.Processes,
  functions or components that are unique to
  mutants or diseases e.g. oncogenesis is not a
  valid GO term because causing cancer is not the
  normal function of any gene.Attributes of
  sequence such as intron/exon parameters these
  are not attributes of gene products and will be
  described in a separate sequence ontology (see
  the OBO website for more information).Protein
  domains or structural features.Protein-protein
  interactions.Environment, evolution and
  expression.Anatomical or histological features
  above the level of cellular components, including
  cell types.GO is not a database of gene
  sequences, nor a catalog of gene products.
  Rather, GO describes how gene products behave in
  a cellular context.GO is not a dictated standard,
  mandating nomenclature across databases. Groups
  participate because of self-interest, and
  cooperate to arrive at a consensus.GO is not a
  way to unify biological databases (i.e. GO is not
  a 'federated solution'). Sharing vocabulary is a
  step towards unification, but is not, in itself,
  sufficient. Reasons for this include the
  followingKnowledge changes and updates lag
  behind.Individual curators evaluate data
  differently. While we can agree to use the word
  'kinase', we must also agree to support this by
  stating how and why we use 'kinase', and
  consistently apply it. Only in this way can we
  hope to compare gene products and determine
  whether they are related.GO does not attempt to
  describe every aspect of biology its scope is
  limited to the domains described above.Back to

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topAnnotation and tools

• How do the terms in GO become associated with
  their appropriate gene products? Collaborating
  databases annotate their genes or gene products
  with GO terms, providing references and
  indicating what kind of evidence is available to
  support the annotations. More information can be
  found in the GO Annotation Guide.If you browse
  any of the contributing databases, you'll find
  that each gene or gene product has a list of
  associated GO terms. Each database also publishes
  downloadable files containing these associations
  these can be downloaded from the GO annotations
  page. You can browse the ontologies using a range
  of web-based browsers. A full list of these, and
  other tools for analyzing gene function using GO,
  is available on the GO Tools section.In addition,
  the GO consortium has prepared GO slims, 'slimmed
  down' versions of the ontologies that allow you
  to annotate genomes or sets of gene products to
  gain a high-level view of gene functions. Using
  GO slims you can, for example, work out what
  proportion of a genome is involved in signal
  transduction, biosynthesis or reproduction. See
  the GO Slim Guide for more information.

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Beyond GO

• GO allows us to annotate genes and their products
  with a limited set of attributes. For example, GO
  does not allow us to describe genes in terms of
  which cells or tissues they're expressed in,
  which developmental stages they're expressed at,
  or their involvement in disease. It is not
  necessary for GO to do these things because other
  ontologies are being developed for these
  purposes. The GO consortium supports the
  development of other ontologies and makes its
  tools for editing and curating ontologies freely
  available. A list of freely available ontologies
  that are relevant to genomics and proteomics and
  are structured similarly to GO can be found at
  the Open Biomedical Ontologies website . A larger
  list, which includes the ontologies listed at OBO
  and also other controlled vocabularies that do
  not fulfill the OBO criteria is available at the
  Ontology Working Group section of the Microarray
  Gene Expression Data (MGED) Network site .

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Download

• All data from the GO project is freely available.
  You can download the ontology data in a number of
  different formats, including XML and mySQL, from
  the GO Downloads page. For more information on
  the syntax of these formats, see the GO File
  Format Guide.If you need lists of the genes or
  gene products that have been associated with a
  particular GO term, the Current Annotations table
  tracks the number of annotations and provides
  links to the gene association files for each of
  the collaborating databases is available.

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GO term enrichment

• Hypergeometric
• SGD example using LA output

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Transitive functional annotation by shortest-path
analysis of gene expression dataPNAS October
1, 2002 vol. 99 no. 20 12783-12788Xianghong
Zhou, Ming-Chih J. Kao, and Wing Hung Wong

• Fig. 1.   (A) Application of the shortest-path
  (SP) algorithm to gene expression data. Nine
  genes are depicted in the graph. The distance
  between two genes is a decreasing function of
  their correlation. For example, there are
  multiple expression dependence paths leading from
  gene a to gene e. Among them, the shortest
  dependence path is a-b-c-d-e, with genes b, c,
  and d serving as the transitive genes. This is
  the most parsimonious summary of the expression
  relationship between the terminal genes a and e.
  (B) Level 0 (L0) and level 1 (L1) matches of
  genes on the SP a-b-c-d-e defined according to
  their relationships in the Gene Ontology (GO)
  classification tree. With respect to the terminal
  genes a and e, the transitive gene b is a L0
  match because it is annotated in the informative
  node where a and e are annotated the transitive
  gene c is a L1 match because it shares the same
  direct parent as the two terminal genes the
  transitive gene d is neither a L0 nor a L1 match.

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Current methods for the functional analysis of
microarray gene expression data make the implicit
assumption that genes with similar expression
profiles have similar functions in cells.
However, among genes involved in the same
biological pathway, not all gene pairs show high
expression similarity. Here, we propose that
transitive expression similarity among genes can
be used as an important attribute to link genes
of the same biological pathway. Based on
large-scale yeast microarray expression data, we
use the shortest-path analysis to identify
transitive genes between two given genes from the
same biological process. We find that not only
functionally related genes with correlated
expression profiles are identified but also those
without. In the latter case, we compare our
method to hierarchical clustering, and show that
our method can reveal functional relationships
among genes in a more precise manner. Finally, we
show that our method can be used to reliably
predict the function of unknown genes from known
genes lying on the same shortest path. We
assigned functions for 146 yeast genes that are
considered as unknown by the Saccharomyces Genome
Database and by the Yeast Proteome Database.
These genes constitute around 5 of the unknown
yeast ORFome.
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Data Processing

• Saccharomyces cerevisiae gene expression profiles
  from the Rosetta Compendium (6), which includes
  300 deletion and drug treatment experiments.
  Genes were annotated by using the biological
  process ontology of Gene Ontology (GO) (7)
  provided by the Saccharomyces Genome Database
  (SGD) (8).
• After removing the genes without GO process
  annotation and the 20 genes for which there are
  less than 80 experimental measurements in the
  Rosetta Compendium, we were left with
  266 mitochondrial, 398 cytoplasmic, and
  659 nuclear GO-annotated genes.
• For each of the three sets of genes, we
  calculated the expression similarities of all
  gene pairs a, b using Ca,b, the minimum of the
  absolute value of leave-one-out Pearson
  correlation coefficient estimates. This estimate
  is a measurement robust against single experiment
  outliers and sensitive to overall similarities in
  expression patterns.

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Graph Construction and SP Computation.

• We constructed three graphs, one for each set of
  the 266 mitochondrial genes, the 398 cytoplasmic
  genes, and the 659 nuclear genes. In each graph,
  two genes were assigned an edge if their absolute
  expression correlation Ca,b was higher than
    0.6. 
• This cut-off, while conservative, nonetheless
  retains a sufficient number of connected gene
  pairs in the graph. The edge length between
  vertices a and b is da,b  f(Ca,b)  (1  Ca,b)k.
  The powering factor k is used to enhance the
  differences between low and high correlations.
  Because the length of a path is the sum of the
  individual edge lengths, by exaggerating the
  differences between edge lengths, the SPs will be
  more likely to cover more transitive genes. Thus
  by increasing k we gain more power to reveal
  transitive co-expression. We set k  6 because
  for k  6, the numbers of transitive genes
  stabilizes (detailed results at
  www.biostat.harvard.edu/complab/SP/). To ensure
  the quality of SPs, we consider only SPs with
  total path lengths lt0.008.

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Predicting the Functions of Unknown Genes.

• We use the SP method to classify previously
  unannotated yeast genes by adding the 3,255 ORFs
  unknown to SGD into the graphs of known genes in
  the mitochondrial, cytoplasmic, and nuclear
  compartments.
• As before, an edge is constructed between two
  genes if their absolute expression correlation is
  higher than 0.6. 
• For all pairs of known genes, we determine the
  SPs connecting them. For the purpose of
  functional prediction, we would like to assign a
  putative function that is as specific as possible
  to the gene. Given all known genes on a SP, we
  achieve this by tracing back their annotations
  along the GO process tree and finding their
  lowest common ancestor.
• If the lowest ancestral node is at least 4 levels
  below the root of the GO tree, that is, it
  defines a sufficiently specific gene function, we
  then assign this function to the unknown genes on
  the SP.
• Analogous to the L0 and L1 matches, here the L0
  prediction then corresponds to the lowest common
  ancestor, and the L1 prediction to its direct
  parent. In this way, the function represented by
  the lowest common ancestor can be more specific
  than that defined by the informative nodes.
• . For each predicted gene function, we provide
  both the number of support SPs from which the
  prediction was derived and the number of unique
  known genes on those support SPs (support genes).
  The more support genes there are, the more
  confidence we have in the corresponding
  prediction.
• Note that a gene can be assigned putative
  functions in multiple graphs, because many genes
  are known to function in multiple cellular
  compartments.
• Under two circumstances an unknown gene may be
  assigned with multiple functions (i) Because
  known genes on a SP may each have multiple
  functions, they may share several lowest common
  ancestors in the GO tree. (ii) An unknown gene
  may reside in different SPs with different lowest
  common ancestors