!version: $Revision: 1.42 $
!date: $Date: 2010/01/28 19:05:20 $
!
! Gene Ontology Reference Collection
!
! The GO reference collection is a set of abstracts that can be cited
! in the GO ontologies (e.g. as dbxrefs for term definitions) and
! annotation files (in the Reference column).
!
! The collection houses two main kinds of references; one type are 
! descriptions of methods that groups use for ISS, IEA, and ND
! evidence codes; the other type are abstract-style descriptions of
! "GO content" meetings at which substantial changes in the ontologies
! are discussed and made.
!
! data fields for this file:
!
!  go_ref_id: [mandatory; cardinality 1; GO_REF:nnnnnnn]
!  alt_id: [not mandatory; cardinality 0,1,>1; GO_REF:nnnnnnn]
!  title: [mandatory; cardinality 1; free text]
!  authors: [mandatory; cardinality 1; free text??
!            or cardinality 1,>1 and one entry per author?]
!  year: [mandatory, cardinality 1]
!  external_accession: [not mandatory; cardinality 0,1,>1; DB:id]
!  citation: [not mandatory; cardinality 0,1; use for published refs]
!  abstract: [mandatory; cardinality 1; free text]
!  comment: [not mandatory; cardinality 1; free text]
!  is_obsolete: [not mandatory; cardinality 0,1; 'true';
!            if tag is not present, assume that the ref is not obsolete
!            denotes a reference no longer used by the contributing database]
!
!  If a database maintains its own internal reference collection, and
!  has a record that is equivalent to a GO_REF entry, the database's
!  internal ID should be included as an external_accession for the
!  corresponding GO_REF.
!
!This data is available as a web page at
!http://www.geneontology.org/cgi-bin/references.cgi
!

go_ref_id: GO_REF:0000001
title: GO Consortium unpublished data
authors: GO curators
year: 1998
abstract: No abstract available.
comment: This reference will normally be replaced upon publication of the data supporting the annotation. Formerly GOC:unpublished.

go_ref_id: GO_REF:0000002
alt_id: GO_REF:0000007
alt_id: GO_REF:0000014
alt_id: GO_REF:0000016
alt_id: GO_REF:0000017
title: Gene Ontology annotation through association of InterPro records with GO terms.
authors: DDB, FB, MGI, GOA, ZFIN curators
year: 2001
external_accession: MGI:2152098
external_accession: J:72247
external_accession: ZFIN:ZDB-PUB-020724-1
external_accession: FB:FBrf0174215
external_accession: dictyBase_REF:10157
external_accession: SGD_REF:S000124036
abstract: Transitive assignment of GO terms based on InterPro classification. For any database entry (representing a protein or protein-coding gene) that has been annotated with one or more InterPro domains, The corresponding GO terms are obtained from a translation table of InterPro entries to GO terms (interpro2go) generated manually by the InterPro team at EBI. The mapping file is available at http://www.geneontology.org/external2go/interpro2go.
comment: Formerly GOA:interpro. Note that GO annotations based on InterPro-to-GO transitive assignment may undergo subsequent filtering, e.g. to remove annotations redundant with manual curation; consult documentation from the annotation providers for further information.

go_ref_id: GO_REF:0000003
alt_id: GO_REF:0000005
title: Gene Ontology annotation based on Enzyme Commission mapping.
authors: GOA curators, MGI curators
year: 2001
external_accession: MGI:2152096
external_accession: J:72245
external_accession: ZFIN:ZDB-PUB-031118-3
external_accession: SGD_REF:S000124037
citation: PMID:11374909
abstract: Transitive assignment using Enzyme Commission identifiers. This method is used for any database entry, such as a protein record in Swiss-Prot or TrEMBL, that has had an Enzyme Commission number assigned. The corresponding GO term is determined using the EC cross-references in the GO molecular function ontology. Also see Hill et al., Genomics (2001) 74:121-128. The mapping file is available at http://www.geneontology.org/external2go/ec2go.
comment: Formerly GOA:spec.

go_ref_id: GO_REF:0000004
alt_id: GO_REF:0000009
alt_id: GO_REF:0000013
title: Gene Ontology annotation based on Swiss-Prot keyword mapping.
authors: GOA curators
year: 2000
external_accession: MGI:1354194
external_accession: J:60000
external_accession: ZFIN:ZDB-PUB-020723-1
external_accession: SGD_REF:S000124038
abstract: Transitive assignment using Swiss-Prot keywords. This method is used for any database record that has one or more Swiss-Prot keywords assigned. Each keyword is mapped to the corresponding GO term in the spkw2go file, which was originally constructed manually by MGI curators and is now maintained by the GOA team at EBI. The mapping file is available at http://www.geneontology.org/external2go/spkw2go.
comment: Formerly GOA:spkw.

go_ref_id: GO_REF:0000006
title: Gene Ontology annotation by the MGI curatorial staff, Mouse Locus Catalog
authors: Mouse Genome Informatics scientific curators
year: 2001
external_accession: MGI:2152097
external_accession: J:72246
citation: PMID:11374909
abstract: For annotations documented via this citation, curators used the information in the Mouse Locus Catalog in MGI to assign GO terms. The GO terms were assigned based on MLC textual descriptions of genes that could not be traced to the primary literature. Details of this strategy can be found in Hill et al, Genomics (2001) 74:121-128.
is_obsolete: true

go_ref_id: GO_REF:0000008
title: Gene Ontology annotation by the MGI curatorial staff, curated orthology
authors: Mouse Genome Informatics scientific curators
year: 2001
external_accession: MGI:2154458
external_accession: J:73065
abstract: The sequence conservation that permits the establishment of orthology between mouse and rat or mouse and human genes is a strong predictor of the conservation of function for the gene product across these species. Therefore, in instances where a mouse gene product has not been functionally characterized, but its human or rat orthologs have, Mouse Genome Informatics (MGI) curators append the GO terms associated with the orthologous gene(s) to the mouse gene. Only those GO terms assigned by experimental determination to the ortholog of the mouse gene will be adopted by MGI. GO terms that are assigned to the ortholog of the mouse gene computationally (i.e. IEA), will not be transferred to the mouse ortholog. The evidence code represented by this citation is Inferred by Sequence Similarity (ISS.)

go_ref_id: GO_REF:0000010
title: Gene Ontology annotation by the MGI curatorial staff, mouse gene nomenclature
authors: Mouse Genome Informatics scientific curators
year: 1999
external_accession: MGI:1347124
external_accession: J:56000
citation: PMID:11374909
abstract: For annotations documented via this citation, curators designed queries based on their knowledge of mouse gene nomenclature to group genes that shared common molecular functions, biological processes or cellular components. GO annotations were assigned to these genes in groups. Details of this strategy can be found in Hill et al., Genomics (2001) 74:121-128.

go_ref_id: GO_REF:0000011
title: Hidden Markov Models (TIGR)
authors: Michelle Gwinn, TIGR curators
year: 2003
abstract: 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_ref_id: GO_REF:0000012
title: Pairwise alignment (TIGR)
authors: Michelle Gwinn, TIGR curators
year: 2003
abstract: Pairwise alignments are generated by taking two sequences and aligning them so that the maximum number of amino acids in each protein match, or are similar to, each other. Tools such as BLAST work by comparing a protein-of-interest individually with every protein in a database of known protein sequences and retaining only those matches with a high probability of being significant. Basic BLAST generates local alignments between proteins for regions of high similarity. Other pairwise alignment tools attempt to generate global (full-length) protein alignments. A tool called Blast_Extend_repraze (BER, http://ber.sourceforge.net) has some benefits over basic BLAST. Input into the BER tool includes the underlying DNA sequence for each protein as well as 300 nucleotides upstream and downstream of the predicted boundaries of the protein coding sequence. This allows annotators to see the DNA sequence that underlies the query protein as part of the alignment. In addition, the BER tool is able to look for continuation of regions of similarity through frameshifts and in-frame stop codons.  If such regions are found the alignment is continued. BER searches are done in a two-step process: step one is a BLAST search against a non-redundant protein database, significant BLAST hits are stored in a mini-database for each query protein; step two is a modified Smith-Waterman alignment between the query and the proteins in its mini-database. In order to assess whether a given BER alignment is good enough to assert that the query shares the function of the match protein, one must look at a several factors. First of all, the match protein must itself be experimentally characterized in order to avoid transitive annotation errors. In addition, any residues or secondary structures known to be important for function in the match protein must be conserved in the query. The alignment should be visually inspected to look for any areas of lesser quality that might indicate the two proteins do not share the same function. Although it is impossible to set cutoff values for percent identity and length of match that will apply for every alignment, there are some guidelines. In general at least 40% identity that extends over the full lengths of both proteins is required in order to even consider functional equivalence. However, this percentage is highly dependent on the length and complexity of the proteins. 40% identity between two proteins 500 amino acids long is much more significant that 40% identity between two proteins that are only 100 amino acids long. Therefore, the annotator's experience and knowledge of what is considered significant for the organism and protein family in question is very important. Some sets of proteins are much more highly conserved than others and therefore tolerances for percent identity may have to be adjusted. Finally, the alignment must be considered in the context of what else is known about the query protein and the organism as a whole.

go_ref_id: GO_REF:0000015
title: Use of the ND evidence code for Gene Ontology (GO) terms.
authors: GO Curators
year: 2002
external_accession: FB:FBrf0159398
external_accession: ZFIN:ZDB-PUB-031118-1
external_accession: dictyBase_REF:9851
external_accession: MGI:MGI:2156816
abstract: The Gene Ontology (GO) Consortium created the evidence code "ND" to indicate "no biological data available". This code is used for annotations to any of the three terms 'molecular function unknown ; GO:0005554', 'biological process unknown ; GO:0000004' or 'cellular component unknown ; GO:0008372'. In GO member databases, the use of any of these three GO terms, attributed to this reference and supported by the ND evidence code, signifies that a curator has examined the available literature and sequence for this gene and that as of the date of the annotation to the unknown term, there is no information supporting an annotation to any GO term in that ontology. (Note that ND can be used with any one (or two) of the 'unknown' terms, even if there is data available to support annotation to a term from one or both of the other ontologies; e.g., ND can be used with GO:0008372 if the function and process are known but component is not).
comment: From FlyBase.

go_ref_id: GO_REF:0000018
title: dictyBase 'Inferred from Electronic Annotation (BLAST method)' 
authors: DictyBase curators
year: 2005
external_accession: dictyBase_REF:10158
abstract: Gene Ontology (GO) annotations with the evidence code 'Inferred from Electronic Annotation' (IEA) are assigned automatically to gene products in dictyBase. All Dictyostelium protein sequences are analyzed by BLAST against GO gene association sequence files, identifying proteins in other organisms that align with Dictyostelium proteins with an E value less than or equal to e-50. GO annotations that have been manually assigned to these proteins from other species are then imported and attached to the corresponding gene product in dictyBase. The proteins from which the annotations are derived are displayed in the 'Evidence' column on the Gene Ontology evidence and references page.

go_ref_id: GO_REF:0000019
title: Automatic transfer of experimentally verified manual GO annotation data to orthologs using Ensembl Compara
authors: Ensembl curators, GOA curators
year: 2006
abstract: GO terms from a source species are projected onto one or more target species based on gene orthology obtained from the Ensembl Compara system. Only one to one and apparent one to one orthologies are used, and only GO annotations with an evidence type of IDA, IEP, IGI, IMP or IPI are projected. Projected GO annotations using this technique will receive the evidence code, inferred from electronic anotation, 'IEA'. The UniProtKB protein accession of the annotation source will be indicated in the 'With' column of the GOA association file.

go_ref_id: GO_REF:0000020
title: Electronic Gene Ontology annotations created by transferring manual GO annotations between orthologous microbial proteins
authors: Swiss Institute of Bioinformatics (SIB) curators, GOA curators
year: 2006
abstract: GO terms are manually assigned to each HAMAP family rule. High-quality Automated and Manual Annotation of microbial Proteins (HAMAP) family rules are a collection of orthologous microbial protein families, from bacteria, archaea and plastids, generated manually by expert curators. The assigned GO terms are then transferred to all the proteins that belong to each HAMAP family. Only GO terms from the molecular function and biological process ontologies are assigned. GO annotations using this technique will receive the evidence code Inferred from Electronic Annotation (IEA). These annotations are updated monthly by HAMAP and are available for download on both GO and GOA EBI ftp sites. To report an annotation error or inconsistency, or for further information, please contact the GO Consortium at gohelp@genome.stanford.edu or submit a comment the SourceForge Annotation Issues tracker (http://sourceforge.net/projects/geneontology/). HAMAP is a project based at the Swiss Institute of Bioinformatics (Gattiker et al. 2003, Comp. Biol and Chem. 27: 49-58). For further information, please see http://www.expasy.org/sprot/hamap/.

go_ref_id: GO_REF:0000021
title: Improving the representation of central nervous system development in the biological process ontology
authors: Judith Blake (1, 2), William Bug (3), Rex Chisholm (1, 4), Jennifer Clark (1, 5), Erika Feltrin (6), Jacqueline Finger (2), David Hill (1, 2), Midori Harris (1, 5), Terry Hayamizu (2), Doug Howe (9), Maryanne Martone (7), Kathleen Millen (8), Francis Sele (4) (1. The Gene Ontology Consortium, 2. Mouse Genome Informatics, Bar Harbor, ME, 3. Drexel University, Philadelphia, PA, 4. Northwestern University, Chicago, IL, 5. EMBL-EBI, Hinxton, Cambridgeshire, UK, 6. The University of Padua, Padua, Italy, 7. The University of California at San Diego, San Diego, CA, 8. The University of Chicago, Chicago, IL, 9. The Zebrafish Information Network, University of Oregon, Eugene, OR)
year: 2006
abstract: Current genetic and molecular studies in many model organisms are aimed at understanding formation and development of the nervous system. Up until this point, the GO has had a very shallow representation of processes pertaining to the nervous system. In June 2006, curators from MGI and ZFIN met with researchers studying central nervous system development to improve the representation of these processes in GO. In particular, emphasis was placed on three areas that are being addressed actively in current research: forebrain development, hindbrain development and neural tube development. This collaboration resulted in the addition of over 500 terms that reflect the development of the forebrain, the hindbrain, and the neural tube from the perspective of biological process and anatomical structure.

go_ref_id: GO_REF:0000022
title: Improving the representation of immunology in the biological process Ontology
authors: Alison Deckhut Augustine (1), Alan Collmer (2), Judith A. Blake (3, 4), Candace W. Collmer (2, 3), Shane C. Burgess (5), Lindsay Grey Cowell (6), Jennifer I. Clark (3, 7), Bernard de Bono (7), Russell T. Collins (8), Alexander D. Diehl (3, 4), Michelle Gwinn Giglio (3, 9), Jamie A. Lee (10), Linda Hannick (3, 9), Jane Lomax (3, 7), Midori A. Harris (3, 7), Christopher J. Mungall (3, 11), David P. Hill (3, 4), Richard H. Scheuermann (10), Amelia Ireland (3, 7), Alessandro Sette (12) (1. NIAID, 2. Cornell University, 3. The GO Consortium, 4. Mouse Genome Informatics, 5. Mississippi State University, 6. Duke University, 7. EMBL-EBI, 8. University of Cambridge, 9. The Institute for Genomic Research, 10. U.T. Southwestern Medical Center, 11. HHMI, 12. La Jolla Institute for Allergy and Immunology)
year: 2005
abstract: GO terms describing processes, functions, and cellular components related to the immune system have existed in the GO from its beginning and been used extensively in the annotation of gene products. However, particularly in the biological process ontology, the initial set of terms relating to immunology failed to cover the breadth of known immunological processes, and in many cases diverged from current usage and understanding in their names, definitions, and ontological placement. As part of a larger effort to improve the representation of immunology in the GO, a GO Content Meeting was held November 15-16, 2005, at The Institute for Genomic Research, to discuss improvements to representation of immunology in the biological process ontology of the GO. As a result of the meeting, a number of high level terms for immunological processes were created, an overall structure for immunologically related terms was established, and certain existing terms were renamed or redefined as well to bring them in line with current usage.

go_ref_id: GO_REF:0000023
title: Gene Ontology annotation based on Swiss-Prot Subcellular Location vocabulary mapping.
authors: GOA curators, UniProt curators
year: 2007
external_accession: SGD_REF:S000125578
abstract: Transitive assignment of GO terms based on Swiss-Prot Subcellular Location vocabulary annotation. The UniProt Consortium has developed a Subcellular Location vocabulary (SPSL) to annotate UniProt Knowledgebase entries (in CC_SUBC LOCATION lines). The GOA curators at EBI have manually mapped this vocabulary to the GO cellular component ontology. This mapping file, spsl2go, is used to obtain corresponding GO terms for any UniPRotKB entry that has SPSL annotation; the mapping file is available is available from ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/external2go/spsl2go or http://www.geneontology.org/external2go/spsl2go.

go_ref_id: GO_REF:0000024
title: Manual transfer of experimentally-verified manual GO annotation data to orthologs by curator judgment of sequence similarity.
authors: GOA, HGNC, AgBase and UniProtKB curators
year: 2007
abstract: Method for transferring manual annotations to an entry based on a curator's judgment of its similarity to a putative ortholog which has annotations with experimental evidence. Annotations are created when a curator judges that the sequence of a protein shows high similarity to another protein that has annotation(s) supported by experimental evidence (IDA, IGI, IMP, IPI or IEP). Annotations resulting from the transfer of GO terms display the 'ISS' evidence code and include an accession for the protein from which the annotation was projected in the 'with' field (column 8). This field can contain either a UniProtKB Accession or an IPI (International Protein Index) identifier. Only annotations with an experimental evidence code and which do not have the 'NOT' qualifier are transferred. Orthologs/homologs are chosen following a protein MPsrch (http://www.ebi.ac.uk/MPsrch) or BLAST, where the aligned sequences show a high degree of similarity over their entire lengths, making it reasonable to infer that the two proteins have a common function. It must be emphasized that curators must check each alignment and use their experience to assess whether similarity is considered to be strong enough to project annotations. While there is no fixed cut-off point in percentage sequence similarity, generally proteins which have greater than 60% identity that covers greater than 80% of the length of both proteins are examined further. For mammalian proteins this cut-off tends to be higher, with an average of 80% identity over 90% of the length of both proteins. Additional tools, such as the HCOP orthology tool (http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/hcop.pl), are used when possible. Strict orthologs are desirable but not essential. When there is evidence of paralogs, annotations are transferred only to the most similar protein in each species. Further detailed information on this procedure, including how ISS annotations are made to protein isoforms, can be found at: http://www.ebi.ac.uk/GOA/ISS_method.html.

go_ref_id: GO_REF:0000025
title: Operon structure as IGC evidence
authors: Michelle Gwinn, TIGR curators
year: 2007
abstract: Genes in prokaryotic organisms are often arranged in operons. Genes in an operon are all transcribed into one mRNA. Generally the genes in the operons code for proteins that all have related functions. For example, they may be the steps in a biochemical pathway, or they may be the subunits of a protein complex. Often the genes in operons shared between organisms are syntenic; that is, the same genes are in the same order in the operon in different species. When assessing sequence-comparison-based evidence during the process of manual annotation of a genome, it is often the case that some of the genes in the operon will have strong sequence-based evidence while others will have weak evidence. If seen alone, not in the presence of an operon, the weak evidence in question may not be sufficient to make a functional annotation. However, in the presence of an operon in which there is strong evidence for some of the genes, the very presence of the gene in the operon is a strong indication that the gene shares in the process carried out by the operon. If the putative function is one expected to exist for the process in question and particularly if that function has been observed in the same operon in another species, then the annotation should be made. This type of evidence is inferred from the context of the gene in an operon, and therefore the evidence code is IGC "inferred from genomic context."

go_ref_id: GO_REF:0000026
title: Improving the representation of muscle biology in the biological process and cellular component ontologies.
authors: Jennifer Deegan nee Clark (1, 5), Alexander D. Diehl (1,7), Elisabeth Ehler (2), Georgine Faulkner (3), Erika Feltrin (4), Jennifer Fordham (2), Midori Harris (1, 5), Ralph Knoell (6) David Hill (1, 7), Paolo Laveder (8), Alessandra Nori (8), Carlo Reggiani (8), Vincenzo Sorrentino (9), Giorgio Valle (4), Pompeo Volpe (8) (1. The Gene Ontology Consortium, 2. King's College, London, UK, 3. ICGEB, Trieste, Italy, 4. CRIBI - University of Padua, Padua, Italy 5. EMBL-EBI, Hinxton, Cambridgeshire, UK, 6. University of Goettingen, Goettingen, Germany 7. Mouse Genome Informatics, Bar Harbor, ME, 8. University of Padua, Padua, Italy, 9. University of Siena, Siena, Italy)
year: 2007
abstract: A meeting focused on the biology of skeletal and smooth muscle has been held on 24-25 July 2007 at the University of Padua, Italy, as a collaboration with the GO consortium and CRIBI Biotechnology Center. The aims of this effort were to provide a comprehensive representation of muscle biology in the biological process and cellular component ontologies and to improve the organization of muscle-specific terms to better describe the current knowledge of biological mechanisms in muscle tissue. Thus, the collaboration brought together experts in several areas of muscle biology and physiology who carried out a thorough review of the existing GO muscle terms as these terms were largely created by non-muscle experts using older definitions. In particular, several areas are being addressed actively in current research: the biological processes of muscle contraction, muscle plasticity, muscle development, and muscle regeneration; and the sarcoplasmic reticulum and membrane delimited compartments. This work resulted in the addition of 159 new terms and in the modification of 57 terms to bring them in line with current usage. Funding for the meeting was provided by Italian Telethon Foundation.

go_ref_id: GO_REF:0000027
title: BLAST search criteria for ISS assignment in PAMGO_GAT
authors: PAMGO_GAT curators
year: 2007
abstract: This GO reference describes the criteria used in assigning the evidence code of ISS via BLAST searches to annotate gene products from PAMGO_GAT. Standard BLASTP from NCBI was used (http://www.ncbi.nih.gov/blast) to query the non-redundant (NR) database. Hits are considered to be significant if the E-value is at or less than 10^-4. All other parameters are default according to http://www.ncbi.nih.gov/blast.

go_ref_id: GO_REF:0000028
title: Criteria for IDA, IEP, ISS, IGC, RCA, ND, and IEA assignment in PAMGO_MGG
authors: PAMGO_MGG curators
year: 2008
abstract: This GO reference describes the criteria used in assigning the evidence codes of IDA, IEP, ISS, IGC, RCA, ND and IEA to annotate gene products from PAMGO_MGG. Standard BLASTP from NCBI was used (http://www.ncbi.nih.gov/blast) to iteratively search reciprocal best hits and thus identify orthologs between predicted proteins of Magnaporthe grisea and GO proteins from multiple organisms with published association to GO terms (http://www.geneontology.org/GO.downloads.database.shtml). The alignments were manually reviewed for those hits with e-value equal to zero and with 80% or better coverage of both query and subject sequences, and for those hits with e<=10^-20, pid >=35 and sequence coverage >=80%. Furthermore, experimental or reviewed data from literature and other sources were incorporated into the GO annotation. IDA was assigned to an annotation if normal function of its gene was determined through transfections into a cell line and overexpression. IEP was assigned to an annotation if according to microarray experiments, its gene was upregulated in a biological process and the fold change was equal to or bigger than 10, or if according to Massively Parallel Signature Sequencing (MPSS), its gene was upregulated only in a certain biological process and the fold change was equal to or bigger than 10. ISS was assigned to an annotation if the entry at the With_column was experimentally characterized and the pairwise alignments were manually reviewed. IGC was assigned to an annotation if it based on comparison and analysis of gene location and structure, clustering of genes, and phylogenetic reconstruction of these genes. RCA was assigned to an annotation if it based on integrated computational analysis of whole genome microarray data, and matches to InterPro, pfam, and COG etc. When no knowledge (experimental/computational) was available about a gene product in any one of the GO aspects, the gene product was annotated to the root term (GO:0005575 for Cellular Component, GO:0003674 for Molecular Function, and GO:0008150 for Biological Process), and was assigned an ND evidence code. IEA was assigned to an annotation if its function assignment based on computational work, and no manual review was done.

go_ref_id: GO_REF:0000029
title: Gene Ontology annotation based on information extracted from curated UniProtKB entries
authors: GOA-UniProt curators
year: 2001-2007
abstract: Method by which GO terms were manually assigned to UniProt KnowledgeBase accessions, using either a NAS or TAS evidence code, by applying information extracted from the corresponding publicly-available, manually curated UniProtKB entry. Such GO annotations were submitted by the GOA-UniProt group from 2001, but this annotation practice was discontinued in 2007.

go_ref_id: GO_REF:0000030
title: Portable Annotation Rules
authors: Daniel Haft, JCVI
year: 2008
abstract: The JCVI is developing a collection of mixed-evidence annotation rules, under the working name BrainGrab/RuleBase (BGRB). A rule has two parts. The first is the set of conditions that must be met for the rule to fire. The second is the set actions to be taken for rules that have fired. BGRB rules are designed to serve as proxies for the annotators that create them. They have very high fidelity but may have low coverage. Types of evidence used in combination include HMM hits and BLAST matches, hits to neighboring genes, pathway reconstruction reports from the Genome Properties system, and species taxonomy. BLAST matches are described by a number of separate parameters for raw score, percent sequence identity, and coverage of total sequence length by the match region. These parameters are customized for each protein family in order to achieve high fidelity in automated annotation systems. The flexible syntax makes it possible to use existing protein family classifiers, such as Pfam and TIGRFAMs HMMs, in new ways. It is especially useful in assigning GO terms to proteins such as SelD (selenide, water dikinase) that have different roles in different contexts.

go_ref_id: GO_REF:0000031
title: NIAID Cell Ontology Workshop
authors: Alexander D. Diehl, Alison Deckhut Augustine, Judith A. Blake, Lindsay G. Cowell, Elizabeth S. Gold, Timothy A. Gondre-Lewis, Anna Maria Masci, Terrence F. Meehan, Penelope A. Morel, Anastasia Nijnik, Bjoern Peters, Bali Pulendran, Richard H. Scheuermann, Q. Alison Yao, Martin S. Zand, Christopher J. Mungall
year: 2008
abstract: The NIAID sponsored a Cell Ontology Workshop, May 13-14, 2008, in Bethesda, focusing on improving representation of immune cell types in the Cell Ontology. The participants in the workshop worked together to extend the current ontology in the area of immune cell types and to provide the necessary information for the upcoming restructuring of the Cell Ontology in single-inheritance form with genus-differentia definitions.
url: http://www.bioontology.org/wiki/index.php/NIAID_Cell_Ontology_Workshop_May_2008

go_ref_id: GO_REF:0000032
title: Inference of Biological Process annotations from inter-ontology links
authors: Christopher J. Mungall, Tanya Z. Berardini, David P. Hill
abstract: We use the GOBO library to propagate annotations from Molecular Function to Biological Process. This results in both increased numbers of annotations, and increased consistency between curators.
url: http://wiki.geneontology.org/index.php/GAF_Inference