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[gofriends] Automatic Gene Ontology Annotation Tools

Sorin Draghici sorin at wayne.edu
Fri May 18 09:34:49 PDT 2007


Stefan,

Please add:

Purvesh Khatri, Bogdan Done, Archana Rao, Arina Done and Sorin Draghici. 
A semantic analysis of the annotations of the human genome 
<http://vortex.cs.wayne.edu/papers/semantic_analysis_bioinfo.pdf>. 
Bioinformatics, 21(16): 3416-3421, August 2005.

http://vortex.cs.wayne.edu/papers/semantic_analysis_bioinfo.pdf

This is an approach to predict GO annotations based on a semantic 
analysis of the current GO content. There is no name as such in the 
paper. A tentative name could be Onto-Semantics.


Regards,

Sorin

Stefan Goetz wrote:
> Hi All, 
> we are trying to make an up-to-date list of all the working, free and
> public available tools who are able to generate or predict automatically
> (not only  recover) gene ontology annotations based on (novel) sequence
> data. We made a first listing and would like to invite you all to
> discuss, complete or correct this list. Thanks in advanced for your
> contributions and help. 
> Regards,
> Stefan Goetz
>
>
> Automatic Gene Ontology Annotation Tools
> (alphabetical order)
>
> NR: 1
> Name: AutoFact
> Paper: BMC BioInformatics, 2005
> High-throughput: yes
> Input Data: fasta
> Method: sequences are classified into 6 different annotation categories,
> blast-based
> Evidence Codes: no
> Manual GO curation: GO, COG, KEGG, Locus, ?
> GO tree visualisation: no
> GO graph visualisation: no
> Avalible: Perl implementation online
> Description: An Automatic Functional Annotation and Classification Tool
> Link: http://www.bch.umontreal.ca/Software/AutoFACT.htm
>
> NR: 2
> Name: Blast2GO
> Paper: Bioinformatics. 2005 Sep
> High-throughput: yes (arround 30 000 Seqs, memory dependent)
> Input Data: fasta, blast-xml
> Method: combination of similarity search based (blast), domain based
> (interproscan) and datamining based (annex) annotation
> Annotation types: GO, InterPro, Kegg
> Evidence Codes: yes
> Manual GO curation: yes
> GO tree visualisation: no
> GO graph visualisation: yes
> Avalible: java web start + web service 
> Description: A universal Gene Ontology annotation, visualization and
> analysis tool for functional genomics research
> Link: http://www.blast2go.org
>
> NR: 3
> Name: GOanna/AgBase
> Paper: BMC Genomics
> High-throughput: file upload (size limit?), (user gets notified by email
> when data is ready)
> Input Data: fasta
> Method: Top-Blast
> Annotation types: GO, ?
> Evidence Codes: yes
> Manual GO curation: no
> GO tree visualisation: -
> GO graph visualisation: -
> Avalible: webinterface
> Description: GOanna is used to find annotations for proteins using
> similarity search. The resulting file contains GO annotations of the top
> BLAST hits. The sequence alignments are also provided so the user can
> use these to access the quality of the match.
> Link: http://agbase.msstate.edu/GOAnna.html
>
> NR: 4
> Name: GOAnno
> Paper: Bioinformatics. 2005 May
> High-throughput: one at a time
> Input Data: only proteins, fasta
> Method: evolutionary information in multible alignments organized
> herarchically into functional subfamilies
> Annotation types: GO, ?
> Evidence Codes: no
> Manual GO curation: no
> GO tree visualisation: -
> GO graph visualisation: -
> Avalible: webinterface
> Description: GO annotation based on multiple alignment
> Link: http://bips.u-strasbg.fr/GOAnno/
>
> NR: 5
> Name: GOblet
> Paper: Two NAR papers, 2003,2004
> High-throughput: max ~150 seqs
> Input Data: fasta
> Method: all-hits mapping with probability scoring	
> Annotation types: GO
> Evidence Codes: no
> Manual GO curation: no
> GO tree visualisation: yes
> GO graph visualisation: no
> Avalible: webinterface, output in java applet
> Description: A platform for Gene Ontology annotation of anonymous
> sequence data
> Link: http://goblet.molgen.mpg.de
>
> NR: 6
> Name:GoFigure + GoDel
> Paper: Bioinformatics. 2003 Dec
> High-throughput: one at a time
> Input Data: fasta
> Method: GoFigure (mappping of GO terms to Blast Hits) + GODel (Selection
> of terms by eValue and evicdence codes)
> Annotation types: GO
> Evidence Codes: yes
> Manual GO curation: no
> GO tree visualisation: no
> GO graph visualisation: yes
> Avalible: webinterface
> Description: BLAST to predict Gene Ontology annotation
> Link: http://udgenome.ags.udel.edu/gofigure/
>
> NR: 7
> Name: GOtcha
> Paper: no publication
> High-throughput: one at a time
> Input Data: fasta?
> Method: all-hits mapping with probability scoring
> Annotation types: GO
> Evidence Codes: yes
> Manual GO curation: no
> GO tree visualisation: yes
> GO graph visualisation: no
> Avalible: webinterface
> Description: GOtcha uses sequence similarity searches to associate GO
> terms with your sequence. These are ranked by probability and displayed
> graphically on a subtree of Gene Ontology. You will need an up to date
> browser and JavaScript to get the best from the results.
> Link: http://www.compbio.dundee.ac.uk/gotcha/gotcha.php
>
> NR: 8
> Name: HT-GO-FAT
> Paper: no publication
> High-throughput: yes
> Input Data: -
> Method: sequence similarity (custom blast db)
> Annotation types: GO, ?
> Evidence Codes: no
> Manual GO curation: ?
> GO tree visualisation: ?
> GO graph visualisation: ?
> Avalible: Windows .NET (download after request)
> Description: High Throughput Gene Ontology Functional Annotation Toolkit
> (Ht-Go-Fat) Utilized for Animal and Plant
> Link: http://genome4.ars.usda.gov/farm/dload.php
>
> NR: 9
> Name: InterProScan
> Paper: Nucleic Acids Res. 2007
> High-throughput:  max. 10 Seqs (web interface)
> Input Data: fasta
> Method: GOs through domains
> Annotation types: GO, ?
> Evidence Codes: no
> Manual GO curation: no
> GO tree visualisation: no
> GO graph visualisation: no
> Avalible: webinterface, web service
> Description: Domain searches agains: BlastProDom, FPrintScan, HMMPIR,
> HMMPfam, HMMSmart, HMMTigr, ProfileScan, ScanRegExp, SuperFamily,
> SignalPHMM, TMHMM, HMMPanther, Gene3D	
> Link: http://www.ebi.ac.uk/InterProScan/
>
> NR: 10
> Name: JAFA
> Paper: no publication
> High-throughput: one at a time
> Input Data: only proteins, fasta
> Method: Joined Assembly of Function Annotations (InterProScan,
> GOtcha,GoFigure, Goblet, Phydbac)
> Annotation types: GO
> Evidence Codes: yes/no
> Manual GO curation: no
> GO tree visualisation: no
> GO graph visualisation: no
> Avalible: webinterface
> Description: Joined Assembly of Function Annotations (InterProScan,
> GOtcha, GoFigure, Goblet, Phydbac)
> Link: http://jafa.burnham.org
>
> NR: 11
> Name: OntoBlast
> Paper: Nucleic Acids Res. 2003 
> High-throughput: one at a time
> Input Data: only protein, fasta
> Method: sequence similarities
> Annotation types: GO
> Evidence Codes: no
> Manual GO curation: no
> GO tree visualisation: no
> GO graph visualisation: no
> Avalible: webinterface
> Description: OntoBlast function: From sequence similarities directly to
> potential functional annotations by ontology terms
> Link: http://functionalgenomics.de/ontogate/
>
>
> NR: 12
> CompuGen?
>
> NR: 13
> GOA?
>
> NR: 13
> GOAT?
>
> NR: 14
> est2uni?
>
>
> ...
>
>
>
> --
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>
>   

-- 
Sorin Draghici, Ph.D. 


Director of the Bioinformatics Core, Karmanos Cancer Institute

Associate Professor		Tel: (313) 577-5484
Dept. of Computer Science	Fax: (313) 577-6868
Wayne State University
5143 Cass Ave, Room 431 State Hall, 
Detroit, MI, 48202
WWW: http://vortex.cs.wayne.edu/Sorin/ (personal)
WWW: http://vortex.cs.wayne.edu/Projects.html (lab)


Check out my recent book: Data Analysis Tools for Microarrays:
http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C3154&parent_id=&pc=


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