g:GOSt Gene Group Functional Profiling
 g:Cocoa Compact Compare of Annotations
 g:Convert Gene ID Converter
 g:Sorter Expression Similarity Search
 g:Orth Orthology search
Welcome! About Contact Beta Archives R
J. Reimand, M. Kull, H. Peterson, J. Hansen, J. Vilo: g:Profiler -- a web-based toolset for functional profiling of gene lists from large-scale experiments (2007) NAR 35 W193-W200 [PDF]
J. Reimand, T. Arak, J. Vilo: g:Profiler -- a web server for functional interpretation of gene lists (2011 update) Nucleic Acids Research 2011; doi: 10.1093/nar/gkr378 [PDF]
[?] Query
(1 gene, protein, or probe)
[?] Organism

[?] Distance measure
[?] Number of probes
[?] Output type

[?] Numeric ID treated as

[?] Microarray dataset


Welcome to g:Sorter!

g:Sorter is a tool for gene expression similarity search. For a selected single gene, protein or probe ID, g:Sorter finds a number of most similar coexpressed (correlated) expression profiles in a specified dataset. Most dissimilar reversely expressed (anti-correlated) profiles may also be selected. A large collection of public gene expression datasets from ArrayExpress are available for analysis for a list of organisms.
The result of g:Sorter analysis is one or more sorted lists of microarray probesets. Several lists are retrieved when several probesets correspond to given input. g:Sorter also finds an intersection list that contains the common elements of all retrieved lists. All related lists can be passed on to functional profiling in g:GOSt and g:Cocoa.

For integrated gene co-expression query across many datasets, please visit our MEM tool.


Examples
  • Query 1: 50 probesets with most similar expression patterns to BRCA2 (Pearson correlation) in MAQC-II human breast cancer dataset (See also query to g:Cocoa).
  • Query 2: Same as above, but retrieving both correlated and anti-correlated patterns (mirroring expression query).
  • Query 3: Same as above, but but Excel Spreadsheet (XLS) produced as output (output format).



g:Profiler 2005-2011
Jüri Reimand & Tambet Arak & Jaak Vilo @ BIIT Group, Institute of Computer Science, University of Tartu, Estonia.