OrthoDisease: A Database of Human Disease Orthologs
Release 2.0, March 2011


1. Introduction

InParanoid is a program for automatic identification of orthologs
while differentiating between inparalogs and outparalogs. An
InParanoid cluster is seeded by a reciprocally bestmatching ortholog
pair, around which inparalogs are gathered independently, while
outparalogs are excluded.  The InParanoid database is a collection of
pairwise ortholog groups aiming to include all 'completely sequenced'
eukaryotic genomes. By this we mean above 6X coverage, and less than
1% X letters in the protein sequences.


2. Online access

The OrthoDisease disease ortholog database is available both for
direct online access as well as for downloading. Online the user has
the option to view all clusters between two species, search for
clusters based on gene ID or free text search. 
Online access is available at:
http://orthodisease.sbc.su.se/.


3. Downloadable content

The current database is available for download at:
http://orthodisease.sbc.su.se/download

The files are stripped-down versions of the Inparanoid tables
containing InParanoid clusters with disease-association, only.
Note that only those protein ID have additional disease information
that can be found in the OMIM database. Thats why no non-human
protein ID has a disease annotation. 
The columns of the table files are tab-separated and contain the following information:
-cluster number  
-bit score       
-species name    
-inparalog score 
-Ensembl protein ID      
-boostrap confidence value       
-OMIM ID 
-OMIM description        
-OMIM class

Analysed sequences:

Both the original and the processed (i.e. non-redundant, keeping only
the longest transcript) sequences used for analysis can be downloaded
either as fasta files or in XML format from 
http://inparanoid.sbc.su.se/download/7.0_current/.

4. Citing OrthoDisease
"OrthoDisease: Tracking disease gene orthologs across 100 species"
Kristoffer Forslund*, Fabian Schreiber*, Nattaphon Thanintorn, Erik L.L. Sonnhammer
Briefings in Bioinformatics (submitted), 2011