Orthology Analysis
Erik Sonnhammer
Center for Genomics and Bioinformatics
Karolinska Institutet, Stockholm
Outline
• Basic concepts
• BLAST-based approaches to orthology
• Tree-based approaches to orthology
• Domain-level orthology
Homologs
= genes with a common origin
• May be genes in the same or in different organisms
• Does not say that function is identical
• Can only be true or false, and not a percentage!
• Homologs have the same 3D-structure layout
Homologs
Orthologs Paralogs
Gene Y1 in human
Gene Y in rat
Gene Y2 in human
DGene X in ancient animal
Gene Yin ancient mammal
In-paralogs
Orthologs: Orthologs: separated by speciationseparated by speciation
Gene Xin ancient mammal
Gene Xin human
Gene X in rat
Time
Orthologs
Orthologs
Out-paralogs
paralogs
speciation
D
S
S
In/Out-paralog definition
In-paralogs ~ co-orthologsparalogs that were duplicated after the speciation and hence are orthologs to a cluster in the other species
Out-paralogs = not co-orthologsparalogs that were duplicated before the speciation. Not necessarily in the same species.
Sonnhammer & Koonin, Trends Genet. 18:619-620 (2002)
Orthologs for functional genomicsOrthologs for functional genomics• Co-orthologs / inparalogs are more likely than outparalogs to
have identical biochemical functions and biological roles.
• Co-orthologs can be used to discover human gene function via model organism experiments
• Co-orthologs are key to exploit functional genomics/proteomics data in in model organisms
Orthology and function conservation
• Orthology does not say anything about evolutionary distance.
• Close orthologs, e.g. human-mouse are very likely to have the same biological role in the organism.
• Distant orthologs, e.g. human-worm are less likely to have the same phenotypical role, but may have the same role in the corresponding pathway.
Ortholog DatabasesSequence database Orthology
detection methodOrtholog database
SwTrembl proteomes Inparanoid (blast) Inparanoid
proteomes COGs (blast) COGs / KOGs
TIGR gene index COGs (blast) TOGA/EGO
proteomes OrthoMCL (blast) OrthoMCL
Pfam Orthostrapper (tree) HOPS
Pfam RIO (tree)
How to find orthologs?How to find orthologs?
1. Calculate phylogenetic tree, look for orthologs in the tree (Orthostrapper, Rio):
2. Two-way best matches between two species can be used to find orthologs without trees.
[However, in-paralogs are harder to find this way]
Two-way best match approachto finding orthologs
COGsCOG2813:
Out-
paralogs
orthologs
Inpara-n-oidInparalog ‘n ortholog identification
Blue = species 1
Red = species 2
Inparanoid
Blue = species 1
Red = species 2
No overlap - no problems:
Partial overlap - separate:
Complete overlap - merge:
Resolve overlapping clustersResolve overlapping clusters
Inparalog score
Score for inparalog P = (scoreAP - scoreAB) / (scoreAA - scoreAB)
0 20 40 60 80 100%
A
P
B
Confidence values for main orthologs from sampling
TVHIVDDEEPVR---KSLAFM---LTMNGFAT+ ++DD +R K L M +T+ G ATILLIDDHPMLRTGVKQLISMAPDITVVGEA
Sampling with replacement; insertions kept intact
GAFDEP---LVTHVR..........GA + ++T +RGAEEHMAPDILTLLR..........
“Bootstrap alignment” -> “bootstrap score”
Confidence = (bootstrap alignments best-best matches / nr of bootstraps)
http://inparanoid.cgb.ki.se
inparanoid.cgb.ki.se
Remm et al, J. Mol. Biol. 314:1041-1052 (2001)
Homo Sapiens vs. C. elegans
Ortholog group sizes, human vs XVersion 2.5:
08-apr-03151360 sequences from Swissprot-TREMBL
44996 sequences from Homo sapiens26674 sequences from Mus musculus20316 sequences from Drosophila melanogaster20997 sequences from Caenorhabditis elegans36751 sequences from Arabidopsis thaliana6910 sequences from Saccharomyces cerevisiae8709 sequences from Escherichia coli
Species
Number of orthologs (orthologous groups) in H.sapiens
Number of sequences (in-paralogs) from H.sapiens in orthologous groups
Number of sequences (in-paralogs) from this species in orthologous groups
M.musculus 12458 19532 17055D.melanogaster 5549 15259 9854C.elegans 4541 14222 6537A.thaliana 3258 10863 12178S.cerevisiae 2175 7265 2751E.coli 599 2144 1037
Nr of inparalogs per ortholog group
Species Avg. inparalogs in model organism ortholog groups
Avg. inparalogs in human
ortholog groups
Mouse 1.36 1.56
Fly 1.77 2.75
Worm 1.44 3.13
Mustard weed 3.73 3.33
Yeast 1.26 3.34
E. coli 1.73 3.57
• No guarantee that the same segment is used in different sequences
• No evolutionary distance model
• Does not take multiple domains into account
Drawbacks of Blast-basedorthology assignment
Domain orthology• Inparanoid Human-Fly ortholog pairs with domains in
Pfam-A 13.0: 20335
• Different domain architectures: 5411– Many of these are minor differences, e.g. 22 vs 21 Spectrin repeats
– Sometimes the difference is big:
ef-hand UCH
TBC UCH
Tree-based approaches
Distance-based tree building
• Bootstrapping: – randomly pick columns to bootstrap alignment, calculate tree
– Repeat 1000 times, frequency of node = bootstrap support
A2 A3
A1 4 8
A2 10
A1
A2
A3
1
3
5
2
A1 MKFYSLPNFPEN
A2 MKYYKLPDLPDE
A3 MRFYTACENPRS
Distance matrix
Orthology by tree reconciliation
Species tree
Gene tree
Infer 2 duplications and 2 losses
• Assumption that the species tree is fully known
• Does not give confidence values
• Gene trees become unreliable when involving a lot of sequences (more data -> less certainty)
• Computationally expensive
Drawbacks of tree reconciliationfor orthology assignment
Partial tree reconciliation
• Find pairwise orthologs by computer parsing of tree.
99
45
85
100
82
99
C14F5.4
AAF49194.1
AH6.2
F37H8.4
Y6E2A.9
C47D12.3
T04F8.1
AAF52138.1
PIR-S67168
Pairwise orthology confidence by ‘orthostrapping’
The original tree with bootstrap support values
C14F5.4
AAF49194.1
AH6.2
F37H8.4
Y6E2A.9
C47D12.3
T04F8.1
AAF52138.1
PIR-S67168
Pairwise orthology confidence by ‘orthostrapping’
01C14F5.4
10T04F8.1
00C47D12.3
00Y6E2A.9
00F37H8.4
00AH6.2
AAF52138.1
AAF49194.1
FlyWorm
C14F5.4
AAF49194.1
AH6.2
F37H8.4
Y6E2A.9
C47D12.3
T04F8.1
AAF52138.1
PIR-S67168
Pairwise orthology confidence by ‘orthostrapping’
02C14F5.4
20T04F8.1
10C47D12.3
00Y6E2A.9
00F37H8.4
00AH6.2
AAF52138.1
AAF49194.1
FlyWorm
99
45
85
100
82
99
C14F5.4
AAF49194.1
AH6.2
F37H8.4
Y6E2A.9
C47D12.3
T04F8.1
AAF52138.1
PIR-S67168
Pairwise orthology confidence by ‘orthostrapping’
099C14F5.4
980T04F8.1
810C47D12.3
770Y6E2A.9
770F37H8.4
770AH6.2
AAF52138.1
AAF49194.1
FlyWorm
orthostrapper.cgb.ki.se
Orthology is not transitive!
Multiple species at different distances may give erroneous groups, that includes out-paralogs
Orthology is not transitive!
-> Orthology strictly defined for only 2 species/clades
Combining species of different distances is very dangerous
But OK to combine multiple equidistant ones
YH1D1H2D2
D1 H2
Y
Domain-level orthology
HOPS - Hierarchy of Orthologs and Paralogs
eukaryota
metazoa
viridiplantae
fungi
nematoda
arthropoda
chordata
1. All species in Pfam are bundled in groups according to scheme:
2. Apply Orthostrapper to groups at same level in Pfam families
3. Display results in NIFAS
Pfam
Pfam in brief:
Profile-HMMHMMer-2.0
FULL alignment
Search database
Manually curated Automatically made
SEED alignmentrepresentative members
Description file
• Release 13.0 (April 2004):– 7426 families Pfam-A domain families
– Based on 1160000 sequences (Swissprot & Trembl)– 21980 unique Pfam-A domain architectures– 73% of all proteins have >=1 Pfam-A domain
HOPS results
Pfam 10, 6190 families:
• 2450 families (40%) have HOPS orthologs
• 1319 families (21%) have HOPS orthologs in all 6 pairwise comparisons
• 286356 pairwise orthology assignments (> 75% orthostrap)
Storm and Sonnhammer, Genome Research 13:2353-2362 (2003)
Ways to access HOPS
• NIFAS graphical browser
• By sequence ID at Pfam.cgb.ki.se/HOPS
• Flatfiles (Orthostrap tables of 2 clades)
Pfam.cgb.ki.se/HOPS
Evolution of Domain Architectures
NIFAS:
ATP sulfurylase /APS kinase
Orthologous shuffled domains?
ATP sulfurylase domain, metazoa vs fungi
APS kinase domain
HOPS orthologs of PPS1_HUMAN (ATP sulfurylase/APS kinase)
Summary of ATP sulfurylases/APS kinases:
Shuffled non-orthologous domains
Fungi
Metazoa
Conclusions
• Orthologs can be detected by – Blast: fast– tree: slow but less error-prone
• Species at different evolutionary distances should not be combined in orthology analysis
• Inparanoid and Orthostrapper were designed to find inparalogs but not outparalogs
• HOPS/NIFAS can be used to find domain orthologs and analyze domain architecture evolution
Future perspectives
• Multiparanoid – multiple species merging of pairwise Inparalogs.
• Functional divergence among inparalogs
Acknowledgments
– Christian Storm
– Maido Remm
– Andrey Alexeyenko
– Volker Hollich
– Mats Jonsson
http://sonnhammer.cgb.ki.se
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