Orthology Analysis

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Orthology Analysis
Erik SonnhammerCenter for Genomics and
BioinformaticsKarolinska Institutet, Stockholm
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Outline

• Basic concepts
• BLAST-based approaches to orthology
• Tree-based approaches to orthology
• Domain-level orthology

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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

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Homologs
Orthologs
Paralogs
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Orthologs separated by speciation
Gene Xin human
Orthologs
Gene Xin ancient mammal
Gene X in rat
S
Out-paralogs
paralogs
In-paralogs
D
Orthologs
S
speciation
Time
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In/Out-paralog definition

• In-paralogs co-orthologs
• paralogs that were duplicated after the
  speciation and hence are orthologs to a cluster
  in the other species
• Out-paralogs not co-orthologs
• paralogs that were duplicated before the
  speciation. Not necessarily in the same species.

Sonnhammer Koonin, Trends Genet. 18619-620
(2002)
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Orthologs 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

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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.

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Ortholog Databases
Sequence database Orthology detection method Ortholog 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)
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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
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Two-way best match approachto finding orthologs
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COGs

• COG2813

Out- paralogs
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Blue species 1 Red species 2
Inparalog n ortholog identification
Inpara-n-oid
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Blue species 1 Red species 2
Inparanoid
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Resolve overlapping clusters
No overlap - no problems
Partial overlap - separate
Complete overlap - merge
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Inparalog score
B
0
20
40
60
80
100
A
P
Score for inparalog P (scoreAP - scoreAB) /
(scoreAA - scoreAB)
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Confidence values for main orthologs from sampling

• TVHIVDDEEPVR---KSLAFM---LTMNGFA
• T DD R K L M T G A
• TILLIDDHPMLRTGVKQLISMAPDITVVGEA
• Sampling with replacement
  insertions kept intact
• GAFDEP---LVTHVR..........
• GA T R
• GAEEHMAPDILTLLR..........
• Bootstrap
  alignment -gt bootstrap score
• Confidence (bootstrap alignments best-best
  matches / nr of bootstraps)

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http//inparanoid.cgb.ki.se
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inparanoid.cgb.ki.se
Homo Sapiens vs. C. elegans
Remm et al, J. Mol. Biol. 3141041-1052 (2001)
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Ortholog group sizes, human vs X
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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
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Drawbacks of Blast-based orthology assignment

• No guarantee that the same segment is used in
  different sequences
• No evolutionary distance model
• Does not take multiple domains into account

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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

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Tree-based approaches
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Distance-based tree building
A1 MKFYSLPNFPEN A2 MKYYKLPDLPDE A3
MRFYTACENPRS
Distance matrix
1
A2 A3
A1 4 8
A2 10
A1 A2 A3
2
3
5

• Bootstrapping
• randomly pick columns to bootstrap alignment,
  calculate tree
• Repeat 1000 times, frequency of node bootstrap
  support

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Orthology by tree reconciliation
Species tree
Gene tree
Infer 2 duplications and 2 losses
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Drawbacks of tree reconciliation for orthology
assignment

• 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 -gt less certainty)
• Computationally expensive

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Partial tree reconciliation

• Find pairwise orthologs by computer parsing of
  tree.

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Pairwise orthology confidence by orthostrapping
The original tree with bootstrap support values
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Pairwise orthology confidence by orthostrapping
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Pairwise orthology confidence by orthostrapping
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Pairwise orthology confidence by orthostrapping
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orthostrapper.cgb.ki.se
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Orthology is not transitive!
Multiple species at different distances may give
erroneous groups, that includes out-paralogs
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Orthology is not transitive!
Y H1 D1 H2 D2
Y
H2
D1
-gt Orthology strictly defined for only 2
species/cladesCombining species of different
distances is very dangerousBut OK to combine
multiple equidistant ones
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Domain-level orthology
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HOPS - Hierarchy of Orthologs and Paralogs

1. All species in Pfam are bundled in groups
   according to scheme

1. Apply Orthostrapper to groups at same level in
   Pfam families
2. Display results in NIFAS

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Pfam
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Pfam in brief
SEED alignment representative members
Profile-HMM HMMer-2.0
Search database
FULL alignment
Description file
Manually curated
Automatically made

• 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 gt1 Pfam-A domain

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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 (gt 75
  orthostrap)

Storm and Sonnhammer, Genome Research
132353-2362 (2003)
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Ways to access HOPS

• NIFAS graphical browser
• By sequence ID at Pfam.cgb.ki.se/HOPS
• Flatfiles (Orthostrap tables of 2 clades)

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Pfam.cgb.ki.se/HOPS
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Evolution of Domain Architectures

• NIFAS

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ATP sulfurylase /APS kinase
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ATP sulfurylase domain, metazoa vs fungi
Orthologous shuffled domains?
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APS kinase domain
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HOPS orthologs of PPS1_HUMAN (ATP sulfurylase/APS
kinase)
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Summary of ATP sulfurylases/APS kinases
Shuffled non-orthologous domains
Metazoa
Fungi
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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

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Future perspectives

• Multiparanoid multiple species merging of
  pairwise Inparalogs.
• Functional divergence among inparalogs

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Acknowledgments

• Christian Storm
• Maido Remm
• Andrey Alexeyenko
• Volker Hollich
• Mats Jonsson

http//sonnhammer.cgb.ki.se