This work is supported by: a grant from the USDA-NSF Microbial Genome Sequencing Program,

a Presidential Early Career Award for Scientists and Engineers from the NSF, and by an R01 from the NHGRI (NIH).

Berkeley PHOG:PhyloFacts Orthology Group Prediction

Web Server

Ruchira S. DattaUC Berkeley

Centre for Integrative BioinformaticsVrije Universiteit Amsterdam

June 24th, 2009

Acknowledgments• Principal Investigator

– Kimmen Sjölander• Programmer

– Chris Meacham• Research Associate

– Bushra Samad• Undergraduate Student

– Christoph Neyer

•This work is supported by: •a grant from the USDA-NSF Microbial Genome Sequencing Program,

•a Presidential Early Career Award for Scientists and Engineers from the NSF, •and by an R01 from the NHGRI (NIH).

"Nothing in Biology Makes Sense Except in the Light of Evolution"

Theodosius Dobzhansky (1973)

Genome trees: Phylogenomic reconstruction of species trees (based on multiple genes).Accomplished using gene-matrix (aka supermatrix approaches) or supertree methods.

4

Homology-based functional annotations are fraught with systematic error

Gilks et al, “Modeling the percolation of annotation errors in a database of protein sequences” Bioinformatics 2002Galperin and Koonin 1998 "Sources of Systematic Error in Functional Annotation of Genomes" In Silico Biology.Brenner, 1999 "Errors in Genome Annotation" Trends Genet.Brown & Sjölander, "Functional Classification using Phylogenomic Inference." PLoS Computational Biology, 2006

Neofunctionalization stemming from gene duplication

Domain shuffling

Percolation of annotation errors

Two key statistics: 1. Up to 25% of sequences may be mis-annotated. 2. Fewer than 3% of sequences have experimental support for their annotated function

5

Gene duplication produces protein superfamilies including paralogs with divergent functions

6

The evolution of PhyloFacts

EXFOLIATIVE TOXIN AStaphylococcus aureus

TRYPSINFusarium

Oxysporum

16%ID

UCSC Protein structure prediction in CASP2

Celera Genomics human genome annotation

Over 57K protein families and domains with predicted phylogenies, structures, functions for millions of proteins

Over 1.4M hidden Markov models for classification of user-submitted sequences to families and subfamilies

•PhyloBuilder build-your-own protein family phylogeny

•INTREPID functional site prediction server

•PhyloFacts orthology prediction

Berkeley Phylogenomics Group PhyloFacts Encyclopedias

7

Construction of genome-scale phylogenomic libraries

Cluster genome into global homology groups

Predict protein structurePredict key residues

Predict cellular localization.

Include homologs from other species

Construct HMMs for the family and for individual subfamilies.

Construct multiple sequence alignment

Construct phylogenetic trees. Overlay with annotation data. Identify subfamilies.Retrieve key literature

Deposit book in library

FlowerPower

INTREPID & Discern

SCI-PHY

SATCHMO

PHOG

(Krishnamurthy et al, Genome Biology, 2006)

8

PhyloFacts Orthology Group (PHOG) prediction

Datta et al., Nucleic Acids Research Web Server Issue, 2009http://phylofacts.berkeley.edu/orthologs/

Inputs accepted: UniProt accession or ID, GenBank ID for protein sequences only.Sequences can also be submitted in FASTA format for BLAST analysis.

9

Homologs: orthologs and Homologs: orthologs and paralogsparalogs

Homologs: genes that have descended from a common ancestral gene.

Gene 1

Gene2

Ancestral gene

Orthologs: the last evolutionary event separating the genes was speciation.

S

Paralogs: the last evolutionary event separating the genes was duplication.

D

Courtesy of Nir Yosef

10

Why is orthology important?

Phylogenomic inference of protein function

Prediction of biological pathways and network alignment

PPI prediction (using interlog analysis)

Reconstructing the Tree of Life

Phylogenetic profile construction

11

Many Orthology Prediction Methods(InParanoid, OrthoMCL, COGs, Reciprocal BLAST, TreeFam, etc.)

• Inputs– BLAST scores– Phylogenetic analysis – Species tree– Alignment analysis– Etc.

• Evaluation criteria– Agreement at GO– Agreement in EC number– Agreement at other annotation– Consistency with observed

interactions– Consistency with synteny

Most rigorous approach: Gene-tree species-tree reconciliation.Process is computationally expensive: Gather many homologs from many species; construct alignments; estimate protein family (gene) phylogeny; reconcile gene tree and species tree; label nodes as duplication or speciation; predict orthologs as those whose MRCA represents a speciation event.

Issues: restrict data to whole genomes results in sparse taxonomic sampling (reduced phylogenetic signal). Including sequences from partially sequenced genomes will include duplicate entries for same gene.

TreeFam-A does full phylogenetic tree reconciliation for whole genomes and follows this up with manual curation. Restricted to animal gene families.

p12

Orthology: the MRCA must correspond to a speciation event. (By this definition, the Yeast sequence is orthologous to all sequences in this example.)

Super-orthology is more restrictive than orthology: all nodes on a path between two leaves must correspond to a speciation event. (Zmasek & Eddy, 2002)

S

D

Orthology prediction using treesOrthology prediction using trees

Human, Chimp, Mouse, Rat, Fly, Worm

H1 C1 M1 R1 F1 W1 H2 C2 M2 R2 F2 W2Yeast

Super-orthologs

13

PHOG approach: Each protein can belong to multiple trees (for global homology or individual domains)

• Trees for individual domains as well as whole proteins.• Homologs from both whole and partially sequenced genomes (as well

as metagenome sequences). • These different trees provide different views of the evolutionary history

of the molecule• Different PHOG variants are available

– PHOG-O (standard definition: the MRCA can be labelled as speciation)– PHOG-S (super-orthology: all sequences in a subtree are each other’s

closest ortholog based on tree distance)– PHOG-T (thresholded variant: allows a subtree to expand past the PHOG-

S cutoff; can be tuned to a specific taxonomic distance)

14

PHOG uses tree distances to avoid ambiguous orthology calls

15

PHOG Report

16

PhyloScope view of a PhyloFacts tree, coloring super-orthology groups

Note: Standard orthology is not transitive, but super-orthology is. This is convenient for functional annotation and species phylogeny estimation.

17

Example PHOG report

18

Reciprocal Nearest Neighbor (RNN) “Orthologs”

• For a protein P in taxon (species) S, its RNN ortholog in taxon (species) T is the

protein Q in taxon T whose tree distance to P is smallest, such

that P is also the protein in S whose tree distance to Q is smallest

• Some proteins may have no RNN ortholog in a given taxon, due to gene loss or incomplete sequencing in some species

19

20

RNN vs Tree Reconciliation

• Tree reconciliation (the accepted standard for phylogenetic ortholog identification):• Comparing the gene tree to the species tree

• Issue: Requires accurate species tree

• Labelling internal nodes as duplication or speciation events• One can argue that this is the “right” thing to do• However, many parts of a species phylogeny are unresolved

• This will lead tree reconciliation to give incorrect results

• Tree reconciliation is time-consuming

• RNN is a fast alternative• RNN can give inconsistent results when genes are

missing

21

Mutually Reciprocal “Orthologs”can be incorrect (when genes from taxa are missing)

22

Add new nodes corresponding to species

Draw edges of length 0 from each observed protein sequence (leaf node) to the corresponding species

S

D

Augment the Tree

Human, Chimp, Mouse, Rat, Fly, Worm

H1 C1 M1 R1 F1 W1 H2 C2 M2 R2 F2 W2Yeast

Yeast

23

Computing RNN Orthologs

• A shortest path between nodes in a tree is V-shaped– From one node up to their most recent common ancestor down to

the other node

• Use dynamic programming– Use breadth-first search to order all nodes in the tree

• This order goes from root to leaves• Each parent is visited before any of its children

– Find the closest protein in each taxon to each node, and record its distance

• Go up from the leaves to the root• Then go down from the root to the leaves

• O(nm) where n = # nodes, m = # taxa– We have m <= n/2 as can omit single-protein taxa

24

Inparalogs & Coorthologs

• Fast computation of inparalogs:– Proteins belonging to pure subtrees

• Containing only proteins of a single taxon

• From previous method:– P0, Q0 are RNN orthologs

– P0, P1, …, Pk are inparalogs

– Q0, Q1, …, Ql are inparalogs

– Then (P0, P1, …, Pk) are co-orthologs of (Q0, Q1, …, Ql)

25

PHOG-S: SuperorthologyA PHOG-S is a maximal subtree such that all its leaves are RNN orthologs or co-

orthologs of each other

26

PHOG-T: Thresholded PHOG

• Let N be a node which is maximal such that P is the nearest protein to N in taxon T

• PHOG-S: Among all such nodes N which contain more than one species, those which are minimal, ordered by inclusion

• Let Q be the next nearest protein to N in taxon T

• Duplication distance: (dist(N,P)+dist(N,Q))/2

• Thresholded PHOG: If the duplication distance is less than the threshold, keep proceeding up the tree to the next maximal node

27

Dataset: 100 (non-homologous) human sequences from TreeFam-A, filtered to remove homologs.

PHOG-O: Standard orthology definitionPHOG-S: Super-orthologs (Zmasek & Eddy)PHOG-T: thresholded PHOGsPHOG-T(M); optimized for mousePHOG-T(Z): optimized for zebrafishPHOG-T(F): optimized for fruit fly

Ortholog prediction accuracy

Assessed vs TreeFam-A manually curated orthologs

28

Interolog analysis(infer interactions based on experimentally observed interactions in orthologs)

29

Phylogenomic inference of pathways and protein-protein interaction

30

Pathways and PPI Through PHOG

31

Protein-Protein Interaction

32

Interologs Through PHOG

33

Coming soon: Interactome Viewer

Dean Starrett, Ph.D

34

Postdoc and PhD student

positions!

This work is supported by: a grant from the USDA-NSF Microbial Genome Sequencing Program,

a Presidential Early Career Award for Scientists and Engineers from the NSF, and by an R01 from the NHGRI (NIH).

Thanks to the many people who have made this possibleKimmen Sjölander, Principal InvestigatorChris Meacham, ProgrammerBushra Samad, Research AssociateChristoph Neyer, Undergraduate Student

And previous group members for PhyloFacts development:Nandini Krishnamurthy, Postdoctoral ResearcherDuncan Brown, Ph.D Student