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Orthology predictions for whole mammalian genomes Leo Goodstadt MRC Functional Genomics Unit Oxford University

Orthology predictions for whole mammalian genomes

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Orthology predictions for whole mammalian genomes. Leo Goodstadt MRC Functional Genomics Unit Oxford University. Finishing. “Evolution of Orthologues” Selection pressures in orthologues and paralogs. “Gene Duplications” Reproduction, immunity or chemosensation. - PowerPoint PPT Presentation

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Page 1: Orthology predictions for whole mammalian genomes

Orthology predictions for whole mammalian genomes

Leo GoodstadtMRC Functional Genomics Unit

Oxford University

Page 2: Orthology predictions for whole mammalian genomes

Fini

shin

g

““Evolution of Orthologues”Evolution of Orthologues”

Selection pressures in orthologues and paralogsSelection pressures in orthologues and paralogs

““Gene Duplications”Gene Duplications”

Reproduction, immunity or chemosensationReproduction, immunity or chemosensation

““Synonymous substitution rates”Synonymous substitution rates”

Mutation and selection varies by chromosome sizeMutation and selection varies by chromosome size

““Gene birth in the human lineage”Gene birth in the human lineage”

Ongoing duplications underlie polymorphismOngoing duplications underlie polymorphism

Page 3: Orthology predictions for whole mammalian genomes

Orthology is the keyOrthology is the key

Page 4: Orthology predictions for whole mammalian genomes

We are “consumers” of orthology / paralogy

Started off using Ensembl predictions

Ensembl 1:1 covered 50% of predicted mouse genes.

Ewan’s manual survey said 80%

How it started

Page 5: Orthology predictions for whole mammalian genomes

Paralogues evolve fast (and are fun!)

1) General observations for all mammalian genomes

Page 6: Orthology predictions for whole mammalian genomes

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2) Observations for whole clades of species

Page 7: Orthology predictions for whole mammalian genomes

3) Inparalogues define lineage specific biology

Marsupial / Monodelphis biology revealed by Marsupial / Monodelphis biology revealed by lineage specific geneslineage specific genes

• ChemosensationChemosensation (OR, V1R and V2R )

• ReproductionReproduction (Vomeronasal Receptors, lipocalins, -microseminoprotein (12:1))

• ImmunityImmunity (IG chains, butyrophilins, leukocyte IG-like receptors, T-cell receptor chains and carcinoembryonic antigen-related cell adhesion molecules )

pancreatic RNAses • DetoxificationDetoxification (hypoxanthine phosphoribosyltransferase homologues nitrogen poor

diets)

• KRAB ZnFingersKRAB ZnFingers

Page 8: Orthology predictions for whole mammalian genomes

4) Interesting stories in the aggregate

Page 9: Orthology predictions for whole mammalian genomes

5) Treasure trove in the details

clade: #2 (ortholog_id = 17117 in panda) 159 mus genes 47 genes new to assembly 36 10 genes completely new to assembly 36 Interpro matches for this clade:

!!! Expansion mainly on chr5 and 14, although single (pseudogene?) versions on chr13 and chr16.!!! Mouse DLG5 is: chr14:22,966,420-22,978,653 (expressed in testis: AK147699)

gene identifier order chrm exons stop length -------------------- ----- ---- ----- ---- ------ MUS_GENE_21705 6639 5 spermatogenesis associated glutamate

(E)-rich protein 1, pseudogene 1 ; ENSMUSP00000086007 4 182 MUS_GENE_22420 6643 5 predicted gene, EG623898 ; ENSMUSP00000099126 2 72 < MUS_GENE_19599 6646 5 spermatogenesis associated glutamate

( E)-rich protein 1, pseudogene 1 (Speer1-ps1) on

chromosome 5 ; NCBIMUSP_83776567 4 157 < MUS_GENE_23688 6651 5 predicted gene, EG623898 ; ENSMUSP00000094421 2 72 MUS_GENE_19774 6657 5 spermatogenesis associated glutamate

(E)-rich protein 3 ;

On going mouse inparalogues analysis: Lots and lots of reproductive genes

Page 10: Orthology predictions for whole mammalian genomes

Secretoglobin Protein Family members: Androgen-binding proteins. Emes et al. (2004) Genome Res.

14(8):1516-29

6) Candidates for evolutionary and functional analyses

Page 11: Orthology predictions for whole mammalian genomes

Hedges, SB Nature Reviews Genetics 3, 838 -849 (2002)

Available GenomesAvailable GenomesAndAnd

DivergencesDivergences

Page 12: Orthology predictions for whole mammalian genomes

How do we find function in the genome?

• Nothing in Biology Makes Sense Except in the Light of Evolution. Theodosius Dobzhansky (1900-1975).

Page 13: Orthology predictions for whole mammalian genomes

How to find the function in the genome?

Similar Sequences

Common Ancestry (homology)

Similar Structures / Folds

Similar Functions ?

(Genes / Genome regions)

Page 14: Orthology predictions for whole mammalian genomes

ARs

WholeGenome

How much of the genome is functional?How much of the genome is functional?Compare with the mouse

Ancestral Repetitive (AR) Ancestral Repetitive (AR) sequence is is non-functional and has evenly non-functional and has evenly distributed conservation scores (red) distributed conservation scores (red) (symmetrical bell shaped due to biological variation)

Whole GenomeWhole Genome sequence contains contains some functional sequence under some functional sequence under selection and thus has a small excess selection and thus has a small excess of conserved sequence under of conserved sequence under purifying selectionpurifying selection(asymetrical)

Functional sequence ==Whole GenomeWhole Genome - Ancestral Ancestral Repetitive Repetitive = 5%= 5%

N.B. This is an estimate that doesn’t take into account sequence

•Turning over rapidly (not shared by mouse/human)•Under positive (diversifying) selection

Page 15: Orthology predictions for whole mammalian genomes

The human genome (euchromatic sequence)

Unknown (old repetitive junk?)

Protein coding: 1.2%UTR: 0.3%

Repeats(Transposable elements, …)~45%

Conserved non-coding (3.5% ?)

Neutral

Page 16: Orthology predictions for whole mammalian genomes

Conserved non-coding materialConserved non-coding material

• Transcription factor binding sitesTranscription factor binding sites• Enhancers, insulators and other Enhancers, insulators and other

non-transcribed regulatory elementsnon-transcribed regulatory elements• Alternative splicing signalsAlternative splicing signals• Transfer RNAs, ribosomal RNAsTransfer RNAs, ribosomal RNAs• Small RNAs (Small RNAs (e.g. snoRNAs, microRNAs, siRNAs and piRNAs))

regulatory/gene silencing / RNA degradationregulatory/gene silencing / RNA degradation

• MacroRNAs (e.g. Xist)MacroRNAs (e.g. Xist)enzymatic? / chromosome inactivationenzymatic? / chromosome inactivation

Page 17: Orthology predictions for whole mammalian genomes

Functional parts of genes are highly conserved

Page 18: Orthology predictions for whole mammalian genomes

How many protein coding genes?• Walter Gilbert [1980s] 100k• Antequera & Bird [1993] 70-80k• John Quackenbush et al. (TIGR)

[2000] 120k• Ewing & Green [2000] 30k• Tetraodon analysis [2001] 35k• Human Genome Project (public) [2001] ~ 31k• Human Genome Project (Celera) [2001] 24-40k• Mouse Genome Project (public) [2002] 25k -30k• Lee Rowen [2003] 25,947• Human Genome Project (finishing) 20-25k [2004]• Current predictions [2008] 19-20k

Page 19: Orthology predictions for whole mammalian genomes

Traditional Genome OrthologyReciprocal BLAST best hits between longest

transcript of each gene (+ synteny)Assumes:• Protein similarity is proportional to

evolutionary distance (selection is invariant!)• Pairwise relationships adequately represent

the evolutionary tree• No gene losses or missing predictions • Alternative splicing can be ignored! • No gene translocations after tandem

duplication

Page 20: Orthology predictions for whole mammalian genomes

Orthology prediction methods

• Two genomes– Reciprocal best blast hit

• Multiple genomes– Clustering of

• reciprocal best hits• protein similarities

QueryBlast hits

Page 21: Orthology predictions for whole mammalian genomes

Reciprocal Blast Best Hits

Advantages:• Fast, Well understood• Works well for distant lineages• Can correlate with protein structure (domains)Disadvantages:• Only provides 1:1 orthologues in the best case• Can be difficult to reconcile with the species tree

Page 22: Orthology predictions for whole mammalian genomes

Genes on chromosome of species 1

Genes on chromosome of species 2

Page 23: Orthology predictions for whole mammalian genomes

?

Reciprocal Blast Best Hits

Page 24: Orthology predictions for whole mammalian genomes

?

Reciprocal Blast Best Hits

Page 25: Orthology predictions for whole mammalian genomes

How to add duplicated genes? synteny

Ensembl compara in the past• Local gene order tends to be conserved in

mammalian lineages• Look for inparalogs locally even if the protein

distances don’t add up (sequence error, sampling error etc.)

Page 26: Orthology predictions for whole mammalian genomes

?

Blast Best Hits in Local Regions

Page 27: Orthology predictions for whole mammalian genomes

?

Blast Best Hits in Local Regions

Page 28: Orthology predictions for whole mammalian genomes

Problems with relying only on synteny

Local homologs are often not inparalogs:

•Local rearrangements

•Missing predictions

(neighbouring orphans)

•Need sanity checking

Page 29: Orthology predictions for whole mammalian genomes

Human and Mouse chromosomes:

•Extensive rearrangements only over larger regions•Conservation of gene order in the short range

Page 30: Orthology predictions for whole mammalian genomes

Mouse chromosome 2Mouse chromosome 2

Rat chromosome 3Rat chromosome 3

One to oneOne to many

Many to manyMany to one

Olfactory Orthology from compara

Page 31: Orthology predictions for whole mammalian genomes

Olfactory OrthologyMouse chromosome 2Mouse chromosome 2

Rat chromosome 3Rat chromosome 3

One to oneOne to many

Many to manyMany to one

Page 32: Orthology predictions for whole mammalian genomes

Inparanoid

• Remm,M., Storm,C.E. and Sonnhammer,E.L.L. (2001) Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J. Mol. Biol. 314, 1041–1052.

• Avoids multiple alignments and phylogenetic methods for speed and to avoid errors

• Heuristics are implicitly phylogenetic

Page 33: Orthology predictions for whole mammalian genomes

How Inparanoid worksLongest Transcripts

Pairwise alignments scores

Reciprocal Best Hits are orthologues

Add lineageSpecific duplicates

(inparalogsinparalogs)With confidences

Resolve conflicts

Use cutoff2.

3.

4.

5.

Orthology

Page 34: Orthology predictions for whole mammalian genomes

Identify “inparalog” candidatesIdentify “main” orthologuesLongest Transcripts

Pairwise alignments scores

Reciprocal Best Hits are orthologues

Add lineageSpecific duplicates

(inparalogsinparalogs)With confidences

Resolve conflicts

Use cutoff2.

3.

4.

Orthology

Reciprocal Best Hits are orthologues

Add lineageSpecific duplicates

(inparalogsinparalogs)

Add lineageSpecific duplicates

(inparalogsinparalogs)With confidencesWith confidences

5.

Page 35: Orthology predictions for whole mammalian genomes

Confidence values for inparalogs

1. Most confident inparalog is when the inparalog is sequence identical to main orthologue.

2. Maximum value = scoreidentical – scoreorthologs

3. Confidence = (scoreinparalog – scoreorthologs) / (scoreidentical – scoreorthologs)

AA BB

Page 36: Orthology predictions for whole mammalian genomes

Resolving conflictsLongest Transcripts

Pairwise alignments scores

Reciprocal Best Hits are orthologues

Add inparalogsWith confidences

Resolve conflictsResolve conflicts

Use cutoff2.

3.

4.

5.

Orthology

1. Merge if orthologs already clustered in same group

2. Merge if two equally good best hits

3. Delete weaker group

4. Merge significantly overlapping

5. Divide overlapping

Page 37: Orthology predictions for whole mammalian genomes

Why are there conflicts?

• Protein differences are a proxy for evolutionary time

• Protein similarity scores approximate protein differences (sequence, alignment, estimation errors)

• Pairwise scores can be used to (conceptually) recover phylogenetic (tree) data

Page 38: Orthology predictions for whole mammalian genomes

Alternatives: phylogenetic methods

• Inparanoid is great because it models phylogeny explicitly

• Why not use phylogenetic methods directly?• Multiple estimators of protein distance

4 pairwise scores used out of 30

Page 39: Orthology predictions for whole mammalian genomes

Phylogenetic methods

• Iterative distance methods are very fast, suitable for whole genome analyses (variants on neighbor joining)

• Statistically consistent with evolutionary models (can have explicit error model with evolutionary distances, e.g. bionj)

• Inparanoid type consistency checking can be carried out after phylogeny is predicted

Page 40: Orthology predictions for whole mammalian genomes

Advantages

• Does not saturate over long evolutionary distances

• Easy to align / predict genes (unlike non-coding regions)

• Sometimes cDNA sequence is not available

Disadvantage

• Assumes constant evolutionary rate

• Assumes invariant selection

Is protein similarity a good proxy for evolutionary distance?

Page 41: Orthology predictions for whole mammalian genomes

• Redundant genetic codeRedundant genetic code, e.g., e.g. GC GCAA GC GCCC GC GCGG GC GCTT

• Third base of a codon “wobbles” without Third base of a codon “wobbles” without changing the translated amino acid changing the translated amino acid

• ddSS approximates neutral mutation rate approximates neutral mutation rate (without selection) in coding regions(without selection) in coding regions

Use Silent Mutations as a genetic clock

→ → AlanineAlanine}}

Page 42: Orthology predictions for whole mammalian genomes

• Easier to align than Ancestral Repeats

• Not neutral sequence!!

• Genomic > 2x variation in dS

• Assumes most gene families are local due to tandem duplication and share dS

• Assume (partial) gene conversions are infrequent

dS as proxy for evolutionary distance

Page 43: Orthology predictions for whole mammalian genomes

• Saturates at long evolutionary distances(but less so than many think)

• Beware of GC / codon frequency biases(use ML rather than heuristic methods)

• Multiple alignment / tree rather than pairwise for best results

• Slow to estimate accurately

• Missing values (where dS saturates)

dS Caveats

Page 44: Orthology predictions for whole mammalian genomes

codeml dS accuracy at 400 codons

Page 45: Orthology predictions for whole mammalian genomes

yn00 dS accuracy at 400 codons

Page 46: Orthology predictions for whole mammalian genomes

Use all transcripts

Page 47: Orthology predictions for whole mammalian genomes

PhyOP: transcript trees from dS1. Whole genome alignment identifies

homologues2. codeml for dS calculation 3. Ignore large dS 4. Hierarchical cluster5. Fitch Margoliash modified to handle missing

values to give giant transcript tree6. Heuristics based on lowest dS to select

1 “representative” transcript per gene7. Map Gene tree to species tree

Page 48: Orthology predictions for whole mammalian genomes

Fitch MargoliashMinimize

Where• dij is the pairwise distance estimate

• pij is the distance between i and j on the tree

Assumes that the error is a fixed proportion of the total distance

(Fitch and Margoliash, 1967) Easily adapted for missing values

Page 49: Orthology predictions for whole mammalian genomes

PhyOP pipeline Part 1

Page 50: Orthology predictions for whole mammalian genomes

3 ways in which transcript trees map to genes

• Simple cladesonly 1 transcript per gene in orthologous relationship: most genes

• Unambigous cladesAlternative transcripts are in the same orthologous relationships

Page 51: Orthology predictions for whole mammalian genomes

• Ambiguous cladesAlternative transcripts are in inconsistent relationships (small proportion)

3 ways in which transcript trees map to genes

Page 52: Orthology predictions for whole mammalian genomes

Where are most transcripts?

Assumption:Assumption:Most transcripts are not in any sort of

orthologous relationships: their conjugates have not been predicted.

RealityRealityMost transcripts are in the same clade as their

alternative transcripts:Because of shared exons, they are most similar to

their alternatively transcribed siblings.

Page 53: Orthology predictions for whole mammalian genomes

How to choose between alternative transcripts?

• Use conserved exon boundaries excludes Use conserved exon boundaries excludes exogenous sequenceexogenous sequence

• Use distance to its ortholog (not tree distance Use distance to its ortholog (not tree distance because these will be equal)because these will be equal)high dS means exogenous sequence and will be excludedWith multiple partially overlapping clades, this is more difficult

Page 54: Orthology predictions for whole mammalian genomes

PhyOP pipeline Part 2

Page 55: Orthology predictions for whole mammalian genomes

Example

Four alternative transcripts (1-4), 6 dog genes, 3 human genes

• Clade 1 transcriptsDoga1 Dogb1 Dogc1 Dogf1 Humanb1 Humanc1

• Clade 2 transcriptsDogb2 Dogc2 Doge2 Dogf2 Humana2 Humanc2

• Clade 3 transcriptsDoga3 Dogb3 Dogd3 Doge3 Dogf3

Humana3 Humanb3 Humanc3

Page 56: Orthology predictions for whole mammalian genomes

“Annointing” transcripts to keep: Example

Circularity / boot-strapping problem: The transcript in the other species which is used for “annointing” The transcript in the other species which is used for “annointing”

might itself be discardedmight itself be discarded• Doga1 is closer to Humanb1 than Doga3 to any human

transcript: Keep Doga1 discard Doga3

• Humanb3 is closer to Doge3 than Humanb1 to any dog transcript: Keep Humanb3 discard Humanb1

• Oops. Now no Human transcript is close to Doga1.

• Clade 1 transcriptsDoga1 Dogb1 Dogc1 Dogf1 Humanb1 Humanc1

• Clade 2 transcriptsDogb2 Dogc2 Doge2 Dogf2 Humana2 Humanc2

• Clade 3 transcriptsDoga3 Dogb3 Dogd3 Doge3 Dogf3 Humana3 Humanb3 Humanc3

Page 57: Orthology predictions for whole mammalian genomes

How to avoid circularity

• Previously: Use mean distance to all other transcripts in the other species. Close eyes. Hope problem goes away.

• Now: 1. Take all transcript pairs from all three clades starting with

the closest dS

2. “Annoint” both transcripts from the pair and throw away all other transcripts

3. Ignore all pairs which involve discarded transcripts4. Recurse5. Complicated by trying to keep merged genes

Page 58: Orthology predictions for whole mammalian genomes

From transcripts to genes

Page 59: Orthology predictions for whole mammalian genomes

dS for orthologues

Page 60: Orthology predictions for whole mammalian genomes
Page 61: Orthology predictions for whole mammalian genomes

dS distributions can be an indication of orthologue quality

Page 62: Orthology predictions for whole mammalian genomes

Dog vs. Human Genomes

Page 63: Orthology predictions for whole mammalian genomes

0

200

400

600

800

1000

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1400

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0 200 400 600 800 1000 1200

Mouse OR Gene Order

Rat

OR

Gen

e O

rder

Conservation of Gene Order in Mouse / Rat ORs

Page 64: Orthology predictions for whole mammalian genomes

How to improve on using dS?

• ds better dates the history, but fails for distant homologs.

• dn works for distant homologs, but tends to be subjected to selective pressures.

Can we combine them?Can we combine them?• Full codon evolutionary model would account for

this automatically• Use bootstrapping: if values -> random, no longer

informative

Page 65: Orthology predictions for whole mammalian genomes

TreeBeST

Tree Building guided by Species Treehttp://treesoft.sourceforge.net/treebest.shtml Heng Li• Tree merge algorithm: merge several trees

that are built from the same alignment with different models.

• Species-aware maximum likelihood:use species phylogeny to correct errors

Page 66: Orthology predictions for whole mammalian genomes

Maximize use of underlying data5 tree types:

1. Synonymous distance NJ2. Non-Synonymous distance NJ3. P distance NJ4. WAG maximum likelihood5. HKY maximum likelihood

Each predicted from same dataUse bootstrap values to identify optimal branches

using context free grammar

Page 67: Orthology predictions for whole mammalian genomes

Context Free Grammar in TreeBeST

Given a set of binary rooted trees with the same leaf set V, reconstruct a binary rooted tree such that:

• each branch of the resultant tree comes from one of the given trees

• the resultant tree minimizes a certain objective function

• additivity• topological independence

Page 68: Orthology predictions for whole mammalian genomes

Maximize use of underlying data

• Switch automatically between – codon: dN, dS;

– nucleotide: HKY and – protein: P-distance

depending on bootstrap

• Fix high probability errors by minimizing distance to species topology

Page 69: Orthology predictions for whole mammalian genomes

Slide from Heng Li

Page 70: Orthology predictions for whole mammalian genomes

Slide from Heng Li

Trees reconciled optimally

Page 71: Orthology predictions for whole mammalian genomes

Is TreeBeST more reliable?

Slide from Heng Li

Page 72: Orthology predictions for whole mammalian genomes

Caveats

• Bootstrapping may not be the most effective way to test the support for a particular tree given the underlying data

• The underlying data are not the state of the art but cannot use codon + ML for speed

• Limited by multiple alignment• Reconciliation with species tree can mask real

gene losses/duplications

Page 73: Orthology predictions for whole mammalian genomes

Alternative transcripts reveal merged genes

• Ensembl includes merged genes435 dog346 human

Page 74: Orthology predictions for whole mammalian genomes

Finding merged genes

Page 75: Orthology predictions for whole mammalian genomes

What is the best way to deal with alternative transcripts?

• Create virtual transcript

Virtual translation

Page 76: Orthology predictions for whole mammalian genomes

What is the best way to deal with alternative transcripts?

If two transcripts do not overlap and have homology to each other, they may be tandemly duplicated gene models merged in error

Include both transcripts in pipeline

Page 77: Orthology predictions for whole mammalian genomes

How to run orthology pipeline for whole genomes

• Take all proteins and cDNA• Make sure correspond exactly, no stop codons,

no genomic mismatches• All vs all blastall• Protein-guided alignments of cDNA• Create virtual translation peptide• Run tree prediction. E.g. TreeBeST• Reconcile with species tree to derive orthology

Page 78: Orthology predictions for whole mammalian genomes

Predicting orthology gets easier with more genes/species

1. Phylogenetic methods improve in power with more data2. Heuristic / pairwise methods decrease in power /

become more ambiguous with more data

Page 79: Orthology predictions for whole mammalian genomes

Why is orthology prediction so hard for mammals?

Because gene predictions is so hard

Page 80: Orthology predictions for whole mammalian genomes

The human genome (euchromatic sequence)

Unknown (old repetitive junk?)

Protein coding: 1.2%UTR: 0.3%

Repeats(Transposable elements, …)~45%

Conserved non-coding (3.5% ?)

Neutral

Page 81: Orthology predictions for whole mammalian genomes

Signals in DNA are weak

• non-canonical splice sites• promotors without TATA box• introns/exons can have varying lengths• ...probabilistic models:

Hidden Markov Models

Page 82: Orthology predictions for whole mammalian genomes

Accuracy of ab-initio gene prediction

• Nucleotide level: – 90% sensitivity/90% selectivity

• Exon level: – 70% sensitivity/50% selectivity

• Gene level: – 40% sensitivity/30% selectivity

• False positives: difficult to refute• False negatives: will be missed

Page 83: Orthology predictions for whole mammalian genomes

Limitations of ab-initio models

• Limited to training set• Limited to model (strange genes)• Problems with long genes• Small exons are difficult to find• Terminal exons are difficult to find

– No splice signals, other signals variable

• e.g. Genscan

Page 84: Orthology predictions for whole mammalian genomes

Comparative/homology methods

• Add extra data to locate genes• Compare genome to known sequences

– cDNAs– ESTs– Known protein sequences– e.g. Genewise

• Compare genome to other genome– e.g. TwinScan

Same or different organism}

Page 85: Orthology predictions for whole mammalian genomes

Using cDNAs/ESTs• cDNAs

Provide 3'UTR and 5'UTR. Provide full gene structure.– Expensive and thus rare– Contamination with genomic DNA

• ESTs Cheap and thus plentiful– Highly redundant– Of variable quality– Not complete

• Both: biased towards highly expressed genes

Page 86: Orthology predictions for whole mammalian genomes

Using cDNAs

5'UTR Exon Intron Exon Intron Exon 3'UTR

cDNA sequence

• Alignment between DNA sequences– Introns and reading frames

Page 87: Orthology predictions for whole mammalian genomes

Using known protein sequences

5'UTR Exon Intron Exon Intron Exon 3'UTR

Predicted protein sequence

Known protein sequence

Alignment between two protein sequences

Alignment between a “cDNA” to a genome

Implicit cDNA sequence

Page 88: Orthology predictions for whole mammalian genomes

Using another genome sequence

5'UTR Exon Intron Exon Intron Exon 3'UTR

5'UTR Exon Intron Exon Exon 3'UTRIntron

Genome 2

Genome 1

BLASTN resultsagainst Genome 2

Add evidence to ab-initio modele.g. TwinScan

Align gene models betweenorthologous regionse.g. DoubleScan

Page 89: Orthology predictions for whole mammalian genomes

Sweet spot for prediction by homology

Guigo et al. (2000)

Ab-Initio

Homology

Sensitivity

Similarity of known protein to target

Homology

Ab-Initio

Specificity

Page 90: Orthology predictions for whole mammalian genomes

Branches of gene trees scale symmetrically

Ideal world

Real world

Median distance to root

0.0 0.5 1.0 1.5 2.00.0

0.2

0.4

0.6

0.8

1.0

Cum

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freq

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Synonymous substitution rate / dS

dana dere dmel dsec dsim dyak

• Variations in branch length

Rate analyses

Page 91: Orthology predictions for whole mammalian genomes

Sequence conservation between mouse and human genesMouse genome paper Nature 420, 520-562

What orthologous genes should look like

Page 92: Orthology predictions for whole mammalian genomes

What orthologous genes should look like

• Exons conserved between genomes• UTRs partially conserved between genomes

CGSC (2004)

Page 93: Orthology predictions for whole mammalian genomes

Gene validations using orthology

• Most genes have orthologues• Almost all genes have mammalian homologs• Exaption of non-coding sequence is rare, especially

for constitutively expressed exons• Conservation of exon-intron structure (number

and phase of exons)• Conservation of length• Conservation of domains• Conservation of synteny

Page 94: Orthology predictions for whole mammalian genomes

Look carefully at genes• For example: small introns

Introns

Pseudogene?

Page 95: Orthology predictions for whole mammalian genomes

Conservation of splice sites:

• Insertions / losses of introns are rare• Phase Never changes• Aligned positions should nearly always match

allowing for alignment errors• Valid mismatches may represent insertions

(outside of protein domains)• Find retrogenes

Page 96: Orthology predictions for whole mammalian genomes

Conservation of splice sites:

• Tandem duplication of non-coding may result in the appearance of splice site conservation

• Check if sequence similarity is absolute

• Check coding potential(Tandem duplicates are often fast evolving genes under positive selection)

Page 97: Orthology predictions for whole mammalian genomes

Retrogenes

• Loss of introns is due to retrotransposition can be confirmed by loss of synteny (blastz)

• Not all retrogenes are non-functional• Ancient ones are functional• Recent retrogenes can be assumed to be

dead

Page 98: Orthology predictions for whole mammalian genomes

Gene validations using orthology

Page 99: Orthology predictions for whole mammalian genomes

Make sure orthology properties look appropriate

dN /dS 0.086dS 1.02

Amino acid sequence identity 81.0%Pairwise alignment coverage 94.2%

Homo sapiens Monodelphis domestica

Number of exons 9 9Sequence length (codons) 471 445Unspliced transcript length (bp) 27,241 25,365G+C content at 4D sites 56.9% 48.7%

Homo Monodelphis 1:1 orthologues

Page 100: Orthology predictions for whole mammalian genomes

What can you do with orthologs?

Wait for part II