1. The Human Genome Source Code
CS273A
The Human Genome Source Code
Gill Lecture 16: Comparative+Functional Genomics
TTh  1:30-2:50pm, mostly Always M106*
Prof: Gill Bejerano
CAs: Boyoung (Bo) Yoo & Yatish Turakhia
* Track class on Piazza
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2. Announcements
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3. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATAAAG
Genome Evolution
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4. Life’s Amazing Diversity – On Your Laptop Now
Mammals (250+)
Birds (130+)
Reptiles (35+)
Amphibians (5+)
Fish (200+)
Genome papers barely scratch the surface of the mysteries genomes holds
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5. What about a tree of related species?
What if we could find evolutionary patterns that were distinct enough to be phenotypically revealing?
Species A
Species B .
Genomes:
Inherited with Modifications.
Traits:
Come and Go.
ancestor
Species H
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6. The PG screen
Capture the independent genomic switch from purifying selection  neutral evolution in all and only the trait loss species.
Robust to: Different trait disabling times.
Different trait disabling mutations.
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7. Insertion in species 1 or
We quantify divergence by comparing sequences to the reconstructed ancestral sequence
Mutation in
species 1 or 2?
Insertion in species 1 or
deletion in species 2 ?
reconstruct
ancestral
sequence
species 1
ACCCTATCGATTGCA
TCCGTATCG-TT-CA
ACTCT-TCGATT-AA
species 2
outgroup
ancestor
ACCCTATCGATT-CA
species 1
ACCCTATCGATTGCA
TCCGTATCG-TT-CA
14 identical bases
species 2
11 identical bases
percent of identical bases:
species 1
93%
species 2
79%
 more diverged

8. We quantify the match to the vitamin C pattern by counting the number of species that violate the pattern
Percent identity
Percent identity
100
100     
1 violation
2 violations
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9. ABCB4 is a phospholipid transporter
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10. Find “Cure” Models for Human Disease
Human ABCB4 mutations lower patient biliary phospholipid levels to guinea pig levels but are detrimental.
Our discovery: Guinea pig and horse have inactivated the Abcb4 gene in their natural state. How can they do it?
create KO gene
Natural KO
try to fix/treat
find nature’s cure!
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11. What about gene regulation?
Capture the independent genomic switch from purifying selection  neutral evolution in all and only the trait loss species.
Robust to: Different trait disabling times.
Different trait disabling mutations.
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12. Combinatorial Regulatory Code
2,000 different proteins can bind specific DNA sequences.
DNA
Proteins
Protein binding site
Gene
DNA
A regulatory region encodes 3-10 such protein binding sites.
When all are bound by proteins the regulatory region turns “on”, and the nearby gene is activated to produce protein.
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13. ChIP-Seq: glimpses of the regulatory genome in action
Peak Calling
Cis-regulatory peak
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14. What is the transcription factor I just assayed doing?
Collect known literature of the form
Function A: Gene1, Gene2, Gene3, ...
Function B: Gene1, Gene2, Gene3, ...
Function C: ...
Ask whether the binding sites you discovered are preferentially binding (regulating) any one or more of the functions listed above.
Form hypothesis and perform further experiments.
Cis-regulatory peak
Gene transcription start site
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15. Example: inferring functions of Serum Response Factor (SRF) from its ChIP-seq binding profile
Gene transcription start site
SRF binding ChIP-seq peak
ChIP-seq identified 2,429 SRF binding peaks in human Jurkat cells1
SRF is known as a “master regulator of the actin cytoskeleton”
In the ChIP-Seq peaks, we expect to find binding sites regulating (genes involved in) actin cytoskeleton formation.
Jurkat (Human T cell lymphoblast-like cell line)
Description: serum response factor (c-fos serum response RefSeq Summary (NM_003131): This gene encodes a ubiquitous nuclear protein that stimulates both cell proliferation and differentiation. It is a member of the MADS (MCM1, Agamous, Deficiens, and SRF) box superfamily of transcription factors. This protein binds to the serum response element (SRE) in the promoter region of target genes. This protein regulates the activity of many immediate-early genes, for example c-fos, and thereby participates in cell cycle regulation, apoptosis, cell growth, and cell differentiation. This gene is the downstream target of many pathways; for example, the mitogen-activated protein kinase pathway (MAPK) that acts through the ternary complex factors (TCFs). [provided by RefSeq].
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16. Example: inferring functions of Serum Response Factor (SRF) from its ChIP-seq binding profile
Gene transcription start site
SRF binding ChIP-seq peak
Ontology term (e.g. ‘actin cytoskeleton’) π π π π
Existing, gene-based method to analyze enrichment:
Ignore distal binding events.
Count affected genes.
Rank by enrichment hypergeometric p-value.
N = 8 genes in genome
K = 3 genes annotated with
n = 2 genes selected by proximal peaks
k = 1 selected gene annotated with π π π π
P = Pr(k ≥1 | n=2, K =3, N=8) π π
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17. Pro: A lot of tools out there for the analysis of gene lists.
We have (reduced ChIP-Seq into) a gene list! What is the gene list enriched for?
Pro: A lot of tools out there for the analysis of gene lists.
Cons: These tools are built for microarray analysis.
Does it matter ??
Microarray data
Microarray data
Gene regulation data
Microarray tool
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18. SRF Gene-based enrichment results
Original authors can only state: “basic cellular processes, particularly those related to gene expression” are enriched1
SRF acts on genes both in nucleus and cytoplasm, that are involved in transcription and various types of binding
SRF Z ~
SRF
Where’s the signal?
Top “actin” term is ranked #28 in the list. ~
[1] Valouev A. et al., Nat. Methods, 2008
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19. Associating only proximal peaks loses a lot of information
Relationship of binding peaks to nearest genes for eight human (H) and mouse (M) ChIP-seq datasets
Restricting to proximal peaks often leads to complete loss of key enrichments
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20. Bad Solution: Associating distal peaks brings in many false enrichmentsπ π π
Why bad? 14% of human genes tagged ‘multicellular organismal development’. But 33% of base pairs have such a gene nearest upstream/downstream.
SRF ChIP-seq set has >2,000 binding events.
Throw a random set of 2,000 regions at the genome. What do you get from a gene list analysis?
Term Bonferroni
corrected p-value
nervous system development x10-9
system development x10-9
anatomical structure development x10-8
multicellular organismal development 1x10-7
developmental process x10-6
Large “gene deserts” are often next to key developmental genes
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21. Real Solution: Do not convert to gene list
Real Solution: Do not convert to gene list. Analyze the set of genomic regions
Gene transcription start site
Ontology term ( ‘actin cytoskeleton’)
Gene regulatory domain
Genomic region (ChIP-seq peak) π π π π π
GREAT = Genomic Regions Enrichment of Annotations Tool
p = 0.33 of genome annotated with π
n = 6 genomic regions
k = 5 genomic regions hit annotation
Fraction of genome resulting in annotation explicitly used in enrichment calculation
P = Prbinom(k ≥5 | n=6, p =0.33) π π π
Since 33% of base pairs are near a ‘multicellular organismal development’ gene, we now expect 33% of genomic regions to hit this term by chance. => Toss 2,000 random regions at genome, get NO (false) enrichments.
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22. How does GREAT know how to assign distal binding peaks to genes?
Future: High-throughput assays based on chromosome conformation capture (3C) methods will elucidate complex regulation mechanisms
Currently: Flexible computational definitions allow assignment of peaks to nearest gene, nearest two genes, etc.
Default: each gene has a “basal regulatory domain” of 5 kb up- and 1kb downstream of transcription start site, extends to basal domain of nearest genes within 1 Mb
Though some associations may be missed or incorrect, in general signal richness and robustness is greatly improved by associating distal peaks
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23. GREAT infers many specific functions of SRF from its binding profile
Top GREAT enrichments of SRF
Ontology Term # Genes Binomial Experimental
P-value support*
Top gene-based
enrichments of SRF
Gene Ontology
actin cytoskeleton
actin binding 30 31
7x10-9
5x10-5
Miano et al. 2007
Pathway
Commons
TRAIL signaling
Class I PI3K signaling 32 26
5x10-7
2x10-6
Bertolotto et al. 2000
Poser et al. 2000
TreeFam
FOS gene family 5
1x10-8
Chai & Tarnawski 2002
(top actin-related term 28th in list)
TF Targets
Targets of SRF
Targets of GABP
Targets of YY1
Targets of EGR1 84 28 44 23
5x10-76
4x10-9
1x10-6
2x10-4
Positive control
ChIp-Seq support
Natesan & Gilman 1995
SRF is “master regulator of the actin cytoskeleton.”
SRF is key regulator of FOS oncogene and has been shown to act in conjunction with YY1 to regulate FOS.
Demonstrated associations between SRF and TRAIL signaling.
SRF is needed for PI3K-dependent cell proliferation.
cFOS and FOSB are known targets of SRF.
* Known from literature – as in function is known, SOME of the genes are known, and the binding sites highlighted are NOT.
Similar results for GABP, NRSF, Stat3, p300 ChIP-Seq
[McLean et al., Nat Biotechnol., 2010]
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24. Advantages of the GREAT approach
Tailored to the biology of gene regulation:
Distal sites are incorporated, not ignored
Variable length gene regulatory domains
Multiple bindings next to same target gene rewarded
Binding sites associated to (both) TSS, not gene body
Extensive ontologies, some home-made
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25. Transcription Factor Function Prediction
1. Curate a rich library of TF binding site motifs. Pro: hundreds are now known, from SELEX, PBM, ChIP-seq.
2. Predict cross-species conserved binding sites. Pro: prediction is more accurate, sites are likely functional.
3. Search for extreme binding site concentration next to genes of any particular function. Pro: Leverage the observed phenomenon, and the very large body of knowledge about all (target) genes in the genome.
Con: You will not get all binding sites of any function.
Pro: You may predict many diverse TF functions.
Predict SRF to regulate:
Actin cytoskeleton,
Muscle development, ...
SRF
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26. 2. Predict conserved binding sites
. = same as human
Human
Human
Chimp
Gorilla
Orangutan
Rhesus
Tarsier
Mouse lemur
Bushbaby
Tree shrew
Mouse
Guinea pig
Squirrel
Rabbit
Alpaca
Cow
Cat
Microbat
Megabat
Hedgehog
Rock hyrax
Tenrec
Armadillo
Sloth
TTTCCCTTAAAAGGCTTAAATAAACTCACCAGTGTTTAATT
...T
...T G G......T...
...T C AT..TG.....C....
...T C....AT...G.....C....
...T TG
...T C...TG.....G.G..
...T TG CG
...T T...G......G...
...T TTG
...T GAC A
...T C
...T T.-.CA
...T G
TTTCCCTTAAAAGGCTTAAATAAACTCACCAGTGTTTAATT
We in fact allow:
Imperfect motif matches
Binding site / alignment wobble
Subset of species support
Guard against alignment fragmentations
Predict efficiently
Improve state of the art using “Excess conservation” scoring
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27. Compare to shuffled motifs & weed aggressively
SRF motif
shuffle #1
shuffle #2
shuffle #3
shuffle #4 …
shuffle #10
[Wenger et al, Genome Research, 2013]
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28. 3. Predict binding site/TF functions
MYOG
BMP4
MYF6
NKX2-5
gene transcription start site
SRF binding site
CAV3
ACTA1
Enhancer to gene association
 SRF must regulate muscle structure development: predicted to bind next to 157 genes (p=7.43×10-41)
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29. PRISM vs. ChIP-seq  GREAT
Actin cytoskeleton
SRF
T cell
Term PRISM ChIP-seq
actin cytoskeleton Known
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30. PRISM vs. ChIP-seq  GREAT
Actin cytoskeleton
SRF
T cell
Term PRISM ChIP-seq
actin cytoskeleton Known
structural constituent of muscle Known
dilated heart ventricles Known
regulation of insulin secretion Novel
muscle
SRF
heart
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31. Independently Eroded Binding Sites
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32. Independently Eroded Binding Sites
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33. Independently Eroded Binding Sites
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34. Independently Eroded Binding Sites
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35. Independently Eroded Binding Sites
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36. Independently Eroded Binding Sites
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37. Convergent phenotypic evolution
Trait
✔ Convergent lineages
New trait evolves ✔
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38. Could Collect Individual Convergent Amino Acids
More exciting to study a pathway/group of genes than it is to study individual ones.
Even more exciting (including journal-wise) if you spin a second angle. For example, ask:
1. Did convergent molecular evolution contribute much to convergent phenotypic evolution?
2. Could highly constrained amino acids in oft pleiotropic genes have contributed?
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39. Don't Just Collect Individual Events
Best if you can resolve some controversy.
For example: echolocating bats and whales share 770 convergent substitutions of otherwise highly conserved amino acids.
But all three trees, where one or both echolocating groups is swapped for its non-echolocating sister group, also present 700+ convergent amino acids.
So? Is there molecular convergence or not?
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40. 1. Identify all conserved amino acids
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41. 2. Identify subset of convergent amino acids
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42. 3. Ask: which group of genes most affected?
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43. 4. Is group of genes most affected relevant?
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44. 5. Rejoice!
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45. The Cochlea Human Rhesus Mouse Dolphin Killer whale Cow Dog
Black flying fox
Megabat
David’s myotis
Microbat
Big brown bat
Armadillo
Echolocation independently evolves
Max. hearing frequency
Dolphin
~100 kHz
Bat
Human
~20 kHz
Dog
~40 kHz
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46. Powerful Method
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47. 20+ amino acids per function/story
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48. Very Appealing Testable Targets
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49. One of the amino acids found
Prestin (SLC26A5)
Liu Z. et al., Mol. Biol. Evol., 2014
Winter H. et al., J. Cell Sci. 2006
N7T
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50. Summary Listen to the Genome See the blueprint
So much to discover in animal genomes
So many methods to develop
So relevant to human health
Element type x mutation type x Species/trait x loss/gain
Mammals, birds, fish (,fly, worm, ..)
Listen to the Genome
See the blueprint
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