1. Comparative genomics in flies and mammals
Manolis Kellis
Broad Institute of MIT and Harvard
MIT Computer Science & Artificial Intelligence Laboratory

2. Resolving power in mammals, flies, fungi
8 Candida
9 Yeasts
Post-duplication
Diploid
Haploid
Pre-dup P
Many species lead to high resolving power in close distances

3. Comparative genomics and evolutionary signatures
Comparative genomics can reveal functional elements
For example: exons are deeply conserved to mouse, chicken, fish
Many other elements are also strongly conserved: exons / regulatory?
Can we also pinpoint specific functions of each region? Yes!
Patterns of change distinguish different types of functional elements
Specific function  Selective pressures  Patterns of mutation/inse/del
Develop evolutionary signatures characteristic of each function

4. 1. Evolutionary signature of protein-coding genes
Revise protein-coding gene catalogue

5. Protein-coding evolution vs. nucleotide conservation
High protein-coding signal, low conservation
 Evolutionary signatures highly sensitive
High conservation, but not protein-coding
 Evolutionary signatures highly specific
Annotated FlyBase gene
Existing cDNA data
New predicted exon
cDNA validation (iPCR)

6. 2. Evolutionary signatures of RNA genes
Typical substitutions
Compensatory changes
G:C  G:U … G:U  A:U
Prediction methodology
Jakob Pedersen: EvoFold with very stringent parameters

7. Reveal novel RNA genes and structures
Intronic: enriched in A-to-I editing, also novel ncRNAs
Coding: A-to-I editing, also translational regulation
3’UTRs: enriched in regulators of mRNA localization
5’UTRs: translational regulation, ribosomal proteins
- 3’ & 5’UTR structures mostly on coding strand (75% & 80%)

8. 3. Structural and evolutionary signatures of miRNAs
Discover novel miRNAs
Recognize miRNA hairpin
Length of hairpin & length of arms
Fold stability, symm/assym bulges
Conservation profile: high|low|high
Pinpoint mature miRNA 5’end
Perfect 8mer conservation at start
Predominance of 5’U (78%)
Number of paired bases is bound
Complementary to 3’UTR motifs
Revise existing miRNAs

9. 4. Evolutionary signatures for regulatory motifs
Known
engrailed
site
(footprint)
D.mel CAGCT--AGCC-AACTCTCTAATTAGCGACTAAGTC-CAAGTC
D.sim CAGCT--AGCC-AACTCTCTAATTAGCGACTAAGTC-CAAGTC
D.sec CAGCT--AGCC-AACTCTCTAATTAGCGACTAAGTC-CAAGTC
D.yak CAGC--TAGCC-AACTCTCTAATTAGCGACTAAGTC-CAAGTC
D.ere CAGCGGTCGCCAAACTCTCTAATTAGCGACCAAGTC-CAAGTC
D.ana CACTAGTTCCTAGGCACTCTAATTAGCAAGTTAGTCTCTAGAG
** * * *********** * **** * **
D.mel
D. ere
D. ana
D. pse.
Motifs discovered
- Recover known regulators
- Many novel motifs
Evidence for novel motifs
Tissue-specific enrichment
Functional enrichment
In promoters & enhancers
Surprises
Core promoter elements
miRNA motifs in coding ex.
To address this question, we first studied the conservation properties of known regulatory motifs.
Here, you can see the alignment of the promoter of Gabpa gene across the four mammalian species used.
The alignment of this region reveals a small conservation island which seems to be under selection. This island in fact corresponds to a functional binding site for Erra, which has been experimentally validated.
The ERRa motif appears perfectly conserved in the four species and stands out from the largely diverged neighboring sequences.
However, is this enough to discover regulatory motifs? The answer is clearly no, since many many such small islands exist, and the vast majority of them are likely to be solely due to chance.

10. Functions of discovered motifs
Positional biases
Tissue-specific enrichment and clustering
miRNA targeting in coding regions

11. 5. Evolutionary signatures of motif instances
Allow for motif movements
Sequencing/alignment errors
Loss, movement, divergence
Measure branch-length score
Sum evidence along branches
Close species little contribution
BLS: 25%
Mef2:YTAWWWWTAR
BLS: 83%

12. Motif confidence selects functional instances
Transcription factor motifs
Confidence
Confidence
Increasing BLS  Increasing confidence
Confidence selects functional regions
Confidence selects in vivo bound sites
High sensitivity
microRNA motifs
Increasing BLS  Increasing confidence
Confidence selects functional regions
Confidence selects positive strand

13. 6. Initial regulatory network for an animal genome
ChIP-grade quality
Similar functional enrichment
High sens. High spec.
Systems-level
81% of Transc. Factors
86% of microRNAs
8k + 2k targets
46k connections
Lessons learned
Pre- and post- are correlated (hihi/lolo)
Regulators are heavily targeted, feedback loop

14. Network captures literature-supported connections

15. Network captures co-expression supported edges
Red = co-expressed
Grey = not co-expressed
Named = literature-supported
Bold = literature-supported

16. 7. ChIP vs. conservation: similar power / complementary
Together: best
 complementary
Bound but not conserved: reduced enrich.
 Selects functional
All-ChIP vs. All-cons: similar enr.
 Similar power
Cons-only vs. ChIP-all: similar
 Additional sites

17. Recovery of regulatory motif instances in mammals
(80% confidence)
~6X
11,000 instances
10k 8k 6k
miRNA motif instances recovered (80%)
Total branch length of inf. species 4k 2k
HMRD
(0.74)
pl-mam
(3.36)
mamm.
(4.33)
H+non-mamm.
(6.36)
HMRD+ non-mam
(6.96)
All vertebr.
(9.66)
~6X
Performance increases with branch length (requires closely-related species)
Measure number of recovered motif instances at a fixed confidence (80%) / FDR (20%)
Discovery power: 6-fold higher than HMRD (Branch length also ~6-fold higher)
With 20 currently-aligned mammals:
Transcription factor motifs: 47 TFs | 16,000 instances | 340 targets on avg
microRNA motifs 21 miRNAs | 11,000 instances | 523 targets on avg
An initial regulatory network for mammalian genomes

18. New insights into animal biology

19. 1.Large-scale evidence of translational read-through
Protein-coding
conservation
Continued protein-coding
conservation
No more
conservation
Stop codon
read through
2nd stop
codon
New mechanism of post-transcriptional control.
Hundreds of fly genes, handful of human genes.
Enriched in brain proteins, ion channels.
Experiments show ADAR necessary & sufficient (Reenan Lab).
Many questions remain
A-to-I editing of stop codon TAG|TGA|TAA  TGG
Cryptic splice sites? RNA secondary structure?

20. 2. Stop codon read-through in mammals
Four candidates found: GPX2, OPRK1, OPRL1, GRIA2, mostly neuronal
A look at FOXP2 – Possible 3’UTR function (not in fish, yes in frog)

21. 3. New insights into miRNA regulation: miRNA* function
Both miRNA arms can be functional
High scores, abundant processing, conserved targets
Hox miRNAs miR-10 and miR-iab-4 as master Hox regulators

22. 4. New insights into miRNA regulation: miR-AS function
A single miRNA locus transcribed from both strands
Both processed to mature miRNAs: mir-iab-4, miR-iab-4AS (anti-sense)
The two miRNAs show distinct expression domains (mutually exclusive)
The two show distinct Hox targets – another Hox master regulator

23. 5. New insights into miRNA regulation: miR-AS function
wing
w/bristles
Sensory bristles
haltere
wing
haltere WT
Note: C,D,E same magnification
wing
sense
Antisense
Mis-expression of mir-iab-4S & AS: altereswings homeotic transform.
Stronger phenotype for AS miRNA
Sense/anti-sense pairs as general building blocks for miRNA regulation
9 new anti-sense miRNAs in mouse

24. Summary of Contributions
Evolutionary signatures specific to each function
Protein-coding genes: Revised catalogue affects 10% of genes
RNA: hundreds of new high-confidence structures discovered
miRNAs: ~double number of genes, families, targeting density
Motifs: ~double number of motifs, tissue & positional enrichment
Targets: ChIP-grade quality, global scale, experimental support
New insights on animal biology
Genes: Abundant stop codon read-through in neuronal proteins
RNA: Abundant structures in RNA editing, translational regulation
Motifs: Coding regions show miRNA targeting
miRNAs: miR/miR* and sense/anti-sense pairs: building blocks
Networks: TF vs. miRNA targets redundancy and integration
Methods are general, applicable in any species

25. Next steps: Drosophila and Human ENCODE
modENCODE: White / Ren / Kellis / Posakony
Hundreds of sequence-specific factors
Dozens of chromatin / histone modifications
Dozens of tissues / stages / conditions
humENCODE: Bernstein / Lander / Kellis / Broad
ChIP-seq for dozens of chromatin modifications
Follow differentiation lineages – activation inactivation
Discover tissue-specific regulatory motifs
Many open questions remain
Dynamics of tissue-specific regulatory networks
Sequence determinants of chromatin establ. & maint
Global views of pre- & post-transcriptional regulation
Many open positions remain (postdoc/grad/ugrad)

26. Acknowledgements Alex Stark Mike Lin Pouya Kheradpour Matt Rasmussen
Genes FlyBase, BDGP, Bill Gelbart, Sue Celniker, Lynn Crosby
miRNAs Leo Parts, Julius Brennecke, Greg Hannon, David Bartel
iab-4AS Natascha Bushati, Steve Cohen, Julius, Greg Hannon
12-flies Andy Clark, Mike Eisen, Bill Gelbart, Doug Smith
24 mammals Sante Gnerre, Michele Clamp, Manuel Garber, Eric Lander