1. Interpreting the human genome
Manolis Kellis
Broad Institute of MIT and Harvard
MIT Computer Science & Artificial Intelligence Laboratory

2. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGAGAAAAATCCATCCATTACCTTAATAAATGCTGATCCCAAATTTGCTCAAAGGAAGTTCGATTTGCCGTTGGACGGTTCTTATGTCACAATTGATCCTTCTGTGTCGGACTGGTCTAATTACTTTAAATGTGGTCTCCATGTTGCTCACTCTTTTCTAAAGAAACTTGCACCGGAAAGGTTTGCCAGTGCTCCTCTGGCCGGGCTGCAAGTCTTCTGTGAGGGTGATGTACCAACTGGCAGTGGATTGTCTTCTTCGGCCGCATTCATTTGTGCCGTTGCTTTAGCTGTTGTTAAAGCGAATATGGGCCCTGGTTATCATATGTCCAAGCAAAATTTAATGCGTATTACGGTCGTTGCAGAACATTATGTTGGTGTTAACAATGGCGGTATGGATCAGGCTGCCTCTGTTTGCGGTGAGGAAGATCATGCTCTATACGTTGAGTTCAAACCGCAGTTGAAGGCTACTCCGTTTAAATTTCCGCAATTAAAAAACCATGAAATTAGCTTTGTTATTGCGAACACCCTTGTTGTATCTAACAAGTTTGAAACCGCCCCAACCAACTATAATTTAAGAGTGGTAGAAGTCACTACAGCTGCAAATGTTTTAGCTGCCACGTACGGTGTTGTTTTACTTTCTGGAAAAGAAGGATCGAGCACGAATAAAGGTAATCTAAGAGATTTCATGAACGTTTATTATGCCAGATATCACAACATTTCCACACCCTGGAACGGCGATATTGAATCCGGCATCGAACGGTTAACAAAGATGCTAGTACTAGTTGAAGAGTCTCTCGCCAATAAGAAACAGGGCTTTAGTGTTGACGATGTCGCACAATCCTTGAATTGTTCTCGCGAAGAATTCACAAGAGACTACTTAACAACATCTCCAGTGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTATAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAAAAAGAGATTGCCGTCTTGAAACTTTTTGTCCTTTTTTTTTTCCGGGGACTCTACGAGAACCCTTTGTCCTACTGATTAATTTTGTACTGAATTTGGACAATTCAGATTTTAGTAGACAAGCGCGAGGAGGAAAAGAAATGACAGAAAAATTCCGATGGACAAGAAGATAGGAAAAAAAAAAAGCTTTCACCGATTTCCTAGACCGGAAAAAAGTCGTATGACATCAGAATGAAAAATTTTCAAGTTAGACAAGGACAAAATCAGGACAAATTGTAAAGATATAATAAACTATTTGATTCAGCGCCAATTTGCCCTTTTCCATTTTCCATTAAATCTCTGTTCTCTCTTACTTATATGATGATTAGGTATCATCTGTATAAAACTCCTTTCTTAATTTCACTCTAAAGCATACCCCATAGAGAAGATCTTTCGGTTCGAAGACATTCCTACGCATAATAAGAATAGGAGGGAATAATGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAA
1. The structure of DNA was solved exactly 50 years ago today. However, it is only
in the last 5-10 years that technological advances have enabled the sequencing of complete
genomes, including our own.
2. Super. We basically read 3 billion letters of A, C, G, T. 6 billion bits of uncommented
lines of machine code, all zeros and ones, probably the biggest computer program ever
written outside Seattle. Without the subsequent analysis, this sequence would be useless.
3. This is a new time in science, where we have more data than we know what to do with.
And it’s all digital information. It is now POSSIBLE to apply computational methods
to understand biology. In fact, it is now IMPERATIVE to apply computational methods
to understand biology.
Without the subsequent analysis, this information is just uncommented assembly code.
How do we interpret 2

3. Genes Regulatory motifs Encode proteins Control gene expression
TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGAGAAAAATCCATCCATTACCTTAATAAATGCTGATCCCAAATTTGCTCAAAGGAAGTTCGATTTGCCGTTGGACGGTTCTTATGTCACAATTGATCCTTCTGTGTCGGACTGGTCTAATTACTTTAAATGTGGTCTCCATGTTGCTCACTCTTTTCTAAAGAAACTTGCACCGGAAAGGTTTGCCAGTGCTCCTCTGGCCGGGCTGCAAGTCTTCTGTGAGGGTGATGTACCAACTGGCAGTGGATTGTCTTCTTCGGCCGCATTCATTTGTGCCGTTGCTTTAGCTGTTGTTAAAGCGAATATGGGCCCTGGTTATCATATGTCCAAGCAAAATTTAATGCGTATTACGGTCGTTGCAGAACATTATGTTGGTGTTAACAATGGCGGTATGGATCAGGCTGCCTCTGTTTGCGGTGAGGAAGATCATGCTCTATACGTTGAGTTCAAACCGCAGTTGAAGGCTACTCCGTTTAAATTTCCGCAATTAAAAAACCATGAAATTAGCTTTGTTATTGCGAACACCCTTGTTGTATCTAACAAGTTTGAAACCGCCCCAACCAACTATAATTTAAGAGTGGTAGAAGTCACTACAGCTGCAAATGTTTTAGCTGCCACGTACGGTGTTGTTTTACTTTCTGGAAAAGAAGGATCGAGCACGAATAAAGGTAATCTAAGAGATTTCATGAACGTTTATTATGCCAGATATCACAACATTTCCACACCCTGGAACGGCGATATTGAATCCGGCATCGAACGGTTAACAAAGATGCTAGTACTAGTTGAAGAGTCTCTCGCCAATAAGAAACAGGGCTTTAGTGTTGACGATGTCGCACAATCCTTGAATTGTTCTCGCGAAGAATTCACAAGAGACTACTTAACAACATCTCCAGTGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTATAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAAAAAGAGATTGCCGTCTTGAAACTTTTTGTCCTTTTTTTTTTCCGGGGACTCTACGAGAACCCTTTGTCCTACTGATTAATTTTGTACTGAATTTGGACAATTCAGATTTTAGTAGACAAGCGCGAGGAGGAAAAGAAATGACAGAAAAATTCCGATGGACAAGAAGATAGGAAAAAAAAAAAGCTTTCACCGATTTCCTAGACCGGAAAAAAGTCGTATGACATCAGAATGAAAAATTTTCAAGTTAGACAAGGACAAAATCAGGACAAATTGTAAAGATATAATAAACTATTTGATTCAGCGCCAATTTGCCCTTTTCCATTTTCCATTAAATCTCTGTTCTCTCTTACTTATATGATGATTAGGTATCATCTGTATAAAACTCCTTTCTTAATTTCACTCTAAAGCATACCCCATAGAGAAGATCTTTCGGTTCGAAGACATTCCTACGCATAATAAGAATAGGAGGGAATAATGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAA
Regulatory motifs
Control
gene expression
Genes
Encode
proteins

4. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGAGAAAAATCCATCCATTACCTTAATAAATGCTGATCCCAAATTTGCTCAAAGGAAGTTCGATTTGCCGTTGGACGGTTCTTATGTCACAATTGATCCTTCTGTGTCGGACTGGTCTAATTACTTTAAATGTGGTCTCCATGTTGCTCACTCTTTTCTAAAGAAACTTGCACCGGAAAGGTTTGCCAGTGCTCCTCTGGCCGGGCTGCAAGTCTTCTGTGAGGGTGATGTACCAACTGGCAGTGGATTGTCTTCTTCGGCCGCATTCATTTGTGCCGTTGCTTTAGCTGTTGTTAAAGCGAATATGGGCCCTGGTTATCATATGTCCAAGCAAAATTTAATGCGTATTACGGTCGTTGCAGAACATTATGTTGGTGTTAACAATGGCGGTATGGATCAGGCTGCCTCTGTTTGCGGTGAGGAAGATCATGCTCTATACGTTGAGTTCAAACCGCAGTTGAAGGCTACTCCGTTTAAATTTCCGCAATTAAAAAACCATGAAATTAGCTTTGTTATTGCGAACACCCTTGTTGTATCTAACAAGTTTGAAACCGCCCCAACCAACTATAATTTAAGAGTGGTAGAAGTCACTACAGCTGCAAATGTTTTAGCTGCCACGTACGGTGTTGTTTTACTTTCTGGAAAAGAAGGATCGAGCACGAATAAAGGTAATCTAAGAGATTTCATGAACGTTTATTATGCCAGATATCACAACATTTCCACACCCTGGAACGGCGATATTGAATCCGGCATCGAACGGTTAACAAAGATGCTAGTACTAGTTGAAGAGTCTCTCGCCAATAAGAAACAGGGCTTTAGTGTTGACGATGTCGCACAATCCTTGAATTGTTCTCGCGAAGAATTCACAAGAGACTACTTAACAACATCTCCAGTGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTATAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAAAAAGAGATTGCCGTCTTGAAACTTTTTGTCCTTTTTTTTTTCCGGGGACTCTACGAGAACCCTTTGTCCTACTGATTAATTTTGTACTGAATTTGGACAATTCAGATTTTAGTAGACAAGCGCGAGGAGGAAAAGAAATGACAGAAAAATTCCGATGGACAAGAAGATAGGAAAAAAAAAAAGCTTTCACCGATTTCCTAGACCGGAAAAAAGTCGTATGACATCAGAATGAAAAATTTTCAAGTTAGACAAGGACAAAATCAGGACAAATTGTAAAGATATAATAAACTATTTGATTCAGCGCCAATTTGCCCTTTTCCATTTTCCATTAAATCTCTGTTCTCTCTTACTTATATGATGATTAGGTATCATCTGTATAAAACTCCTTTCTTAATTTCACTCTAAAGCATACCCCATAGAGAAGATCTTTCGGTTCGAAGACATTCCTACGCATAATAAGAATAGGAGGGAATAATGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAA

5. Large-scale comparative genomics datasets
32 mammals
12 flies
17 fungi
8 Candida
9 Yeasts
Post-duplication
Diploid
Haploid
Pre-dup P N

6. Evolution Genomics Comparative Genomics
Using evolution to study genomes
Evolution
Genomics
Using genomics to study evolution

7. Computational challenges in biology
1. Genomic interpretation
Decode the human genome
Discovery all functional elements
 The building blocks
2. Cell circuitry
Discover all control constructs
Regulatory network properties
 The interconnections
3. Evolutionary innovation
Emergence of new functions
Genome duplication
 The dynamics

8. Evolutionary signatures for diverse functions
Protein-coding genes
- Codon Substitution Frequencies
- Reading Frame Conservation
RNA structures
- Compensatory changes
- Silent G-U substitutions
microRNAs
- Shape of conservation profile
- Structural features: loops, pairs
- Relationship with 3’UTR motifs
We have developed comparative genomics methods to systematically interpret complete genomes.
[top graph] Comparative genomics can help pinpoint functional regions, based on their increased conservation. However, high conservation alone does not reveal the specific functions of these elements.
[bottom figure with four panels] We showed that when you look more closely within conserved elements, you can distinguish their specific functions, based on characteristic patterns of nucleotide mutation associated with each function, which we call “evolutionary signatures”. We developed such signatures for the systematic annotation of protein-coding genes, RNA structures, microRNAs, regulatory motifs, and individual motif instances.
[Animation] We applied them genome-wide in yeast, fly, and human, to discover new genes, refine and extend known genes, and recognize mistakes in the existing annotations.
[Animation] Using these signatures we were able to also find genes that defied the traditional rules of protein-coding translation. For example, 150 genes, strongly enriched in neuronal functions, showed protein-coding translation well-past a conserved stop codon, suggesting abundant stop-codon read-through in the adult brain.
[Animation] We found abundant novel RNA structures strongly associated with RNA editing, mRNA localization, and new structures suggestive of translational regulation.
[Animation] Lastly, We were able to discover many novel microRNA genes in the human and fly genome, and to refine the annotation of existing microRNAs, resulting in drastic changes to their target spectrum.
Overall, the method of evolutionary signatures has proved extremely powerful in yeast, fly, and human, and has resulted in many new insights about gene usage and gene regulation. The methods are general, and should be applicable in any species.
Regulatory motifs
- Mutations preserve consensus
- Increased Branch Length Score
- Genome-wide conservation
Stark et al, Nature 2007
Lin et al, Genome Research 2007 8

9. Surprise 1: translational read-through in the brain
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.
Initial experiments show potential ADAR role (Reenan Lab).
Many questions remain
A-to-I editing of stop codon TAG|TGA|TAA  TGG
Cryptic splice sites? RNA secondary structure?
Lin et al, Genome Research 2007

10. Surprise 2: MicroRNAs and developmental control
Illustrates miR/miR* and miR/miR-AS cooperation

11. miR-iab-4AS leads to homeotic transformations
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
Stark et al, Genes&Development 2007

12. Computational challenges in biology
1. Genomic interpretation
Decode the human genome
Discovery all functional elements
 The building blocks
2. Cell circuitry
Discover all control constructs
Regulatory network properties
 The interconnections
3. Evolutionary innovation
Emergence of new functions
Genome duplication
 The dynamics

13. 3: Sequence determinants of TF binding
Hundreds of proteins bind overlapping regions
Regulatory motif analysis reveals sequence specificity
Basis for understanding motif combinations & grammars
CTCF, check
GAF, check
Example: insulator proteins in Drosophila
Su(Hw), check
BEAF-32, variant
CP190, novel
Although insulator-bound regions overlap,
each motif is specific to exactly one protein
Mod(mdg4), novel

14. 4: Regulatory network inference
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
Sushmita Roy
Kheradpour et al, Genome Research, 2007

15. Chromatin signatures for genome annotation
Similarly to evolutionary signatures:
chromatin signatures encode functional elements
The difference
this information is dynamic
The epigenetic code hypothesis
Distinct combinations of marks encode distinct chromatin states
Can we discover them de novo

16. A multivariate HMM for understanding chromatin state
Transcription Start Site
Enhancer
Transcribed Region
DNA
Observed
Histone Modifications
Most likely Hidden State 2 4 5 5 6 1 3 5 5 5 6 6
Highly Likely Modifications in State
Even though modification was not observed can still infer correct state based on neighboring locations that this state is likely of the same type as its neighboring states
0.8 1:
0.8 4:
0.7 2:
0.9 5:
0.9 3:
0.9
0.8 6: 16

18. Combine chromatin signatures and evolutionary signatures  New class of large non-coding RNAs (lincRNAs) in human
H3K4me3 - K3K36me3
Our experiments confirm:
These regions produce RNA molecules
They have exon/intron structures
They are evolutionarily conserved
They show no coding potential, no evo. sign.
Their promoters and regulation are conserved
They play diverse roles in chromatin regulation
Mikkelsen et al. 2007
Guttman et al. Nature, March 12, 2009 (online already)

19. Combine chromatin signatures and regulatory motifs  New developmental enhancers in human and fly
Visel, Penacchio, Rubin, Ren, Nature 2008
Zeitlinger et al, Genes & Development 2007
Chromatin signatures and evolutionary signature are predictive of enhancer elements
Experimental techniques developed for inferring expression domains in human and fly
Large-scale databases mapping every elements to its expression pattern emerge
Ability to test new patterns and artificial elements in fly / mouse embryos

20. The grand challenge ahead
Binding sites of every
developmental regulator
Sequence motifs for
every regulator
Annotations & images for all expression patterns
GAF, check
Su(Hw), check
BEAF-32, variant
Mod(mdg4), novel
CP190, novel
CTCF, check
Dorsal-Ventral
Expression domain primitives reveal underlying logic
Anterior-Posterior
Understand regulatory logic specifying development

21. Going forward: Drosophila and Human ENCODE
Kevin White, Bing Ren, Jim Posakony
Hundreds of sequence-specific factors
Dozens of chromatin / histone modifications
Dozens of tissues / stages / conditions
Bernstein, Lander, Broad Institute
ChIP-seq for dozens of chromatin modifications
Follow differentiation lineages – activation inactivation
Discover tissue-specific regulatory motifs
Many open questions remain:
Regulatory logic and architecture within enhancer regions.
Sequence determinants of enhancer/promoter specificity.
Motif-based prediction of expression domains of enhancer elements.
Motif-based design of novel enhancers and developmental patterns.

22. Computational challenges in biology
1. Genomic interpretation
Decode the human genome
Discovery all functional elements
 The building blocks
2. Cell circuitry
Discover all control constructs
Regulatory network properties
 The interconnections
3. Evolutionary innovation
Emergence of new functions
Genome duplication
 The dynamics

23. Alignment of closely related species
S.cerevisiae
S.paradoxus
S.mikatae
S.bayanus
When we take a closer look the local gene order is conserved across all four genomes.
Easy to argue orthology in presence of synteny and whole-genome disambiguation.
Moreover we can distinguish coding and non-coding regions (based on common signals and type of conservation) and analyze these separately.
Hence we can align every intergenic region unambiguously based on flanking genes.
(Maybe a quick mention of transposable elements tRNA rRNA and their mobility or lack thereof)
All genes are present, same order + orientation

24. Distant species: Evolutionary mysteries
K. waltii
K. waltii
K. waltii
Few genes remain in 2 copies
Intro (previous slide). Now let’s look closely at the correspondence of these three regions,
The one copy from K. yarrowii, and the two copies from S. cerevisiae.
We observe a very intriguing pattern of gene correspondence.
Two
The indirect evidence from the duplicated genes is weak, since nearly 90% of duplicated
Genes were lost, but the mapping becomes unambiguous given the full gene interleaving.
When we look closely at the gene correspondence within t
Look closely at these regiones. They are both orthologous.
Gene order is conserved across the entire segment for both copies in S. cerevisiae .
In fact, we see an interesting phenomenon, that each of the regions only contains
A subset of the k. yarrowii genes, while their union contains the complete gene set.
S. cerevisiae regions together account for all K. yarrowii genes
Each K. yarrowii gene is present in one sister or the other (and sometimes both)
K. yarrowii genes account for both S. cerevisiae regions
S. cerevisiae genes from both sisters present in K. yarrowii
Gene order and transcriptional orientation conserved
Gene interleaving is evidence of complete duplication

25. Evolution by whole-genome duplication
Ancestral gene order
Duplicated lineage
Single-copy
Duplication
Gene loss
100 Myrs
Yeast Duplication
Kellis et al. Nature, Apr 8, 2004
Vertebrate Duplication
Jaillon et al. Nature, Oct 21, 2004

26. Duplicate mapping over entire genome
K.waltii
Scer copy1
Scer copy2
Chr 1
S. cer.
Chr 2
Chr 3
Chr 4
K. waltii chromosomes
Chr 5
Chr 6
Chr 7
Chr 8

27. Duplicate mapping of chromosomes
Additionally, this figure also illustrates our ability to detect ancestrally duplicated regions
Even in absence of any remaining two-copy genes. This has allowed to complete the duplication map of S. cerevisiae.
Most duplicated genes are lost

28. Whole-genome duplication results in 500 new genes
Number
of genes
10,000
WGD
5,500
Gene
Loss
~500 gained
5,000
We can conclude that a WGD event has indeed occurred in the lineage of Saccharomyces cerevisiae,
and we have resolved the controversy regarding its ancestry.
time
100Myrs
Today
Emergence of new functions?

29. Emerging gene functions after duplication
Origin of replication  silencing
4-fold acceleration
Scer - Sir3 (silencing)
Scer - Orc1 (origin of replication)
Kwal - Orc1
Translation initiation  anti-viral defense
3-fold acceleration
Scer - Ski7 (anti-viral defense)
Scer - Hbs1 (translation initiation)
Kwal - Hbs1
Asymmetric divergence  recognize ancestral / derived

30. Distinct properties of emerging functions
Ancestral function
Derived function
Gene deletion
Lethal (20%)
Never lethal
Expression
Abundant
Specific
(stress, starvation)
Localization
General
(mitochondrion, spores)
Gain new function and lose ancestral function

31. Network evolution by duplication
Time
Modern Network
Pre-WGD
Lost
Duplicate
Network motif
Duplication
Loss - -
Ancestral
network motifs
Scenario 1
Duplication
Modern
network motif
Gain + +
Scenario 2

32. Phylogenomics Traditional phylogenetics focused on uniform trees
Traditionally
kept distinct
Many species
One gene
Many species
Many genes
One species
Many genes
Traditional phylogenetics focused on uniform trees
Any topology makes a good story
Phylogenomics imposes additional constraints
Gene trees evolve inside species trees
Independent measure of phylogenetic accuracy

33. Wide-spread errors in phylogenetic reconstruction
Phylogenies of 5154 syntenic 1:1 orthologs ?
Correct
phylogeny
Incorrect
Etc…
316 other
topologies
Incorrect

34. Errors are due to lack of informative sites
Typical gene length
species trees: Concatenate 20 genes
Recommended length for accurate
Reconstruction accuracy
37%
Sequence length
Due to lack of informative sites (not methodology)
Accuracy increases with gene length
‘Recommended length’ unrealistic for gene trees
Need additional information  study gene-trees systematically

35. Study recurring properties of phylogenetic treesA A A A A A A
Study properties of correct trees:
Input correct topology
Fit branch lengths
Study resulting tree properties
0.56 subs
0.8 subs
1.0 subs
1.26 subs
2.01 subs
Total:
Total tree length varies widely
Some genes are fast-evolving, some are slow-evolving
But, importantly: branches are uniformly longer or shorter
Branch lengths are highly correlated

36. Gene-trees are highly correlated
1.00
1.26
0.80
2.01
0.56
Average gene tree
Correlations found:
Fast genes are fast in all species
(gene-specific mutation rate)
Fast species are fast for all genes
(species-specific mutation rate)
Gene trees scaled versions of avg tree
Gene tree = F * average-tree
93% of gene trees
show R>.8 to avg tree
Scaling factor
for each gene
Common property
across all genes
(genome-wide)
0.6
0.8
1.0
correlation

37. The two forces of gene-family evolution
Mutation rate of gene j in species i = gene-rate * species rate
bij = Fj * Si
1. Family rate
2. Species-specific rates Fj Si
~gamma(a,b)
~normal(ui,si)
Selective pressures on gene function
Population dynamics of the species

38. Evaluation: Great increase in accuracy
Real data, syntenic regions:
Increasing number of species
Both 1-to-1 & duplication sets
 Great increase in accuracy
Simulated data:
Run (generative) model
1 dup event  many dup genes
Method robust to dup/loss

39. Computational challenges in biology
1. Genomic interpretation
Decode the human genome
Discovery all functional elements
 The building blocks
2. Cell circuitry
Discover all control constructs
Regulatory network properties
 The interconnections
3. Evolutionary innovation
Emergence of new functions
Genome duplication
 The dynamics

40. compbio.mit.edu where we are

41. compbio.mit.edu who we are
Jason
Rachel
Jessica
Rogerio
Sushmita
Rogerio
Manolis
Pouya
Abdoulaye
Chris
Loyal
Daniel
Mike
Matt