lecture1

CS173

MW 11:00-12:15 in Beckman B302

Prof: Gill Bejerano

TAs: Jim Notwell & Harendra Guturu http://cs173.stanford.edu [BejeranoWinter12/13] 1

Welcome back!

http://cs173.stanford.edu [BejeranoWinter12/13] 2

What will we study?

The most amazing “tape” in existence, your genome.


TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA

CATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC

AGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC

CGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT

AGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG

ATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA

AAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT

TGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA

TTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG

ATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT

TTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT

CTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG

AACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC

ATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA

AAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA

GCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAA

CTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGA

TAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTT

GGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAA

GTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAA

TGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGA

TACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT

TCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACAT

TTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAA

AGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAAT

ACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTAC

AACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATAT

CAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCG

TTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTC

TTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATT

AATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT

TCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG

TTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATA

CCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG

TTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTA

AGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGA

TTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATA

GTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATG

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TGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAAT

Why Genomics?

• This century is owned by Genomics.

• Growing impact on everybody’s life.

• Rewriting the book on our understanding of life.

• Genomics is an information/computational science.

• “There is gold in them thar hills”

• That gold can be yours .

http://cs173.stanford.edu [BejeranoWinter12/13]

5

Why Genomics?



• Make tantalizing discoveries with CS107 proficiency.

• Knowing more CS does make you more dangerous.


6

Genomics is different

Theoretical CS studies the hardness of questions.

A question is as hard as its easiest solution.

A lot of focus on how to answer.

• Order of importance in genomics:

• What to ask?

• Why ask it?

• How to answer?

• What does my answer mean?

Genomics is drowning in data.

Much to discover. Some fascinating, lots quite boring.

Focus on what & why to ask, and can we answer?


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Why Genomics?



• Make tantalizing discoveries with CS107 proficiency.

• Knowing more CS does make you more dangerous.

• Reject the “us” (CS) and “them” (BIO) dichotomy.

• Read what you need.

• Develop your own taste for questions and answers.


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Field Goals


Class Goals

• Meet your genome (learn to surf, learn the surf)

• Understand genomic tools (theory, applications)

• DIY (pose questions, use tools, write code, get answers) http://cs173.stanford.edu [BejeranoWinter12/13] 10

Materials

How many people need the BIO primer?

We’ll have three primers on:

Biology; Text Processing; and the UCSC Genome Browser.

Programming background recommended (CS107 level)

Homework (schedule on website):

Two individual homework assignments, plus a group project.

Instead of an exam we’ll have a project milestone and a final poster session.

Attendance is mandatory (for grade). You may skip 2 lectures without affecting your grade.

Get on the mailing list!

Reading Material: mostly journal papers http://cs173.stanford.edu [BejeranoWinter12/13] 11

Topics

(0) Genome context: cells, DNA, central dogma

(1) Genome content / genome function: coding and non-coding genes, gene regulation, repeats, epigenetics, signaling

(2) Genome sequencing: technologies, assembly/analysis, technology dependence

(3) Genome evolution: evolution = mutation + selection, modes of evolution, comparative genomics, ultraconservation, exaptation

(4) Genomics & disease: disease susceptibility, cancer genomics, treatment response, personal genomics, gene therapy

(5) Genome and “genome output” (organism) evolution

Evo devo, the great divide, bridges across the great divide http://cs173.stanford.edu [BejeranoWinter12/13] 12

Important in Genomics

• Understand deep dependence on technology.

• The only truth in genomics is…

• Technology dictates what can be asked.

• Technology dictates how it can be answered.

• Recent data deluge – the beauty, and the risks.

• Hypothesize, code, analyze, repeat until you find gold, or your sieve is empty.

• Analyze ensembles, learn from outliers.

• When you make an exciting discovery, first try hard to refute it.

• If it’s too good to be true, it is most likely not true.

• Seek the exciting invisible but plausible.


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Further Reading

These principles and tips will be revisited at course’s end.

At which point we will ask ourselves:

Are we any wiser?

Check out the Bejerano lab “resources” page:

• Popular science books

• Core Stanford classes

• Core technical books/skills

What if I get hooked?

Classes, rotations, CURIS, honors theses, etc. http://cs173.stanford.edu [BejeranoWinter12/13]

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Organism – Cell - Genome

10 13 different cells in an adult human.

The cell is the basic unit of life.

DNA = linear molecule inside the cell that carries instructions needed throughout the cell’s life ~ long string(s) over a small alphabet

Alphabet of four (nucleotides/bases) {A,C,G,T} Strings of length 10 4 -10 11

Genome:

“instruction”

...ACGTACGACTGACTAGCATCGACTACGACTAGCAC...


One Cell, One Genome, One Replication

•Every cell holds a copy of all its DNA = its genome.

•The human body is made of ~10 13 cells.

•All originate from a single cell through repeated cell divisions.

egg cell

DNA strings =

Chromosomes egg genome = all DNA cell division chicken ≈ 10 13 copies

(DNA) of egg (DNA) chicken egg http://cs173.stanford.edu [BejeranoWinter12/13] 17







































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Genomes, Genes & Proteins

The most visible instructions in our genome are Genes.

Genes explain exactly HOW to synthesize any protein.

Proteins are the work horses of every living cell.

Genome: gene


protein http://cs173.stanford.edu [BejeranoWinter12/13] cell

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Genes & Gene Regulation

• Human genome encodes 20-25,000 genes (2% genome),

>1,000,000 genomic switches that control genes (>10%).

• Gene = genomic substring that encodes

HOW to make a protein.

• Genomic switch = genomic substring that encodes

WHEN, WHERE & HOW MUCH of a protein to make.

[0,1,1,1]

B

Gene

H Gene N

H

Gene

N B Gene [1,0,0,1] http://cs173.stanford.edu [BejeranoWinter12/13]

[1,1,0,0]

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Epigenomics: transient writing “on” the genome http://cs173.stanford.edu [BejeranoWinter12/13] 21

Repeats / obile Elements

("selfish/junk DNA")

Human

Genome:

3*10 9 letters

2% known function

>50% junk http://cs173.stanford.edu [BejeranoWinter12/13] 22

Genome: conceptual part list

Genome:

“instruction”


A copy (actually two) in every cell.

Contains:

1. Genes

2. Gene regulation sequences

3. Repetitive DNA

4. Transient marks on genome packing material http://cs173.stanford.edu [BejeranoWinter12/13] 23







































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DNA sequencing

Genomes are awesome.

Let’s get’em.


…ACGTGACTGAGGACCGTG

CGACTGAGACTGACTGGGT

CTAGCTAGACTACGTTTTA

TATATATATACGTCGTCGT

ACTGATGACTAGATTACAG

ACTGATTTAGATACCTGAC

TGATTTTAAAAAAATATT…

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Genomic Drama

HGC Celera

Getting the “blueprint of life” http://cs173.stanford.edu [BejeranoWinter12/13]

2001

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1st gen

DNA sequencing costs

2nd gen http://cs173.stanford.edu [BejeranoWinter12/13]

3rd gen

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1st Genome Assembly

Some Terminology out of sequencer

2. Merge some “good” pairs of reads into longer contigs contig a contiguous sequence formed by several overlapping reads supercontig an ordered and oriented set

(scaffold) of contigs, usually by mate pairs

4. Derive consensus sequence consensus sequence derived from the sequene multiple alignment of reads

..ACGATTACAATAGGTT..

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2nd Gen: Next Generation (re)Sequencing

Output = massive amounts of short, lower quality reads.

New Technologies + New Algorithms = New Opportunities http://cs173.stanford.edu [BejeranoWinter12/13] 29

3rd Gen: cost effective, long reads

Just one example:

Output: very long reads of 10,000-100,000 basepairs each.

Sequence “anything” you like. In a lab. Trivial assembly.

Genomes, sequences everywhere

100 million species

7 billion individuals or sequence just an active portion

10 13 cells in a human http://cs173.stanford.edu [BejeranoWinter12/13] 31







































32


Portals to the Human Genome

Human Genome = three billion (3*10 9 ) basepairs:

GGTGCCAGGGAAAGGGCAGGAGGTGAGTGCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAG

GCTCAGGGCCCTGGAGTATAAAGCAGAATGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCGAAAGACCTGTTGGAGGCTATGAATGC

AATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATTTAGGACCAAGAATCGGGAGCTTGTGAA

CGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTCAGAGGCCCTGCGAGTGGAGTTCAGCTT

TTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCCATCTCATCTTCACCCTGCCTGGCTTCC

CTTGCTTGTTCCAGGATTGTTTCATAAAGAGGGATGTGGTTGGTCTTTAACCCTATGAATGCTGGCTGAGGATGCCTGCGGAACCTGTAGTGAAGCTTTC

AGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTAGTTGTGTAAGAGAGGGGCCCTTGGCAG

ACAAACAGGTTCTTCTCTGTTGGTGGGCCAGCCAGCAGGTCAGTGGGAAGGTTAAAGGTCATGGGGTTTGGGAGAACTGGGTGAGGAGTTCAGCCCCATC

CCCCGTAAAGCTCCTGGGAAGCACTTCTCTACTGGGGCAGCCCCTGATACCAGGGCACTCATTAACCCTCTGGGTGCCAGGGAAAGGGCAGGAGGTGAGT

GCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAGGCTCAGGGCCCTGGAGTATAAAGCAGAA

TGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCT

CTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAG

GAAAGACCTGTTGGAGGCTATGAATGCAATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATT

TAGGACCAAGAATCGGGAGCTTGTGAACGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTC

AGAGGCCCTGCGAGTGGAGTTCAGCTTTTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCC

ATCTCATCTTCACCCTGCCTGGCTTCCCTTGCTTGTTCCAGGATTGTTTCATAAAGAGGGATGTGGTTGGTCTTTAACCCTATGAATGCTGGCTGAGGAT

GCCTGCGGAACCTGTAGTGAAGCTTTCAGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTA

GTTGTGTAAGAGAGGGGCCCTTGGCAGACAAACAGGTTCTTCTCTGTTGGTGGGCCAGCCAGCAGGTCAGTGGGAAGGTTAAAGGTCATGGGGTTTGGGA

GAAACTGGGTGAGGAGTTCAGCCCCATCCCCCGTAAAGCTCCTGGGAAGCACTTCTCTACTGGGGCAGCCCCTGATACCAGGGCACTCATTAACCCTCTG

GGTGCCAGGGAAAGGGCAGGAGGTGAGTGCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAG

GCTCAGGGCCCTGGAGTATAAAGCAGAATGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCGAAAGACCTGTTGGAGGCTATGAATGC

AATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATTTAGGACCAAGAATCGGGAGCTTGTGAA

CGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTCAGAGGCCCTGCGAGTGGAGTTCAGCTT

TTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCCATCTCATCTTCACCCTGCCTGGCTTCC

CTTGCTTGTTCCAGGATTGTTTCATAAAGAGGGATGTGGTTGGTCTTTAACCCTATGAATGCTGGCTGAGGATGCCTGCGGAACCTGTAGTGAAGCTTTC

AGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTAGTTGTGTAAGAGAGGGGCCCTTGGCAG

ACAAACAGGTTCTTCTCTGTTGGTGGGCCAGCCAGCAGGTCAGTGGGAAGGTTAAAGGTCATGGGGTTTGGGAGAACTGGGTGAGGAGTTCAGCCCCATC

CCCCGTAAAGCTCCTGGGAAGCACTTCTCTACTGGGGCAGCCCCTGATACCAGGGCACTCATTAACCCTCTGGGTGCCAGGGAAAGGGCAGGAGGTGAGT

GCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAGGCTCAGGGCCCTGGAGTATAAAGCAGAA

TGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCT

CTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAG http://cs173.stanford.edu [BejeranoWinter12/13] 33

Genome Browser Database

visualize

Underlying

Database

(MySQL) search & download

Primary table: positions, names, etc.

http://cs273a.stanford.edu [Bejerano Fall11/12]

Auxiliary table: related data

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35


Genome Evolution

So far we’ve treated the genome as immutable.

But boy is it alive .

“Nothing in Biology Makes Sense

Except in the Light of Evolution”

Theodosius Dobzhansky http://cs173.stanford.edu [BejeranoWinter12/13] 36

Every Genome is Different

DNA Replication is imperfect – between individuals of the same species, even between the cells of an individual.

junk functional

...ACGTACGACTGACTAGCATCGACTACGA...

chicken egg

TT CAT

...ACGTACGACTGACTAGCATCGACTACGA...

“anything goes” many changes are not tolerated chicken

This has bad implications – disease, and good implications – evolution.


Genome mutation types: anything you can do to a string

Deletion

Inversion

Translocation

Duplication

time

Modes of evolution = Mutation + Selection

population

Don’t care Harmful mutation http://cs173.stanford.edu [BejeranoWinter12/13]

Beneficial mutation

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Cancer is a disease of the genome

that makes cell veer off plan

(whatever its particular plan is) and start dividing uncontrollably.


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Single Base Changes Can Be Detrimental


“Non-coding” mutations can be detrimental


[de Kok et al, 1996]

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Finding Disease Loci


Gene/Cell Therapy: Curing Genomics Defects

3

1

2

1 Get’em

2 Fix’em

3 Put’em back http://cs173.stanford.edu [BejeranoWinter12/13] 45







































46


Evolution is not all bad!

S

S

Sampled Genomes

S Speciation

Comparative Genomics, Evo Devo

We can sample ancient genomes

(tens of thousands of years old).

We can reconstruct ancestral genomes

(tens of millions of years).

How to learn from different genomes about different owners?

t

(we can get the tape, we can play the tape, we want to hear the music!) human chimp macaque mouse rat cow dog opossum platypus chicken zfish tetra fugu http://cs173.stanford.edu [BejeranoWinter12/13] 48

The great genotype-phenotype divide

and ways to cross it!

Genotype

http://cs273a.stanford.edu [Bejerano Fall11/12]

Phenotype

To Be Continued… http://cs173.stanford.edu [BejeranoWinter12/13] 50

lecture1

Welcome back!

What will we study?

Why Genomics?

Why Genomics?

Genomics is different

Why Genomics?

Field Goals

Class Goals

Materials

Topics

Important in Genomics

Further Reading

Organism – Cell - Genome

One Cell, One Genome, One Replication

Genomes, Genes & Proteins

Genes & Gene Regulation

Repeats / obile Elements

Genome: conceptual part list

DNA sequencing

Genomic Drama

DNA sequencing costs

2nd Gen: Next Generation (re)Sequencing

3rd Gen: cost effective, long reads

Genomes, sequences everywhere

Portals to the Human Genome

Genome Browser Database

Genome Evolution

“Nothing in Biology Makes Sense

Except in the Light of Evolution”

Every Genome is Different

Genome mutation types: anything you can do to a string

Deletion

Inversion

Translocation

Duplication

Modes of evolution = Mutation + Selection

Cancer is a disease of the genome

Single Base Changes Can Be Detrimental

“Non-coding” mutations can be detrimental

Finding Disease Loci

Evolution is not all bad!

Comparative Genomics, Evo Devo

The great genotype-phenotype divide

Genotype

Phenotype

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