7.91 / 7.36 / BE.490

Lecture #1

Feb. 24, 2004

Genome Sequencing

&

DNA Sequence Analysis

Chris Burge

What is a Genome?

A genome is NOT a bag of proteins

What’s in the Human Genome?

Outline of Unit II:

DNA/RNA Sequence Analysis

Reading*

2/24

2/26

3/2

M Ch. 4

3/4

M Ch. 4

3/9

DNA Sequence Evolution

3/11

RNA Structure Prediction & Applications

M Ch. 5

3/16

Literature Discussion

TBA

Genome Sequencing & DNA Sequence Analysis

M Ch. 3

DNA Sequence Comparison & Alignment

M Ch. 7

DNA Motif Modeling & Discovery

Markov and Hidden Markov Models for DNA

M Ch. 6

* M = Mount, “Bioinformatics: Sequence and Genome Analysis”

Feedback to Instructor

Examples from past years:

• Comic font looks stupid

• Burge uses too much genomics jargon

• Better synergy between Yaffe/Burge sections

• Asks questions to the class, student answers,

but I didn’t hear/understand the answer…

DNA vs Protein Sequence Analysis

Protein Sequence Analysis

DNA Sequence Analysis

- emphasis on chemistry

- emphasis on regulation

- protein structure

- RNA structure

- selection is everywhere

- signal vs noise (

statistics

)

- multiple alignment

- motif finding

- comparative proteomics

- comparative genomics

- data:

O(10^8) aa

- data:

O(10^10) nt

Read your probability/statistics primer!

Genome Sequencing

& DNA Sequence Analysis

•

The Language of Genomics

•

Shotgun Sequencing

•

DNA Sequence Alignment I

•

Comparative Genomics Examples

- Progress: genomes, transcriptomes, etc.

- How to choose a mismatch penalty

- PipMaker, Phylogenetic Shadowing

Recent Media Attention

Genomespeak

Bork, Peer, and Richard Copley. " Genome Speak."
Nature

409 (15 February 2001): 815.

Learn to speak genomic

In the following article, note the use of the following genomic terms:

euchromatic, whole-genome shotgun sequencing, sequence reads,

5.11-fold coverage, plasmid clones, whole-genome assembly,

regional chromosome assembly.

Venter, JC, MD Adams, EW Myers, PW Li, RJ Mural, GG Sutton, HO Smith, … "The
Sequence of The Human Genome." Science 291, no. 5507 (16 February 2001): 1304-51.

Types of Nucleotides

• ribonucleotides

• deoxyribonucleotides

• dideoxyribonucleotides

DNA Sequencing

Adapted from Fig. 4.2 of “Genomes” by T. A. Brown, John Wiley & Sons, NY, 1999

Shotgun Sequencing a BAC or a Genome

200 kb (NIH)

3 Gb (Celera)

Sequence, Assemble

Sonicate, Subclone

Subclones

Shotgun Contigs

What
would
cause
problems
with
assembly?

Shotgun Coverage

(Poisson distribution)

Sequence

N

reads, 500 bp each, from a 200kb BAC

Coverage/read

p

= 500/200,000 = 0.0025

Total coverage

C

=

Np

Y

= no. of reads covering the point

x

P(Y=k) = (N!/(N-k)!k!) p

k

(1-p)

N-k

≈ e

-c

c

k

/ k!

.

x

P(Y=0)= e

-c

Examples:

e

-2

≈ 0.14

e

-4

≈ 0.02

What could cause reality to differ from theory?

Clickable Genomes

Eukaryotes

Protists

Eubacteria

S. cerevisiae

Plasmodium

E. coli

S. pombe

Giardia

B. subtilis

C. elegans

…

S. aureus

Drosophila

(several)

…

Anopheles

(>100)

Ciona

Archaea

Arabidopsis

Methanococcus

Human

Sulfolobus

Phages/Viruses

Mouse

…

Lots

Tetraodon

(total of ~16)

Fugu

Organelles

Zebrafish

Lots

Neurospora

Aspergillus

…

Large-scale Transcript Sequencing

Please see the following example article that uses large-scale

transcript sequencing.

Nature

420

, no. 6915 (5 December 2002): 563-73.

Okazaki, Y, M Furuno, T Kasukawa, J Adachi, H Bono, S Kondo, … "Analysis of The

M

ouse Transcriptome Based On Functional Annotation of 60,770 Full-length cDNAs."

EST Sequencing

dbEST release 022004

No. of public entries: 20,039,613

Summary by Organism - as of February 20, 2004

Homo sapiens

(human)

5,472,005

Mus musculus + domesticus

(mouse)

4,055,481

Rattus sp. (rat)

583,841

Triticum aestivum

(wheat)

549,926

Ciona intestinalis

492,511

Gallus gallus

(chicken)

460,385

Danio rerio

(zebrafish)

450,652

Zea mays

(maize)

391,417

Xenopus laevis

(African clawed frog)

359,901

Hordeum vulgare + subsp. vulgare

(barley)

352,924

Source: NCBI - http://ncbi.nlm.nih.gov

*

-omes and -omics

Proteome

Variome

Transcriptome

Genome

Mass spec, Y2H, ?

SNPs, haplotypes

ESTs, cDNAs, microarrays

Genome sequences

Ribonome?

Glycome ???

*Warning: some of the words on this slide may not be in Webster’s dictionary

DNA Sequence Alignment I

How does DNA alignment differ from protein alignment?

S

ubject:

Use BLASTN instead of BLASTP

1

ttgacctagatgagatgtcgttcacttttactgagctacagaaaa 45

|||| |||||||||||| | |||||||||||||||||||||||||

403 ttgatctagatgagatgccattcacttttactgagctacagaaaa 447

Query:

Nucleotide-

nucleotide

BLAST Web

Server

(BLASTN)

DNA Sequence Alignment II

Translating searches:

translate in all possible reading frames
search peptides against protein database (BLASTP)

ttgacctagatgagatgtcgttcactttactgagctacagaaaa

ttg|acc|tag|atg|aga|tgt|cgt|tca|ctt|tta|ctg|agc|tac|aga|aaa

L T x M R C R S L L L S Y R K

t|tga|cct|aga|tga|gat|gtc|gtt|cac|ttt|tac|tga|gct|aca|gaa|aa

x P R x D V V H F Y x S T E

tt|gac|cta|gat|gag|atg|tcg|ttc|act|ttt|act|gag|cta|cag|aaa|a

D L D E M S F T F T E L Q K

Also consider reading frames on complementary DNA strand

DNA Sequence Alignment III

Common flavors of BLAST:

Program

Query

Database

BLASTP

aa

aa

BLASTN

nt

nt

BLASTX

nt

(

⇒

aa

)

aa

TBLASTN

aa

nt

(

⇒

aa

)

TBLASTX

nt

(

⇒

aa

)

nt

(

⇒

aa

)

PsiBLAST

aa

(

aa

msa)

aa

Which would be best for searching ESTs against a genome?

DNA Sequence Alignment IV

Which alignments are significant?

Q:

1

S:

Identify high scoring segments whose score

S

exceeds

a cutoff

x

using dynamic programming.

Scores follow an extreme value distribution:

P(S > x) = 1 - exp[-Kmn e

-

λx

]

For sequences of length

m, n

where

K,

λ

depend on the score

matrix and the composition of the sequences being compared

(Same theory as for protein sequence alignments)

ttgacctagatgagatgtcgttcacttttactgagctacagaaaa 45

|||| |||||||||||| | |||||||||||||||||||||||||

403 ttgatctagatgagatgccattcacttttactgagctacagaaaa 447

Notes (cont)

From M. Yaffe

Lecture #2

• The random sequence alignment scores

would give rise to an “extreme value”
distribution – like a skewed gaussian.

• Called Gumbel extreme value

distribution

For a normal distribution with a mean m and a variance

σ, the height of the

curve is described by Y=1/(

σ√2π) exp[-(x-m)

2

/2

σ

2

]

For an extreme value distribution, the height of the curve is described by
Y=exp[-x-e

-x

] …and P(S>x) = 1-exp[-e

-

λ(x-u)]

where u=(ln Kmn)/

λ

Can show that mean extreme score is ~ log

2

(nm), and the probability of

getting a score that exceeds some number of “standard deviations” x is:
P(S>x)~ Kmne

-

λx.

***K and

λ are tabulated for different matrices ****

-

λS

For the less statistically inclined: E~ Kmne

-2 -1

0.2

Yev

0.4

-4

4

0.4

B.

Yn

Probability values for the extreme value distribution (A) and the

normal distribution (B). The area under each curve is 1.

0

1

2

X

X

A.

3

4

5

i

DNA Sequence Alignment V

How is

λ

related to the score matrix?

λ

is the unique positive solution to the equation*:

∑

p p

j

e

λ

s

ij

= 1

i

i,j

p

= frequency of nt i,

s

ij

= score for aligning an i,j pair

What kind of an equation is this?

(transcendental)

What would happen to

λ



if we doubled all the scores?

(reduced by half)

What does this tell us about the nature of

λ

?

(scaling factor)

*Karlin & Altschul, 1990

DNA Sequence Alignment VI

What scoring matrix to use for DNA?

Usually use simple match-mismatch matrices:

i

j: A

C

G

T

A

1

m

m

m

C

m

1

m

m

s

i,j

:

G

T

m

m

m

m

1

m

m

1

m =

“mismatch penalty” (must be negative)

DNA Sequence Alignment VII

How to choose the mismatch penalty?

Use theory of High Scoring Segment composition*

High scoring alignments will have composition:

q

ij

= p

i

p

j

e

λs

ij

where

q

ij

=

frequency of

i,j

pairs (“target frequencies”)

p , p =

freq of

i, j

bases in sequences being compared

i

j

What would happen to the target frequencies if we

doubled all of the scores?

*Karlin & Altschul, 1990