BIOSC 1540: L04 (Genome assembly)

Computational Biology

(BIOSC 1540)

Sep 5, 2024

Lecture 04:
De novo assembly

Announcements

A01 is due tonight at 11:59 pm
A02 will be released tomorrow and due next Thursday

We are putting our computer algorithm hats now

After today, you should be able to

1. Explain the fundamental challenge of reconstructing a complete genome.
2. Describe and apply the principles of the greedy algorithm.
3. Understand and construct de Bruijn graphs.

This is just alignment with extra steps
(our topic for next Thursday)

What is done 99% of the time

Repeats and high coverage are the main challenges

After today, you should be able to

1. Explain the fundamental challenge of reconstructing a complete genome.
2. Describe and apply the principles of the greedy algorithm.
3. Understand and construct de Bruijn graphs.

Let's formulate our problem

Suppose we have a collection of strings (i.e., reads)

(In CS, we call a sequence of characters a string)

BAA

AAB

BBA

ABA

ABB

BBB

AAA

BAB

We want to assemble these strings into a single, continuous string (i.e., contig)

What's the easiest way?

Concatenate

BAAAABBBAABAABBBBBAAABAB

Done!

Well, no.

Right?

This is called a "superstring"

Suppose we want the shortest superstring

This is a valid superstring, but why would we want the shortest?

BAAAABBBAABAABBBBBAAABAB

Talk with your neighbors

Overlap maximization

Reduces redundancy
Maximizes confidence with highest overlaps

Repeat resolution

Resolves repeats by favoring collapsed arrangements

Evolutionary pressure

Most genomes have selective pressure to be efficient

Merge strings by highest overlap

We can arrange the strings with overlaps of two

BAAAABBBAABAABBBBBAAABAB

Concatenated:

AAABBBABAA

Overlapped:

AA

AAA

B

AB

B

BB

B

BB

A

BA

B

AB

A

BA

A

Great! That was easy

Merge strings one at a time keeping a consistent 5' and 3'
Always merge the largest overlap
Repeat

Procedure

What happens if we have a tie?

Talk with your neighbors

5

4,

4

Length = 9

Tie breakers are a personal preference

First encountered, first merged

Highest quality base calls

Highest coverage

Look ahead

Exclude

The one you found first

Use sequence with highest quality

Whichever results in more coverage

Do both and evaluate consequences

Be petty and don't merge them (separate contigs)

Being greedy makes genome assembly tractable

Let's get some practice being greedy

ABA

ABB

AAA

AAB

BBB

BBA

BAB

BAA

For ties, choose the one you found first

Repeats ruin our assembly

a_long_long_long_time

We are missing a "_long". Why?

Let's take a string, and cyclically permute it with k = 6

Longer reads and genome assembly

We get the correct string back, but how did increasing our k fix this?

a_long_long_long_time

By having one read span all three "long"s, we prevented a collapse

k = 8

Greedy assembly is not used in practice

It just helps us understand our problem

After today, you should be able to

1. Explain the fundamental challenge of reconstructing a complete genome.
2. Describe and apply the principles of the greedy algorithm.
3. Understand and construct de Bruijn graphs.

Graphs is a data structure for drawing relationships between items

Node

Represents a single entity

Person
Location
Protein
Sequencing read

Edge

Represents a connection (possibly with a direction)

Instagram follower
Flights
Protein-protein interaction
Sequence overlap

Genome assembly uses direct edges to specify overlap and concatenation

"tomorrow and tomorrow and tomorrow"

Let's build a directed multigraph:

Each unique k-mer is a node
Add directed edges for each overlap and concatenation

K-mer is a substring of length k

(We will cheat here and write down just unique words)

tomorrow

and

Building k-mers from a string

GGCGATTCATCG

Slice first k characters
Shift right one character
Repeat

Spectrum with k = 3

GGC

GCG

CGA

TCG

ATC

GAT

ATT

TTC

TCA

CAT

All 3-mers

Build a De Bruijn graph with k-1 nodes

AATGGCGTA

AAT

ATG

GGC

GCG

TGG

CGT

GTA

AATG

ATGG

TGGG

GGCG

GCGT

CGTA

5'

3'

Step 1:

Let's use k = 4

Step 2:

AATG

L

R

Repeat

ATGG

TGGG

GGCG

GCGT

CGTA

A node is balanced if indegree equals outdegree

Semi-balanced has difference of 1

Take left and right k-1 mer and make two connected nodes

Build k-mers

Graph is Eulerian if it contains <= 2 of these

De Bruijn practice

CGTAAAT

Build a De Bruijn graph with k = 3

CGT

GTA

TAA

AAA

AAT

CG

GT

TA

AA

AT

De Bruijn graphs with multiple reads

5' AATGGCGTA 3'

5' CGTAAAT 3'

Read 1

Read 2

Let's use k = 4

Wait, what happend? This is not Eulerian

Circular genomes are not Eulerian

AAT

ATG

GGC

GCG

TGG

CGT

GTA

TAA

AAA

Redo, but make it not circular

5' AATGGCGTA 3'

5' CGTAAAG 3'

5' TAAAGGCGAA3'

Read 1

Read 2

Read 3

AAT

ATG

GGC

GCG

TGG

CGT

GTA

TAA

AAA

AAG

CGA

GAA

AGG

Overlap

Edges on walk extend the contig

Why is this not Eulerian?

More than two semi-balanced nodes

Cannot walk along each edge once

We can add weights to edges

5' AATGGCGTA 3'

5' CGTAAAG 3'

5' TAAAGGCGAA3'

Read 1

Read 2

Read 3

Still not Eulerian, but we can walk it

If there was no overlap, then we would have some unconnected graphs

AAT

ATG

GGC

GCG

TGG

CGT

GTA

TAA

AAA

AAG

CGA

GAA

AGG

2

1

2

1

Errors dramatically increase the number of edges and unconnected graphs

Errors affect k-mer counts

Error correction