1. De-novo Assembly
Day 4

2. The Challenge
Given many (millions or billions) of reads, produce a linear (or perhaps circular) genome
Issues:
Coverage
Errors in reads
Reads vary from very short (35bp) to quite long (800bp), and are double-stranded
Non-uniqueness of solution
Running time and memory

3. In an ideal world… Reads have no error
Reads are long enough and each appears once
Each read given in the same orientation (all 5’ to 3’, for example)
Contiguous reads have enough overlap so that they can easily be assembled

4. Shotgun DNA Sequencing
DNA target sample
SHEAR & SIZE
End Reads / Mate Pairs
550bp
Not all sequencing technologies produce
mate-pairs.
Different error models
Different read lengths
10,000bp 4

5. Terminology Reads are what you start with (35bp-800bp)
Fragmented assemblies produce contigs
Contigs can be put together into scaffolds

6. Reference-based vs de novo assembly
Comparative assembly means you have a “reference” genome
Same OR related species
Can get weird in bacteria and viruses due to recombination
De novo assembly means you do everything from scratch

7. Comparative Assembly Much easier than de novo! Basic idea:
Take the reads and map them onto the reference (allowing for small mismatches)
Collect all overlapping reads, produce a multiple sequence alignment, and produce consensus sequence

8. Comparative Assembly Fast
Short reads can map to several places (especially if they have errors)
Needs close reference genome
Repeats are problematic
Can be highly accurate even when reads have errors

9. De Novo assembly Much easier to do with long reads
Need very good coverage
Generally produces fragmented assemblies
Necessary when you don’t have a closely related (and correctly assembled) reference genome

10. Conceptual steps in de novo assembly
1. Find reads that overlap by a specified number of bases (the k-mer size)
2. Merge overlapping, “good” reads into longer contigs
3. Link contigs to form scaffolds using paired-end information
Diagrams from Serafim Batzoglou, Stanford

11. De Novo Assembly paradigms
Overlap-layout-consensus methods
k-mer graph (especially useful for assembly from short reads)
aka de Bruijn graph 11 11

12. de Bruijn graph has k-mers as nodes connected by reads; assembly involves finding Eulerian path through graph
Diagram from Michael Schatz, Cold Spring Harbor

13. de Bruijn graph
Vertices are k-mers that appear in some read, and edges defined by overlap of k-1 nucleotides
Small values of k produce small graphs
Does not require all-pairs overlap calculation!
But: loss of information about reads can lead to “chimeric” contigs, and incorrect assemblies
Also produces fragmented assemblies (even shorter contigs)

14. de Bruijn Graph use
Create the de Bruijn graph for the following string, using k=5
ACATAGGATTCAC
Find the Eulerian path
Is the Eulerian path unique?
Reconstruct the sequence from this path

15. But…
Because of
Errors in reads
Repeats
Insufficient coverage
de Bruijn graphs generally don’t Eulerian paths/circuits
This means the first step doesn’t completely assemble the genome

16. Velvet and SOAPdenovo are two leading assemblers
Velvet is from EMBL-EBI
SOAPdenovo is from BGI
k-mer size is adjustable parameter
Typically it is adjusted to maximize N50 length of scaffolds or contigs
N50 length is central measure of distribution weighted by lengths

17. NOTE: QC step slightly different
You still do standard checks with something like FASTQC
Read Trimming is different
Algorithms/tools can deal with different read lengths
Finding overlaps give longer contigs
So we never want to sacrifice good reads
Solution:
remove sequencing adapters
trim individual reads as needed

18. Three useful measures for optimization of k-mer length
Mean length: the usual average of the lengths
Median length: the length for which half the sequences are shorter & half are longer
N50 length: the length that splits the total bases in half, after the lengths are ordered
Example values for distribution of contig lengths:
Mean length: 627
Median length: 200
N50 length: 1,718
We’ll look at using N50 in in the practical

19. Practical prep Download and install
TRIMMOMATIC:
VELVET:
Use make ’MAXKMERLENGTH=70’ when compiling
Grab practical dataset from course page