1. Pre-assembly analyses
Facility manager, SciLifeLab Bioinformatics Long-term Support (a.k.a WABI)
Credits to Doug Scofield, Nat Street, Francesco Vezzi, Amaryllis Vidali, Andrea Zuccolo and others!

2. Two types of assemblies
Case 1 :
Flycatcher (1.2 Gbp)
Herring (800 Mbp)
Malassezia (7 Mbp)

3. Two types of assemblies
Case 1 :
Flycatcher (1.2 Gbp)
Herring (800 Mbp)
Malassezia (7 Mbp)
Case 2 :
Spruce (20 Gbp)
Barnacle (1.4 Gbp)
Wolbachia (4 Mbp)

4. Just a word on N50…
N50 typically refers to a contig (or scaffold) length
But…
The original definition is the number of contigs needed to reach half of the genome size (L50 is the length)
Many programs use the total assembly size as a proxy for the genome size; this is sometimes completely misleading: Use NG50!
PI:s don’t understand N50 anyway; use something more intuitive : - contigs larger than 1 kbp sum to 93% of the genome size - contigs larger than 10 kbp sum to 48% of the genome size - contigs larger than 100 kbp sum to 19% of the genome size
N50
NG50
Assembly size
Genome size
Genome
Assembly
3 contigs
100 kbp
5 contigs
30 kbp

5. Why is it hard?

6. The devil is in the repeats
Mathematically best result: C R A B

7. Repeat errors Collapsed repeats Overlapping non-identical reads
and chimeras
Overlapping non-identical reads
Wrong contig order
Inversions

8. It’s getting worse A: ATCGGGTATATAG-CCTA ||||||| || || |||| B:
ATCGGGTGTACAGCCCTA A ?
A & B B

9. …and humans are easy.
Bacteria, archaea, fungi, some plants
Most animals, some plants
Many plants
Also: Heterozygozity is generally very low in mammals; most other species are much harder

10. Pre assembly Quality trimming (Error correction) Kmer analysis
De novo repeat library

11. Quality trimming
DeBruijn-graph assemblers are in principle sensitive to errors
since they do not take base quality values into account
Trim adapters (e.g. Cutadapt)
Filter on quality, both 5’ and 3’ end! (e.g. Trimmomatic)
Consider hard-trimming of 5’ end
Error correction (e.g. Quake)
Inspect (e.g. FastQC)
Plots by Olof Karlberg

12. Reads vs kmers …….. 1 read: 100 bp Kmers: k=21bp N= (L – k + 1)
(100bp – 21 bp + 1) 80
……..
Base coverage * (L-k+1) = Kmer coverage L
Ex: 50X * ( ) = 40X (i.e. kmer coverage is 80% of base coverage)
100

13. Kmer analyses Compute the frequency of each kmer in the dataset
(e.g. Jellyfish --both-strands)
Note: RAM-intense!

14. Digging into the kmers Genome size Remove low-copy kmers
Identify the coverage peak
Divide total nb of kmers by peak
“Cpeak
20 million distinct kmers occure
55 times in all reads combined”
Genome size = Ktot/Cpeak
Here:
1.4 Gbp = 80 G / 55
Note: Ktot = Nb reads * (L-k+1)
Base coverage = Cpeak
(L-k+1)/L
Here:
69X =
(100 – 21 +1)/100

15. Repeats: first shot
The nb of distinct kmers in the single-copy peak corresponds roughly to the single-copy genome size
Single-copy
Example
Beetle: 0.75 Gbp is single-copy, so almost 40% of the 1.2 Gbp genome is repeated (kmer=27)
Repeats

16. Heterozygocity
Double peak in the kmer histogram; clear indication of heterozygocity
Not entirely easy to quantify (although attempts have been made)

17. A word on quality filtering…
Light QC filter
Hard QC filter

18. Repeat library and repeat quantification
Create a de novo repeat library
Run a low-coverage (e.g. 0.1X) assembly (e.g. RepeatExplorer or Trinity)
Filter contaminants and mito/chloro
[ Make non-redundant (e.g. Cdhit) ]
Quantify the (high) repeat content by an independent subset of reads
- Mapping (e.g. bwa), or
- Mask with RepeatMasker

19. Repeat library from low coverage data
Sparse
seq data
Overlaps?

20. Repeat library from low coverage data
Sparse
seq data
Overlaps?
Assembled contigs

21. Repeat library from low coverage data
Sparse
seq data
Overlaps?
Assembled contigs
Warning! Beware of contaminations, plastids etc

22. Quantify your repeat seqs
Independent
set of sparse
data
Screen reads with repeat seqs
33% of all bases in the reads are covered by repeat seqs 
33% of the genome is “repeated”
Warning! The quantification depends heavily on the size of the original read set

23. Classifying repeats LTR Gypsy/Copia LINE/SINE Getting tricky…
DNA elements …
Getting tricky…
Classifying the repeat library directly
RepeatMasker
Repeat protein domain serach (
Problems
No close homologs in databases
Rapid evolution of repeats (like transposable elements)
Non-autonomous TE:s do not contain proteins
Solutions
Fetch intact ORF:s from hits in assembly
Extend assembly matches and get more complete elements
Check match alignment profiles in assembly (LINES conserved at 3’ end but not at 5’..)
=> Often slow, manual, species-specific solutions

24. Take home
Genome assembly is sometimes reasonably easy, if you are lucky and not too picky. There are tools to indicate which one you are up against.
Adapters and quality trimming is a pain in the neck. But you should probably do it. Unless you use ALLPATHS-LG
Genome size and repeat content can (often better!) be estimated without an assembly

25. Thanks