1. Henrik Lantz - NBIS/SciLifeLab/Uppsala University
Genome properties
Henrik Lantz - NBIS/SciLifeLab/Uppsala University

2. Organisms are different, and so are assembly projects

3. Heterozygosity levels Repeat-content GC-content Secondary structure
Genome properties
Genome size
Heterozygosity levels
Repeat-content
GC-content
Secondary structure
Ploidy level

4. Genome sizes range from 100 kbp to 150 Gbp
The larger the genome, the more data is needed to assemble it (>50x usually)
Compute needs grow with increased amount of data (running time and memory)
Note that larger genomes do not necessarily have to be harder to assemble, although empirically this is often the case

5. Heterozygosity
(Slide by Torsten Seeman, Victorian Life Sciences Computation Initiative)

6. Highly heterozygous fungus
(Zheng et al. (2013) Nature Com.)

7. Highly heterozygous regions tend to be assembled separately
Heterozygosity
Highly heterozygous regions tend to be assembled separately
Homologous regions existing in multiple copies in the assembly
Downstream problems in determining orthology for gene based analyses, comparative genomics etc.

8. Effect of heterozygosity on assembly size
(Pryszcz and Gabaldon (2016) Nucl. Acids. res.)

9. Repeats
Identical, or near identical, regions occurring in multiple copies in a genome
(Istvan et al. (2011), PLoS ONE)

10. Repeats Low complexity regions
Regions where some nucleotides are overrepresented, such as in homopolymers, e.g., AAAAAAAAAA, or slightly more complex, e.g., AAATAAAAAGAAAA
Tandem repeats
A pattern of one or more nucleotides repeated directly adjacent to each other, e.g., AGAGAGAGAGAGAGAGAGAG
2-5 nucleotides - microsatellites (e.g., GATAGATAGATA)
10-60 nucleotides - minisatellite
Complex repeats (transposons, retroviruses, segmental duplications, rDNA, etc.)

11. How repeats can cause assembly errors
Mathematically best result: C R A B

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

13. When can I expect repeats to cause a problem?
Always…
Much more common in eukaryotes, in particular plants and many animals
Several conifers have a repeat content of ~75%, mostly simple repeats -> huge genomes

14. How to deal with repeats
Long range information, e.g., long reads or paired reads with long insert sizes R1 R2
Short reads

15. How to deal with repeats
Long range information, e.g., long reads or paired reads with long insert sizes
Long reads

16. GC-content Secondary structure Ploidy level Genome properties
Regions of low or high GC-content have a lower coverage (Illumina, not PacBio)
Secondary structure
Regions that are tightly bound get less coverage
Ploidy level
On higher ploidy levels you potentially have more alleles present

17. Additional complexity
Size of organism
Hard to extract enough DNA from small organisms
Pooled individuals
Increases the variability of the DNA (more alleles)
Inhibiting compounds
Lower coverage and shorter fragments
Presence of additional genomes/contamination
Lower coverage of what you actually are interested in, potentially chimeric assemblies

18. Henrik Lantz - NBIS/SciLifeLab/Uppsala University
Project planning
Henrik Lantz - NBIS/SciLifeLab/Uppsala University

19. Sequencing technology comparison
Sequencing system
Read length
Yield
Illumina Hi-Seq 2500
2x125 bp
180 M read pairs/lane, 28 Gbp/lane
Illumina HiSeqX
2x150 bp
350 M read pairs/lane, 78Gbp/lane
Illumina NovaSeq 6000
2000 M read pairs/lane
Illumina MiSeq
Up to 2x500 bp
18 M read pairs/lane, 7.4Gbp/run
PacBio RSII bp
1 Gb/SMRTcell
PacBio Sequel bp
~10 Gb/SMRTcell
Oxford Nanopore bp
148 Gb/Flow cell

20. Error rates and types Sequencing system Error type Error rate Illumina
Substitutions
0.1%
PacBio
Insertions
% depending on read length
Oxford Nanopore
Substitutions, indels
15%

21. De novo genome project workflow
Plan your project!
Extract DNA (and RNA)
Choose best sequence technology for the project
Sequencing
Quality assessment and other pre-assembly investigations
Assembly
Assembly validation
Assembly comparisons
Repeat masking?
Annotation

22. De novo genome project workflow
Plan your project!

23. What do you want to achieve?
Quality
Fully assembled and
phased genome,
full gene space
Draft genome,
split in longer repeats,
complete genes,
almost full gene space
Draft genome,
highly fragmented,
split genes,
partial gene space
Effort and cost

24. Pilot project?
One lane of Illlumina data is cheap and can be used to investigate genome size, presence of contaminants, and more.
Long read technologies are sensitive to DNA quality issues. Trying several extractions before deciding which one to use can be a good idea. An extraction that gives good QC values (fragment sizes, absorption rates, etc.), can still fail in sequencing!

25. Estimate computing resources
(Dominguez del Angel et al., 2018)

26. Estimate computing resources
What tools do you want to run?
Assembly can be memory-intense. Polishing can also require a lot of memory. A normal Rackham node might not be enough.
Do you have the necessary storage space?
Can you run your tools on several nodes over MPI?

27. Henrik Lantz - NBIS/SciLifeLab/Uppsala University
DNA extraction
Henrik Lantz - NBIS/SciLifeLab/Uppsala University

28. Example

29. Causes of DNA degradation
Mechanical damage during tissue homogenization.
Wrong pH and ionic strength of extraction buffer.
Incomplete removal / contamination with nucleases.
Phenol: too old, or inappropriately buffered (pH 7.8 – 8.0); incomplete removal.
Wrong pH of DNA solvent (acidic water).
Recommended: 1:10 TE for short-term storage, or 1xTE for long-term storage.
Vigorous pipetting (wide-bore pipet tips).
Vortexing of DNA in high concentrations.
Too many freeze-thaw cycles (we tested 5, still Ok).
Debatable: sequence-dependent

30. What are the main contaminants?
Polysaccharides
Lypopolysaccharides
Growth media residuals
Chitin
Protein
Secondary metabolites
Pigments
Growth media residuals
Chitin
Fats
Proteins
Pigments
Polyphenols
Polysaccharides
Secondary metabolites
Pigments
By Olga Vinnere Pettersson, Uppsala Genome Center, SciLifeLab

31. What do absorption ratios tell us?
Pure DNA 260/280: 1.8 – 2.0
< 1.8:
Too little DNA compared to other components of the solution; presence of organic contaminants: proteins and phenol; glycogen - absorb at 280 nm.
> 2.0:
High share of RNA.
Pure DNA 260/230: 2.0 – 2.2
<2.0:
Salt contamination, humic acids, peptides, aromatic compounds, polyphenols, urea, guanidine, thiocyanates (latter three are common kit components) – absorb at 230 nm.
>2.2:
High share of RNA, very high share of phenol, high turbidity, dirty instrument, wrong blank.
Photometrically active contaminants:
phenol, polyphenols, EDTA, thiocyanate, protein,
RNA, nucleotides (fragments below 5 bp)
By Olga Vinnere Pettersson, Uppsala Genome Center, SciLifeLab

32. DNA quality requirements
Some DNA left in the well
Sharp band of 20+kb
No sign of proteins
No smear of degraded DNA
No sign of RNA
NanoDrop:
260/280 = 1.8 – 2.0
260/230 = 2.0 – 2.2
Qubit or Picogreen:
10 kb insert libraries: 3-5 ug
20 kb insert libraries: ug

33. De novo genome project workflow
Plan your project!
Extract DNA (and RNA)
Extract much more DNA than you think you need
Also remember to extract RNA for the annotation
Single individual and haploid tissue if possible
In particular for Illumina mate-pairs data and PacBio, a lot of high molecular weight DNA is critical!
Extracting DNA for de novo assembly is very different from extractions intended for PCR
Do several extractions if possible, and run them on a gel to get an idea of how fragmented the DNA is
Try to remove contaminants from the extractions

34. Effect of insert size on scaffold length
(Treangen and Salzberg, 2013)