De novo assembly, a multi-technology approach: Illumina, PacBio, and OpGen. A multi technological prospective for de novo assembly projects.

1. De novo assembly, a
multi-technology approach:
Illumina, PacBio, and OpGen
PhD. Francesco Vezzi
Senior Bioinformatician, NGI-Stockholm

2. Both Stockholm and Uppsala nodes
Illumina HiSeq 2000/2500 16
Illumina MiSeq 3
Life Technologies SOLiD 5500xl 4
Life Technologies SOLiD 5500wildfire 2
Life Technologies Ion Torrent 2
Life Technologies Ion Proton 6
Life Technologies Sanger ABI3730 2
Pacific Biosciences RSII 1
Argus Whole Genome Mapping System 1
One of 3 best-equipped sequencing sites in Europe

3. In this talk
Illumina (Stockholm):
• 100/150 bp paired reads (low error rate)
• 900/200 Gbp in 6/2 day(s)
PacBio (Uppsala):
• 8.5 Kbp reads, (max 30Kbp, high error rate)
• 375 Mbp (1 SMRT Cell) in 10 hours
OpGen Argus System (Stockholm):
• ~300 Kbp maps
• 10 Gbp in ~1 day

4. Optical Maps
• Restriction Map
◦ Representation of the cut sites on a
given DNA molecule to provide spatial
information of genetic loci
• An enzyme is selected and used
to cut the molecules. This
provides a 2D representation of
the molecule structure

5. Optical Maps: workflow
DNA extraction directly
from culture
Quality control of
extracted material
Prepare a chip
Run Argus System
Data assembly
StepsTime
3-8h
1h
1.5h
1h
2-8h
Notes

6. Closing genomes with Optical Maps
De novo reconstructs parts
missing in the reference strain
Correctly assembles long tandem
repeats
De Novo assembly
(Illumina, PacBio)
Set of un-ordered and
not oriented contigs
Optical Map
Contigs

7. Case Study: Combing all the technologies
~15 Mbp genome sequenced at High Coverage with:
• Illumina HiSeq:
• 500X PE libraries (180bp and 650bp insert)
• 150X MP library (3Kbp)
• 150X MP library (7Kbp)
• PacBio
• 50/60X with reads longer than 2Kbp
• OpGen
• 3 chips (only one worked really well)
• 300X coverage
• Average map length 320Kbp

8. Assembly Strategy
https://github.com/vezzi/de_novo_scilife
Semi-automated pipeline for de novo assembly:
• Global configuration file  tools and system configuration
• Sample configuration file  samples description
3 modules:
1. QC-module (Illumina only):
• Adaptor removal, kmer-analysis, fastqc, (insert size estimation)
2. Assemble-module (Illumina only):
• Runs specified assemblers and outputs executed commands
3. Validation-module:
• FRCbam, coverage analysis, GC-analysis, (N50)
I NEED USERS/FEEDBACK/CONTIRBUTIONS

9. QC-Module
Kmer analysis:
• Samples complexity
• Error rate
• Heterozygosity
0 1000 2000 3000 4000 5000 6000
05000100001500020000
Insert Size Histogram for All_Reads
in file lib_3000.bam
Insert Size
Count FR
RF
TANDEM
FASTQC
Adaptor removal
Alignment (partial assembly)

10. Assemble-Module
Illumina only:
• SOAPdenovo
• MaSuRCA
• Allpaths-LG
PacBio only:
• HGAP
• CABOG
Hybrid:
• PB-jelly (HAH)
>5000
#scaffolds totalLength maxContigLength N50 N80 percentageNs
Allpaths-LG 227 14513103 596012 139364 57619 15%
MASURCA 163 18549484 1188669 526519 282507 2%
HGAP 290 14399273 763592 142483 37117 0%
PB-Jelly 179 14718213 747750 195225 85127 13%
• Try-and-fail process
• Automated pipeline developed in order to
streamline these analysis
• MASURCA surprisingly the “best” assembler

11. MaSuRCA HGAP PB-Jelly (HAH)
Validation-Module

12. FRCbam
Validation-Module
PacBio-only assembly is
clearly outperforming
the others

13. Optical Maps
PacBio produces the best assembly however 290 contigs contigs are produced.
Optical Maps allowed to obtain
the 2D representation of the 7
chromosomes.
N.B. chromosome number was
one of the biological questions of
this project!!!
But much more can be done!!!

14. Incredible tool to finish (or almost finish) genomes
% contigs placed
Total size of placed
contigs
% size placed
contigs
% genome
covered
pacBio+OpGene 94.12 11578995 97% 77.05
Allpaths+OpGene 71.88 10692027 84% 52.88
Allpaths+Masurca+Opgene 80.65 27506424 92% 69.64
Allpaths+PacBio+Opgene 82.32 22271022 91% 83.05
Masurca+PacBio+pgene 94.44 28393392 98% 83.79
Allpaths+Masurca+PacBio+Opgene 85.42 39085419 94% 87.39
Combing all the technologies

15. Conclusions – Take home message
Attempt to automate de novo assembly process:
• https://github.com/vezzi/de_novo_scilife
• Not 100% automated
Illumina, PacBio, Hybrid assemblies:
• PacBio alone seems to produce the best assemblers
• Hybrid assembly seems to not be able to correct merged-assembly
problems
Mixing technologies is always a good idea:
• Possibility to compensate technological biases
• Allows to produce better assemblies

16. Thanks
https://github.com/vezzi/de_novo_scilife