1. Henrik Lantz - BILS/SciLife/Uppsala University
Genome assembly
Henrik Lantz - BILS/SciLife/Uppsala University

2. De novo genome project workflow
Extracting DNA (and RNA) - as much DNA as possible!
Choosing best sequence technology for the project
Sequencing
Quality assessment and other pre-assembly investigations
Assembly
Assembly validation
Assembly comparisons
Repeat masking?
Annotation

3. Genome assembly - things to think about
Genome specifics - Size of genome, number of chromosomes, repeat content, heterozygosity
Which assembly programs can run on “my” genome?
What kind of data do these programs need?

4. Genome assembly - things to think about

5. Genome assembly - things to think about
Genome specifics - Size of genome, number of chromosomes, repeat content, heterozygosity
Which assembly programs can run on “my” genome?
What kind of data do these programs need?
How much data do I need? Will I have enough coverage? Do I need to subsample?
Are there closely related organisms that already have had their genome sequenced?
Do I need additional data for post-assembly?

6. Genome assembly programs
Abyss
Allpaths-LG
CABOG (a.k.a. Celera)
HGAP
Masurca
Mira
Newbler
SGA
SoapDeNovo
Spades
Velvet

7. Genome assembly programs
Name
Algorithm
Data
Abyss
De Bruijn
Illumina
Allpaths-lg
Illumina/PacBio
CABOG (Celera)
OLC
All
HGAP
PacBio
Masurca
De Bruijn/OLC
Mira
“OLC”
Newbler
454/Illumina/Torrent
SGA
String
SoapDeNovo
Spades
Velvet

8. OLC vs. de Bruijn

9. de Bruijn

10. de Bruijn

11. Sequence Assembly via De Bruijn Graphs
The first step in a de Bruijn graph-based assembly is to construct the de Bruijn graph from the sequence reads.
Each read is decomposed into substrings of some specified length k. Each word of length k is called a k-mer. In this example, k is set to 5, so here each 5-mer is extracted from the read. An ordered list of k-mers is generated by scanning a window of length k across the length of the read. You’ll notice that each k-mer overlaps the next k-mer by exactly k-1 bases. --
Then, a de Bruijn graph is constructed by assigning each unique k-mer as a node in the graph and connecting immediately overlapping k-mers by an edge. This is a very effective and compact way of representing the sequence data within the reads. For example, hundreds of millions of reads can be sequenced, and the identical sequence regions within reads become compressed into individual nodes within the graph.
At positions where related sequences diverge due to allelic polymorphisms, splicing variations, repeats, or due to sequencing errors, the graph will branch and can form bulges or loops.
From Martin & Wang, Nat. Rev. Genet. 2011

12. From Martin & Wang, Nat. Rev. Genet. 2011
After building the graph from all the reads, the graph is typically pruned to remove bubbles and structures that likely stem from sequencing errors, --
and the graph is compacted by collapsing those nodes that form linear unbranched chains of overlapping k-mers.
For example, this linear chain of kmers is compressed into a single node in the compacted graph.
From Martin & Wang, Nat. Rev. Genet. 2011

13. From Martin & Wang, Nat. Rev. Genet. 2011
Now, to reconstruct transcripts, paths are traversed across the graph. --
In this example, there are four possible paths from the beginning to the end of the graph, each path shown traced by a different color.
By traversing each path, a different transcript sequence is generated.
In this case, each of the four differently colored paths generates a different sequence as shown.
By taking into account the paths that the reads trace through the graph, along with any mate-pairing information, constraints can be placed such that not all possible path combinations are reported, but instead only those paths that are best supported by the RNA-seq reads.
From Martin & Wang, Nat. Rev. Genet. 2011

14. De Bruijn
Pros: Computationally efficient, can work with large coverage short read datasets
Cons: Sensitive to sequence errors, connection between assembly and read is lost, does not work so well with longer reads

15. OLC Pros: Utilizes longer reads well
Cons: Time consuming, high memory requirements

16. Assemblathon 2
Uses 454, Illumina, and PacBio for three large eukaryote genomes: a bird, a fish, and a snake
Bird - Illumina 14 libraries, 454, PacBio
Fish - Illumina, 8 libraries
Snake - Illumina, 4 libraries
Teams take the data, perform assemblies with whatever tools they wish, and then submit their results => teams are evaluated more than individual programs!
GigaScience 2013, 2:10

17. Assemblathon 2

18. Assemblathon 2 - Bird vs. Snake

19. Assemblathon 2 - Bird

20. CEGMA

21. Assemblathon 2 - Validation measures

22. GAGE-B Uses Illumina (HiSeq and MiSeq) data for a number of bacteria
One team runs all programs => assembly programs are compared, not teams
Reference high quality assemblies are available => errors/misassemblies can be quantified

23. Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study.
Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study. All datasets were 100 bp HiSeq reads from B.cereus
Magoc T et al. Bioinformatics 2013;29:
© The Author Published by Oxford University Press. All rights reserved. For Permissions, please

24. GAGE-B

25. GAGE-B statistics

26. Genome assembly programs - pros and cons
Abyss
Allpaths-LG
CABOG (a.k.a. Celera)
Masurca
Mira
Newbler
SGA
SoapDeNovo
Spades
Velvet

27. Allpaths-LG
Pros: Produces contigs and scaffolds with high N50 values, can use PacBio data for scaffolding, can run on large genomes with high coverage
Cons: Only accepts Illumina data, needs very specific libraries to work at all (180 bp + 3 kbp), needs very high coverage (100x), takes a long time to run and requires a lot of memory

28. Assemblathon 2 - Bird vs. Snake

29. Assemblathon 2 - Bird

30. Masurca
Pros: Can accept any type of data, is a true hybrid assembler, usable for very large genomes, produces top results in comparison of assembly statistics
Cons: Takes a long time to run, unstable(?)

31. Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study.
Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study. All datasets were 100 bp HiSeq reads from B.cereus
Magoc T et al. Bioinformatics 2013;29:
© The Author Published by Oxford University Press. All rights reserved. For Permissions, please

32. GAGE-B statistics

33. MIRA
Pros: Can accept any type of data, is a true hybrid assembler, produces good assemblies for smaller genomes, excellent documentation
Cons: Only useful for smaller genomes (bacteria, fungi), can not use high coverage data (prefers max 50x), takes a long time to run, no scaffolding

34. Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study.
Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study. All datasets were 100 bp HiSeq reads from B.cereus
Magoc T et al. Bioinformatics 2013;29:
© The Author Published by Oxford University Press. All rights reserved. For Permissions, please

35. GAGE-B statistics

36. SoapDeNovo
Pros: Usable on large genomes, easy to use and runs fairly quickly, can use high coverage data
Cons: Only accepts Illumina data, medium results in assembly statistic comparisons

37. Assemblathon 2 - Bird vs. Snake

38. Assemblathon 2 - Bird

39. GAGE-B statistics

40. Spades
Pros: Designed to work with amplified data, performs very well in GAGE-B with MiSeq data
Cons: Only accepts Illumina data, only for smaller genomes

41. Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study.
Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study. All datasets were 100 bp HiSeq reads from B.cereus
Magoc T et al. Bioinformatics 2013;29:
© The Author Published by Oxford University Press. All rights reserved. For Permissions, please

42. GAGE-B statistics

43. Some recommendations
Large eukaryote genome, Illumina data: Allpaths-LG (needs specific libraries), SoapDeNovo, SGA, Masurca
Large eukaryote genome, additional longer reads: Masurca, Newbler, CABOG
Small eukaryote or prokaryote genome, Illumina data: Spades, Masurca, SoapDeNovo, Abyss, Velvet
Small eukaryote or prokaryote genome, mixed data: MIRA, Masurca, Newbler
Need to run in parallel: Abyss
Amplified data (Single Cell Genomics): Spades

44. Assemblathon 2 recommendations
Based on the findings of Assemblathon 2, we make a few broad suggestions to someone looking to perform a de novo assembly of a large eukaryotic genome:
1. Don’t trust the results of a single assembly. If possible, generate several assemblies (with different assemblers and/or different assembler parameters). Some of the best assemblies entered for Assemblathon 2 were the evaluation assemblies rather than the competition entries.
2. Do not place too much faith in a single metric. It is unlikely that we would have considered SGA to have produced the highest ranked snake assembly if we had only considered a single metric.
3. Potentially choose an assembler that excels in the area you are interested in (e.g., coverage, continuity, or number of error free bases).
4. If you are interested in generating a genome assembly for the purpose of genic analysis (e.g., training a gene finder, studying codon usage bias, looking for intron-specific motifs), then it may not be necessary to be concerned by low N50/NG50 values or by a small assembly size.
5. Assess the levels of heterozygosity in your target genome before you assemble (or sequence) it and set your expectations accordingly.

45. Post assembly considerations
External scaffolders: SSPACE (commercial), SGA (see Hunt et al. in Genome Biology 2014:15, R42).
Gap Closers (use with caution!): IMAGE, PILON, GapCloser
Error correction: Nesoni, PILON
Assembly validation is extremely important!

46. Abyss
Pros: Only assembler that can run in parallel on different nodes => does not need a single huge memory node, fast, can run on large genomes with a high coverage
Cons: Only accepts Illumina data, does not excel in any statistics

47. Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study.
Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study. All datasets were 100 bp HiSeq reads from B.cereus
Magoc T et al. Bioinformatics 2013;29:
© The Author Published by Oxford University Press. All rights reserved. For Permissions, please

48. Assemblathon 2 - Bird vs. Snake

49. CABOG (Celera)
Pros: Can accept any type of data, is a true hybrid assembler, output can easily be analyzed in the assembly validation toolkit AMOSvalidate, usable for large genomes
Cons: Does not perform so well for any statistic in comparisons

50. Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study.
Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study. All datasets were 100 bp HiSeq reads from B.cereus
Magoc T et al. Bioinformatics 2013;29:
© The Author Published by Oxford University Press. All rights reserved. For Permissions, please

51. GAGE-B statistics

52. Newbler
Pros: Easy to run, works very well on 454 and Ion Torrent data, can use many types of data, usable for larger genomes, produces competitive assemblies if longer reads are available
Cons: Requires longer reads to perform well

53. Assemblathon 2 - Bird vs. Snake

54. Assemblathon 2 - Bird

55. SGA Pros: Usable on large genomes, memory-efficient
Cons: Only accepts Illumina data, does not perform well in comparisons of assembly statistics

56. GAGE-B statistics

57. Assemblathon 2 - Bird

58. Velvet Pros: Easy to use, runs quickly
Cons: Only accepts Illumina data, only for smaller genomes, does not excel in any assembly statistic comparison

59. Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study.
Comparison of N50 contig size (in kilobases) on the y-axis, versus depth of coverage on the x-axis, for the eight assemblers used in this study. All datasets were 100 bp HiSeq reads from B.cereus
Magoc T et al. Bioinformatics 2013;29:
© The Author Published by Oxford University Press. All rights reserved. For Permissions, please

60. GAGE-B statistics