This document discusses de novo genome assembly, which is the process of reconstructing long genomic sequences from many short sequencing reads without the aid of a reference genome. It is challenging due to factors like short read lengths, repetitive sequences that complicate the assembly graph, and sequencing errors. The goals of assembly are to produce contiguous sequences with high completeness and correctness by resolving overlaps between reads into consensus sequences. Metrics like N50, core gene content, and read remapping are used to assess assembly quality.

1. De novo Genome Assembly
A/Prof Torsten Seemann
Winter School in Mathematical & Computational Biology - Brisbane, Australia - 4 July 2016

2. Introduction

3. The human genome has 47 pieces
MT
(or XY)

4. The shortest piece is 48,000,000 bp
Total haploid size
3,200,000,000 bp
(3.2 Gbp)
Total diploid size
6,400,000,000 bp
(6.4 Gbp)
∑ = 6,400,000,000 bp

5. Human DNA iSequencer ™ 46 chromosomal
and1 mitochondrial
sequences
In an ideal world ...
AGTCTAGGATTCGCTATAG
ATTCAGGCTCTGATATATT
TCGCGGCATTAGCTAGAGA
TCTCGAGATTCGTCCCAGT
CTAGGATTCGCTAT
AAGTCTAAGATTC...

6. The real world ( for now )
Short
fragments
Reads are stored in “FASTQ” files
Genome
Sequencing

8. Assemble Compare

9. Genome assembly
(the red pill)

10. De novo genome assembly
Ideally, one sequence per replicon.
Millions of short
sequences
(reads)
A few long
sequences
(contigs)
Reconstruct the original genome
from the sequence reads only

11. De novo genome assembly
“From scratch”

12. An example

13. A small “genome”
Friends,
Romans,
countrymen,
lend me your ears;
I’ll return
them
tomorrow!

14. • Reads
ds, Romans, count
ns, countrymen, le
Friends, Rom
send me your ears;
crymen, lend me
Shakespearomics
Whoops!
I dropped
them.

15. • Reads
ds, Romans, count
ns, countrymen, le
Friends, Rom
send me your ears;
crymen, lend me
• Overlaps
Friends, Rom
ds, Romans, count
ns, countrymen, le
crymen, lend me
send me your ears;
Shakespearomics
I am good
with words.

16. • Reads
ds, Romans, count
ns, countrymen, le
Friends, Rom
send me your ears;
crymen, lend me
• Overlaps
Friends, Rom
ds, Romans, count
ns, countrymen, le
crymen, lend me
send me your ears;
• Majority consensus
Friends, Romans, countrymen, lend me your ears; (1 contig)
Shakespearomics
We have
reached a
consensus !

17. Overlap - Layout - Consensus
Amplified DNA
Shear DNA
Sequenced reads
Overlaps
Layout
Consensus ↠ “Contigs”

18. Assembly graphs

19. Overlap graph

20. Another example is this

21. Overlaps find

22. Do the graph traverse
Size matters not. Look at me. Judge me by my size, do you?
Size matters not. Look at me. Judge me by my soze, do you?
2 supporting reads
1 supporting reads

23. So far, so good.

25. Why is it so hard?

26. What makes a jigsaw puzzle hard?
Repetitive
regions
Lots of
pieces
Missing
pieces
Multiple
copies
No corners
(circular
genomes)
No box
Dirty
pieces
Frayed
pieces : .,

27. What makes genome assembly hard
Size of the human genome = 3.2 x 109
bp (3,200,000,000)
Typical short read length = 102
bp (100)
A puzzle with millions to billions of pieces
Storing in RAM is a challenge ⇢ “succinct data structure”
1. Many pieces (read length is very short compared to the genome)

28. What makes genome assembly hard
Finding overlaps means
examining every pair of reads
Comparisons = N×(N-1)/2
~ N2
Lots of smart tricks to reduce
this close to ~N
2. Lots of overlaps

29. What makes genome assembly hard
ATATATATATATATATATATATATATATATATATATATATATATATATATATATATATATATA
TATATATA TATATATA TATATATA TATATATA TATATATA
TATATATA TATATATA TATATATA
TATATATA TATATATA TATATATA
3. Lots of sky (short repeats)
How could we possibly assemble this segment of DNA?
All the reads are the same!

30. What makes genome assembly hard
Read 1: GGAACCTTTGGCCCTGT
Read 2: GGCGCTGTCCATTTTAGAAACC
4. Dirty pieces (sequencing errors)
1. The error in Read 2 might prevent us from seeing that it
overlaps with Read 1
2. How would we know which is the correct sequence?

31. What makes genome assembly hard
5. Multiple copies (long repeats)
Our old nemesis the REPEAT !
Gene Copy 1 Gene Copy 2

32. Repeats

33. What is a repeat?
A segment of DNA
that occurs more than once
in the genome

34. Major classes of repeats in the human genome
Repeat Class Arrangement Coverage (Hg) Length (bp)
Satellite (micro, mini) Tandem 3% 2-100
SINE Interspersed 15% 100-300
Transposable elements Interspersed 12% 200-5k
LINE Interspersed 21% 500-8k
rDNA Tandem 0.01% 2k-43k
Segmental Duplications Tandem or
Interspersed
0.2% 1k-100k

35. Repeats
Repeat copy 1 Repeat copy 2
Collapsed repeat consensus
1 locus
4 contigs

36. Repeats are hubs in the graph

37. Long reads can span repeats
Repeat copy 1 Repeat copy 2
long
reads
1 contig

38. Draft vs Finished genomes (bacteria)
250 bp - Illumina - $250 12,000 bp - Pacbio - $2500

39. The two laws of repeats
1. It is impossible to resolve repeats of length L
unless you have reads longer than L.
2. It is impossible to resolve repeats of length L
unless you have reads longer than L.

40. How much data do we need?

41. Coverage vs Depth
Coverage
Fraction of the genome
sequenced by
at least one read
Depth
Average number of reads
that cover any given region
Intuitively more reads should increase coverage and depth

42. Example: Read length = ⅛ Genome Length
Coverage = ⅜
Depth = ⅜
Genome
Reads

43. Example: Read length = ⅛ Genome Length
Coverage = 4.5/8
Depth = 5/8
Genome
Reads
Newly Covered Regions = 1.5 Reads

44. Example: Read length = ⅛ Genome Length
Coverage = 5.2/8
Depth = 7/8
Genome
Reads
Newly Covered Regions = 0.7 Reads

45. Example: Read length = ⅛ Genome Length
Coverage = 6.7/8
Depth = 10/8
Genome
Reads
Newly Covered Regions = 1.5 Reads

46. Depth is easy to calculate
Depth = N × L / G
Depth = Y / G
N = Number of reads
L = Length of a read
G = Genome length
Y = Sequence yield (N x L)

47. Example: Coverage of the Tarantula genome
“The size estimate of the tarantula
genome based on k-mer analysis is 6 Gb
and we sequenced at 40x coverage from
a single female A. geniculata”
Sangaard et al 2014. Nature Comm (5) p1-11
Depth = N × L / G
40 = N x 100 / (6 Billion)
N = 40 * 6 Billion / 100
N = 2.4 Billion

48. Coverage and depth are related
Approximate
formula for
coverage
assuming
random reads
Coverage = 1 - e-Depth

49. Much more sequencing needed in reality
● Sequencing is not random
○ GC and AT rich regions
are under represented
○ Other chemistry quirks
● More depth needed for:
○ sequencing errors
○ polymorphisms

50. Assessing assemblies

51. Contiguity
Completeness
Correctness

52. Contiguity
● Desire
○ Fewer contigs
○ Longer contigs
● Metrics
○ Number of contigs
○ Average contig length
○ Median contig length
○ Maximum contig length
○ “N50”, “NG50”, “D50”

53. Contiguity: the N50 statistic

54. Completeness : Total size
Proportion of the original genome represented by the assembly
Can be between 0 and 1
Proportion of estimated genome size
… but estimates are not perfect

55. Completeness: core genes
Proportion of coding sequences can be estimated based on
known core genes thought to be present in a wide variety of
organisms.
Assumes that the proportion of assembled genes is equal to the
proportion of assembled core genes.
Number of Core Genes in
Assembly
Number of Core Genes in
Database
In the past this was done with a tool called CEGMA
There is a new tool for this called BUSCO

56. Correctness
Proportion of the assembly that is free from errors
Errors include
1. Mis-joins
2. Repeat compressions
3. Unnecessary duplications
4. Indels / SNPs caused by assembler

57. Correctness: check for self consistency
● Align all the reads back to the contigs
● Look for inconsistencies
Original Reads
Align
Assembly
Mapped Read

58. Assemble ALL the things

59. Why assemble genomes
● Produce a reference for new species
● Genomic variation can be studied by comparing
against the reference
● A wide variety of molecular biological tools become
available or more effective
● Coding sequences can be studied in the context of their
related non-coding (eg regulatory) sequences
● High level genome structure (number, arrangement of
genes and repeats) can be studied

60. Vast majority of genomes remain unsequenced
Estimated number of species ~ 9 Million
Whole genome sequences (including
incomplete drafts) ~ 3000
Estimated number of species Millions to
Billions
Genomic sequences ~ 150,000
Eukaryotes Bacteria & Archaea
Every individual has a different genome
Some diseases such as cancer give rise to massive genome level variation

62. Not just genomes
● Transcriptomes
○ One contig for every isoform
○ Do not expect uniform coverage
● Metagenomes
○ Mixture of different organisms
○ Host, bacteria, virus, fungi all at once
○ All different depths
● Metatranscriptomes
○ Combination of above!

63. Conclusions

64. Take home points
● De novo assembly is the process of reconstructing a long
sequence from many short ones
● Represented as a mathematical “overlap graph”
● Assembly is very challenging (“impossible”) because
○ sequencing bias under represents certain regions
○ Reads are short relative to genome size
○ Repeats create tangled hubs in the assembly graph
○ Sequencing errors cause detours and bubbles in the assembly graph

65. Contact
tseemann.github.io
torsten.seemann@gmail.com
@torstenseemann

66. The End