St. Petersburg Academic University Anton Bankevich Sergey Nurk Dmitry Antipov Alexey Gurevich

36 Pages·2012·8.46 MB·English

by  Glenn Tesler

University of California,
St. Petersburg Academic University
San Diego
Anton Bankevich
Pavel Pevzner
Sergey Nurk
Son Pham
Dmitry Antipov
Glenn Tesler
Alexey Gurevich
Mikhail Dvorkin
Alexander Kulikov University of South Carolina
Valery Lesin
Max Alekseyev
Srergey Nikolenko
Andrey Prjibelski
Funding
Alexey Pyshkin
Alexander Sirotkin Russian Federation grant
Nikolay Vyahhi 11.G34.31.0018

NIH 3P41RR024851-02S1
3
Genome sequencing
 
◦  Conventional
◦  Metagenomics
◦  Single Cell
De Bruijn graphs and SPAdes
 
Results on E. coli and an uncultivated marine genome
 
5
Multiple (Unsequenced) Genome Copies
Read Generation
Reads
Fragment Assembly
Sequenced Genome
GGCATGCGTCAGAAACTATCATAGCTAGATCGTACGTAGCC
… …
Traditional microbial genome sequencing requires isolating a pure
 
strain and reproducing it in a ‘culture’ under controlled laboratory
conditions. But >99% of bacteria cannot be cultured.
Metagenomics enables studies of organisms not easily cultured in a
 
laboratory. It uses collective sequencing of non-identical cells.
Until recently, metagenomics was the only option for studies of
 
microbial communities. However, metagenomics provides
information about only a few genes (across many species).
gene 1 gene 2 gene 3
17
Traditional microbial genome sequencing requires isolating a pure
 
strain and reproducing it in a ‘culture’ under controlled laboratory
conditions. But >99% of bacteria cannot be cultured.
Metagenomics enables studies of organisms not easily cultured in a
 
laboratory. It uses collective sequencing of non-identical cells.
Until recently, metagenomics was the only option for studies of
 
microbial communities. However, metagenomics provides
information about only a few genes (across many species).
18
Traditional microbial genome sequencing requires isolating a pure
 
strain and reproducing it in a ‘culture’ under controlled laboratory
conditions. But >99% of bacteria cannot be cultured.
Metagenomics enables studies of organisms not easily cultured in a
 
laboratory. It uses collective sequencing of non-identical cells.
Single Cell Bacterial Genomics: Complementing gene-centric
 
metagenomics data with whole-genome assembly of uncultivated
organisms.
1000s of genes sequenced from a single cell
19
1. Random hexamer primers
2. Phi29 DNA polymerase Strand
displacing
3. Isothermal reaction (30°C)
Genomic DNA
F.B. Dean, J.R. Nelson, T.L. Giesler, R.S. Lasken (2001). Genome Res. 11:1095-9
F.B. Dean, S. Hosono, L. Fang, et al. (2002). PNAS 99:5261-6
Roger Lasken’s lab developed Multiple Displacement Amplification (MDA).
 
More effective than PCR for amplification of a single cell.
 
TempliPhi and GenomiPhi (GE Healthcare) and REPLI-g (Qiagen).
 
REPLI-g: fragments ~ 2 – 100 kb; usually > 10 kb on average.
 
24
  Lander-Waterman model predicts ~15x coverage needed for complete E. coli assembly.
  Assumes uniform coverage; error-free reads; and no repeats in genome.
  For our single cell E. coli assembly, 600x average coverage still has some gaps since
there are positions with no reads.
28
A cutoff threshold will eliminate about 25% of valid data in the single cell
case, whereas it eliminates noise in the normal multicell case.

Chitsaz, et al., Nat. Biotechnol. (2011).
29
E. coli, Lane 1 E. coli, Lane 2
200000 250000
180000
s s
r 160000 r 200000
ai ai
p 140000 p

d d
a 120000 a 150000
e e
r 100000 r
f f
o o
80000 100000
r r
e e
b 60000 b
m m
u 40000 u 50000
N N
20000
0 0
0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Insert length Insert length
E. coli, Lane 3 E. coli, Lane 4
250000 250000
s s
r 200000 r 200000
ai ai
p p

d d
a 150000 a 150000
e e
r r

f f
o o
100000 100000
r r
e e
b b
m m
u 50000 u 50000
N N
0 0
0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Insert length Insert length
E. coli, Lane 6 E. coli, Lane 7
180000 180000
160000 160000
s s
r 140000 r 140000
ai ai
p p
120000 120000
d d
a a
e 100000 e 100000
r r

f f
o 80000 o 80000

r r
e e
60000 60000
b b
m m
u 40000 u 40000
N N
20000 20000
0 0
0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Insert length Insert length
Normal Single Cell
E. coli, Lane 8 E. coli, Lane normal E. coli, Lane 1 E. coli, Lane 2
160000 500000 200000 250000
450000 180000
140000
s s s s
r r 400000 r 160000 r 200000
ai 120000 ai ai ai
p p 350000 p 140000 p
d 100000 d d d
a a 300000 a 120000 a 150000
e e e e
of r 80000 of r 250000 of r 100000 of r
r 60000 r 200000 r 80000 r 100000
e e e e
b b 150000 b 60000 b
m 40000 m m m
u u 100000 u 40000 u 50000
N N N N
20000
50000 20000
0 0 0 0
0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Insert length Insert length Insert length Insert length
E. coli, Lane 3 E. coli, Lane 4
250000 250000
s s
r 200000 r 200000
Chitsaz, et al., Nat. Biotechnol. (2011). ai ai
p p

d d
a 150000 a 150000
e 30 e
r r

f f
o o
100000 100000
r r
e e
b b
m m
u 50000 u 50000
N N
0 0
0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Insert length Insert length
E. coli, Lane 6 E. coli, Lane 7
180000 180000
160000 160000
s s
r 140000 r 140000
ai ai
p p
120000 120000
d d
a a
e 100000 e 100000
r r

f f
o 80000 o 80000

r r
e e
60000 60000
b b
m m
u 40000 u 40000
N N
20000 20000
0 0
0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Insert length Insert length
E. coli, Lane 8 E. coli, Lane normal
160000 500000
450000
140000
s s
r r 400000
ai 120000 ai
p p 350000

d 100000 d
a a 300000
e e
r 80000 r 250000
f f
o o
200000
r 60000 r
e e
b b 150000
m m
40000
u u 100000
N N
20000
50000
0 0
0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400
Insert length Insert length