Xin Zhou - Saturday Closing Plenary

Taxon diversity analysis for bulk insect samples using
Illumina Hi-seq platform

Xin ZHOU, Shanlin LIU, Yiyuan LI,
Qing YANG, and Xu SU

Department of Science and Technology
Environmental Genomics Research Group
BGI, China

Adelaide, Australia, 3 December 2011

Problem Solutions?

Opt.1: ......zzzzZZZZZ

Opt.2: morph sorting  indiv. ID  …  Opt.1

Opt.3: morph sorting  indiv. barcoding  …  Opt.1

Opt.4: grinding up  NGS  CLUSTERING/BLAST
 DIVERSITY!

Zhou et al. 2011, 4th International Barcode of Life Conference

Environmental barcoding of bulk insects

 aquatic insects
 mini-barcode (130bp)
 454

 bat diet (insects)
 COI fragment, 157 bp
 454

Biodiversity soup: metabarcoding of arthropods  Malaise trap (insects)
for rapid biodiversity assessment and  COI fragment, ~400 bp
biomonitoring, Yu D.W. et.al., in review  454


Major NGS platforms applicable in environmental barcoding

Requirement
Read Data/run
NGS platforms Run time of library
length (GB)
construction
454 platform
~400bp 0.7 23 hr. Yes
(GS FLX Titanium XL+)
Illumina platform 150bp
600 14 d. Yes
(Hi-Seq 2000) PE reads
Illumina platform 150bp
2 27 hr. Yes
(Mi-Seq) PE reads

Ion Torrent 200bp ~1 3.5 hr. No

Illumina Hi-Seq  higher through-put
 less $ / bp
 increasing reading length
 variety of bioinformatics tools available from genomic
pipelines

Sequencing capacity at BGI

• 28 Illumina GAIIx Data production:
• 137 Illumina Hi-Seq2000 • 100 Gb / day (2009)
• 25 Life Tech SOLiD 4 • >5 Tb / day (end of 2010)
• 16 ABI 3730XL • >1500X human genome / day
• 110 MegaBACEs
• 2 Illumina iScan
• 1 Roche 454
• 1 Ion Torrent
• 1 Illumina Mi-Seq


What I am NOT going to talk about:
• Primer optimization
• Systematic comparisons of NGS platforms
• Quantitative diversity analysis

What I AM going to talk about:
• Can Illumina NGS be used in diversity analysis?


Can Illumina NGS be used in diversity analysis?

 Sequencing error rate
 Read-length


Sequencing error rate

 No indel issue in homopolymers
 Sequencing quality keeps increasing
 Rare nucleotide error can be easily
corrected by:
Recent improvement in sequencing quality
 increasing sequencing depth using Illumina’s V3 chemical
(even at 100 bp, only about 10% of the base callings has error
 pair-end (PE) sequencing rate >1%)

 setting stringent matching criteria in
150bp
the overlapping fragment by allowing
only >99% identity 150bp

Insert-size
250nt

PE sequencing enables forming
sequence contigs


Read length
150bp

150bp

Insert-size
250nt
 Read length keeps increasing
150PE enables contig read of 250bp
 Short-gun reads can be further assembled
into longer fragments (“short-gun” assembly
strategy used in genome sequencing
projects)

Option of scaffold assembly


Illumina environmental barcoding
Illumina
e-barcoding

PCR based PCR free
Lib1 (658bp, 150PE) Lib2 (200bp, 150PE)

Full length COI COI amplicons
barcode PE shotgun PE Mitochondrial
sequencing sequencing shotgun PE
sequencing

Full length COI Full length COI
without PCR bias


Approach #1: PCR-based
Sample information
XSBN
Mock
(provided by Yu et al.)
# Specimens 23 292
# Haplotypes (2%) 12 230
Soup protocol DNA extracted individually and mixed for PCR
PCR primers LepF1/LepR1 Customized
Sequence length 658 bp 700 bp
Sequencing
Full length (658bp) + Short-gun library (~200bp)
library details
Sequencing
150PE
protocol


Pre-analysis data filtering

Lib 1 Mock XSBN
Raw data 1.67G 4.04G
Filtering adapter 1.60G 1.28G
High quality (Q20) 0.35G 0.50G
# Reads
1,081,997 1,150,477
(Primer removed)
# Unique reads
36,618 45,444
(Abundance > 1)


OTU filtering workflow

Unique OTU Alignment Remove Compared
reads cluster Chimera to reads
(abunda (98%) of Lib 2
nce > 1)

Mock 36,618 784 490 119 44

XSBN 45,444 4,189 3887 403 399


Sanger Reference
Blast at 100% identity
Results
NGS OTUs

Mock 4 8 36 LepF1/R1

Customized
XSBN 32 198 197 primers


Sanger Reference
Mock
NGS OTUs

“False positive”?
31 can be found in
False negative our total sample,
from which our
mock samples
Not found in raw were assembled
data (likely due
to primer failure)
4 8 36

5 likely to be PCR
errors


Sanger Reference
XSBN Cross-sample
NGS OTUs contamination?

17 not found in raw
data (primer failure)

Mean + SE
32 198 197
(group1) (group2)
15 were lost in
data filtering


Sanger Reference

NGS OTUs

Significantly less false
positives

after removal of sequences
with abundance <10

32 198 197 49 181 84

Slight drop of true
positives


What’s next?
Illumina
e-barcoding

 Obtaining full-length barcodes via short-gun reads assembly
(new program in development – “SOAPbarcode”)
 New algorithm to filter out false positive OTUs

Approach #2: PCR-free method

Total MT isolation
Individual
&
barcoding
DNA extraction

Shotgun sequencing

Reference Reference
based method independent method


Building reference library: individual barcoding

1. 89 individuals;
2. 84 reference barcodes;
3. 39 OTUs (2%);

Taxon group # OTUs
Lepidoptera 25
Diptera 7
Hemiptera 4
Hymenoptera 2
Psocoptera 1
Total 39


Total MT isolation
& DNA extraction

Sample Total MT MT DNA
mixture isolation extraction


Shotgun sequencing
Insert size: 200bp；
Read length: 100bp PE;

Percentage of
base pairs
Q20
96.2%
(Sequencing error rate < 1%)
Q30
92.9%
(Sequencing error rate < 0.1%)
GC content 38.0%


Pre-analysis

Data filtering:
1. Adaptor contamination removal;
2. Quality control:
in each read, only allowing <10bp with seq. error rate >1%

Raw data 2.45G
After filtering 2.20G
Ratio of high
89.91%
quality reads


Method 1: Reference based
Blast reads to reference barcodes,
confident identification is made only when:
1. Best BLAST hit >98% identity;
2. Reference coverage > 90%;

Taxon groups # OTUs
Reference 1 Coverage: 100%
Lepidoptera 20
Correct Diptera 2
mapping Hemiptera 3
Psocoptera 1
Total 26
Reference 2Not found 13 Coverage: 30%
Incorrect
mapping

Potential sources of failure in detecting taxa

Taxon specific
or
Bio-mass
(size & number)


Failures in taxon detection

Taxon bias?

Taxon groups # Total # OTUs
undetected OTUs missing
Lepidoptera 25 5
Diptera 7 5
Hymenoptera 2 2
Hemiptera 4 1
Psocoptera 1 0
Total 39 13


Failures in taxon detection

OR bio-mass (body size, # individuals)?

Readily detected Missing
Average length> 5mm Average length < 5mm


Method 2: Reference independent
(Will we be able to identify diversity without reference MT genomes
for the targeted species?)

Workflow:
1. Assembly of COI gene using genome
assembly program (SOAPdenovo);

2. Annotation using ~240 MT genomes
downloaded from Genbank;


PCR-Free reference-independent: results

23/31 falling in standard COI barcode
region (mostly >600 bp);

1 of 23 is not in our reference barcodes;
(Insecta; Lepidoptera; Pyralidae);

Multiple genes obtained simultaneously;
1 nearly complete mitochondrial genome (~15k bp);
3 fragments >6000 bp;


Reference independent

23/31 falling in standard COI barcode
1 of 23 was not presented in our reference barcodes;
region (mostly >600 bp);
(Insecta; Lepidoptera; Pyralidae);

Number of individuals we collected
5 individuals failed in Sanger sequencing
89 individuals

3 OTUs not detected in reference
Barcode references
independent method because: 39 OTUs (84 individuals)

References based
(1) sequencing depth is too low 26 OTUs

(<10X) to allow for reliable References independent
23 OTUs
assembly
(2) relatively small body-size


PCR-free method

Multiple MT genes obtained simultaneously

Gene Number
ATP6 29
ATP8 4
COX1 31
COX2 33
COX3 31
CYTB 31
ND1 35
ND2 34
ND3 24
ND4 30
ND4L 16
ND5 30
ND6 24 Zhou et al. 2011, 4th International Barcode of Life Conference

PCR-free method

1 nearly complete mitochondrial genome (~15k bp);
3 fragments longer than 6k bp;

Barcode region

What’s next?
Currently:
 MT DNA 5-10% after isolation;
 Non-targeting DNA affects MT assembly (e.g.,
bacteria & genomic DNA);
 Taxonomic/biomass bias

Potential solutions:
1. Wet-lab protocol optimization
 Pre-sorting insects by body-size
 Alternative MT isolation methods

2. Increase sequencing depth


Conclusions
 Illumina Hi-Seq delivers compatible performance
as other NGS platforms in analyzing bulk insect
samples, with potential advantages in achieving
higher sensitivity at lower cost;
 Deep sequencing capacity enables a novel PCR-
free approach, which may eventually solve biases
caused by DNA amplification;
 It shares issues with other NGS platforms (non-
quantitative, inflation of OTUs, etc.)
 Methodology optimization is much needed in
many details of the pipeline;
 Collaborative and synergistic efforts made by the
community would greatly advance the progress.


Acknowledgements

Funder:

Collaborators: Douglas W. Yu
Kunming Institute of Zoology, Chinese Academy of Sciences

Mehrdad Hajibabaei, Shadi Shokralla
University of Guelph

Owain Edwards
CSIRO Ecosystem Sciences

LU Jianliang
WU Qiong
AN Sainan
ZHOU Yizhuang
ZHAO Jing


Thanks for your
attention!

36

Xin Zhou - Saturday Closing Plenary

Xin Zhou - Saturday Closing Plenary

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