Decoding ancient Bulgarian DNA with semiconductor-based sequencing
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With the development of Next Generation Sequencing Technology (NGS), the field of hominin paleogenetics has transformed significantly from studying specific DNA markers to revealing whole genome information. However, ancient DNA of interest is usually highly fragmented so an NGS library preparation protocol optimized to capture short DNA fragments (40bp to 200bp) was developed. The improved workflow includes the use of column-based DNA purification and concentration and automated gel-based sizeselection. This workflow permitted production of “shotgun” genomic libraries from very limited input DNA (6ng to 39ng). Methods that permit the use of such low input, degraded DNA enable the partitioning of exceedingly rare samples into multiple analytical workflows. Data from two orthogonal sequencing platforms for these ancient Bulgarian samples demonstrated very similar base-substitution profiles with C>T and G>A variants accounting for ~75-80% of all SNPs called in both datasets. With such orthogonal verification, we expect to be able to reduce the false positive rate and generate a “truth” list of SNPs that will enhance our understanding of ancient population genomics and migrations. In summary, we have demonstrated a library preparation and semiconductor-based NGS workflow that is applicable for processing contaminated and degraded samples and can be used for ancient DNA research.
![Thermo Fisher Scientific • 5791 Van Allen Way • Carlsbad, CA 92008 • www.lifetechnologies.com
Yuan-Chieh Ku1, Meredith L. Carpenter2, Martin Sikora2, Hannes Schroeder3, Clarence C. Lee1, Christopher Davies1, M. Thomas P. Gilbert3,
Carlos D. Bustamante2, Gavin D. Meredith1
Thermo Fisher Scientific, South San Francisco, CA, USA; 2. Stanford University, Stanford, CA , USA; 3. Centre for GeoGenetics, Copenhagen,
Denmark.
Decoding ancient Bulgarian DNA with semiconductor-based sequencing
ABSTRACT
With the development of Next Generation
Sequencing Technology (NGS), the field of hominin
paleogenetics has transformed significantly from
studying specific DNA markers to revealing whole
genome information. However, ancient DNA of
interest is usually highly fragmented so an NGS
library preparation protocol optimized to capture
short DNA fragments (40bp to 200bp) was
developed. The improved workflow includes the
use of column-based DNA purification and
concentration and automated gel-based size-selection.
This workflow permitted production of
“shotgun” genomic libraries from very limited input
DNA (6ng to 39ng). Methods that permit the use
of such low input, degraded DNA enable the
partitioning of exceedingly rare samples into
multiple analytical workflows.
Data from two orthogonal sequencing platforms for
these ancient Bulgarian samples demonstrated
very similar base-substitution profiles with C>T and
G>A variants accounting for ~75-80% of all SNPs
called in both datasets. With such orthogonal
verification, we expect to be able to reduce the
false positive rate and generate a “truth” list of
SNPs that will enhance our understanding of
ancient population genomics and migrations. In
summary, we have demonstrated a library
preparation and semiconductor-based NGS
workflow that is applicable for processing
contaminated and degraded samples and can be
used for ancient DNA research.
Figure 1. Whole-genome in-solution
capture
INTRODUCTION
Whole-genome in-solution capture (WISC) is an
unbiased way to increase endogenous DNA
proportion in ancient DNA samples. Human
genomic “bait” libraries were created from a
modern reference individual. These bait libraries
were constructed with adapters containing T7 RNA
polymerase which can be used for in vitro
transcription to generate RNA baits covering the
entire human genome. These baits were then
hybridized to ancient DNA libraries in solution and
pulled down with magnetic streptavidin-coated
beads. Captured endogenous human DNA was
then eluted and amplified for sequencing.
RESULTS
Figure 2. Ion Torrent low input
fragment library workflow
Small amount and highly degraded
ancient DNA samples were end
repaired and purified with Zymo DNA
Clean & ConcentratorTM. These
samples were then adapter ligated
and purified with AMPure® XP beads
to remove excess adapter dimer.
Libraries were nick translated and
amplified for 8 cycles. Final libraries
were size selected for fragments
range from100bp to 400bp by Pippin
PrepTM follow by AMPure® XP beads
cleanup.
Figure 5. Mapping statistics comparisons
High quality filtered reads were mapped to the human
genome. Detected SNPs were cross-referenced with the
1000 Genomes reference panel.
Figure 4. DNA damage profiles comparison
DNA substitution patterns of (A) Illumina pre-capture,
(B) Illumina post-capture, (C) Ion Proton™ pre-capture,
(D) Ion Proton™ post-capture.
Sample
(P192-1)
Total read
number
filtered (MQ30, length >30)
Mapped % Duplicates %
Position
covered
SNPs in
1000G
Proton_precapture 312,510,164 6% 56% 528,158,981 6,981,671
Proton_postcapture 51,644,781 18% 80% 169,396,091 2,298,092
Illumina_precapture 705,234 4.3% 9% 2,248,978 30,081
Illumina_postcapture
829,256 23.2% 66% 50,003,999 67,221
CONCLUSIONS
1.We developed a library workflow specific for low input
and highly degraded ancient DNA samples on an Ion
Torrent Proton™ system.
2.Ion Proton™ and Illumina results show very similar
substitution profiles. A less biased “truth” list can be
generated by orthogonal verification.
Figure 6. Principal component analysis of
Proton pre- and post-capture runs
Detected SNPs that are overlapped with HGDP 650K
and population datasets from the Estonian Biocentre
were used for principal component analysis.
(A) Ion Proton™ pre-capture, (B) Ion Proton™ post-capture.
For Research Use Only. Not for use in diagnostic procedures.
© 2014 Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the
property of Thermo Fisher Scientific and its subsidiaries unless otherwise specified.
TaqMan is a registered trademark of Roche Molecular Systems, Inc., used under
permission and license. DNA Clean & Concentrator is a trademark of Zymo Research.
AMPure is a trademark of Beckman Coulter, Inc.
Ancient DNA
(6 to 39ng)
End repair
Adapter ligation
Nick translation
&
PCR amplification
Pippin Prep size
selection
Index2_2x150.merge_and_trim.mappedonly.rmdup.LongerThan30.rg
Single−end read length distribution
Occurences
Read length
33
43
53
63
73
83
93
103
113
123
133
143
153
163
173
183
193
203
213
0
200
400
600
800
1000
Single−end read length per strand
subplus$Length
subplus$Occurences
Occurences
Read length
33
43
53
63
73
83
93
103
113
123
133
143
153
163
173
183
193
203
213
0
100
200
300
400
500
+ strand
− strand
Index
c(0, cumsum(subplus[, mut]/sum(subplus[, mut])))
0
10
20
30
40
50
60
70
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
C>T
Read position
Cumulative frequencies
+ strand
− strand
Index
c(0, cumsum(subplus[, mut]/sum(subplus[, mut])))
0
10
20
30
40
50
60
70
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
G>A
Read position
Cumulative frequencies
+ strand
− strand
P1_ALL.rmdup.LongerThan30.rg
Single−end read length distribution
Occurences
Read length
28
38
48
58
68
78
88
98
108
118
128
138
148
158
168
178
188
198
208
218
228
238
248
258
268
0
20000
40000
60000
80000
Single−end read length per strand
subplus$Length
subplus$Occurences
Occurences
Read length
28
38
48
58
68
78
88
98
108
118
128
138
148
158
168
178
188
198
208
218
228
238
248
258
0
10000
20000
30000
40000
+ strand
− strand
Index
c(0, cumsum(subplus[, mut]/sum(subplus[, mut])))
0
10
20
30
40
50
60
70
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
C>T
Read position
Cumulative frequencies
+ strand
− strand
Index
c(0, cumsum(subplus[, mut]/sum(subplus[, mut])))
0
10
20
30
40
50
60
70
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
G>A
Read position
Cumulative frequencies
+ strand
− strand
Index2.merge_and_trim.hg19.mappedonly.rmdup.LongerThan30.rg
Single−end read length distribution
Occurences
Read length
29
39
49
59
69
79
89
99
109
119
129
139
149
159
0
500
1000
1500
2000
2500
3000
Single−end read length per strand
subplus$Length
subplus$Occurences
Occurences
Read length
29
39
49
59
69
79
89
99
109
119
129
139
149
159
0
500
1000
1500
+ strand
− strand
Index
c(0, cumsum(subplus[, mut]/sum(subplus[, mut])))
0
10
20
30
40
50
60
70
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
C>T
Read position
Cumulative frequencies
+ strand
− strand
Index
c(0, cumsum(subplus[, mut]/sum(subplus[, mut])))
0
10
20
30
40
50
60
70
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
G>A
Read position
Cumulative frequencies
+ strand
− strand
Figure 3. Read length comparison
Library size distribution of (A) Illumina pre-capture,
(B) Illumina post-capture, (C) Ion Proton™ pre-capture,
(D) Ion Proton™ post-capture.
A B
C D
A
B
C
D
A
B](https://image.slidesharecdn.com/ashg2014ancientdna-seqapproved13oct2014-141017163254-conversion-gate02/85/Decoding-ancient-Bulgarian-DNA-with-semiconductor-based-sequencing-1-320.jpg)
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Decoding ancient Bulgarian DNA with semiconductor-based sequencing
- 1. Thermo Fisher Scientific • 5791 Van Allen Way • Carlsbad, CA 92008 • www.lifetechnologies.com Yuan-Chieh Ku1, Meredith L. Carpenter2, Martin Sikora2, Hannes Schroeder3, Clarence C. Lee1, Christopher Davies1, M. Thomas P. Gilbert3, Carlos D. Bustamante2, Gavin D. Meredith1 Thermo Fisher Scientific, South San Francisco, CA, USA; 2. Stanford University, Stanford, CA , USA; 3. Centre for GeoGenetics, Copenhagen, Denmark. Decoding ancient Bulgarian DNA with semiconductor-based sequencing ABSTRACT With the development of Next Generation Sequencing Technology (NGS), the field of hominin paleogenetics has transformed significantly from studying specific DNA markers to revealing whole genome information. However, ancient DNA of interest is usually highly fragmented so an NGS library preparation protocol optimized to capture short DNA fragments (40bp to 200bp) was developed. The improved workflow includes the use of column-based DNA purification and concentration and automated gel-based size-selection. This workflow permitted production of “shotgun” genomic libraries from very limited input DNA (6ng to 39ng). Methods that permit the use of such low input, degraded DNA enable the partitioning of exceedingly rare samples into multiple analytical workflows. Data from two orthogonal sequencing platforms for these ancient Bulgarian samples demonstrated very similar base-substitution profiles with C>T and G>A variants accounting for ~75-80% of all SNPs called in both datasets. With such orthogonal verification, we expect to be able to reduce the false positive rate and generate a “truth” list of SNPs that will enhance our understanding of ancient population genomics and migrations. In summary, we have demonstrated a library preparation and semiconductor-based NGS workflow that is applicable for processing contaminated and degraded samples and can be used for ancient DNA research. Figure 1. Whole-genome in-solution capture INTRODUCTION Whole-genome in-solution capture (WISC) is an unbiased way to increase endogenous DNA proportion in ancient DNA samples. Human genomic “bait” libraries were created from a modern reference individual. These bait libraries were constructed with adapters containing T7 RNA polymerase which can be used for in vitro transcription to generate RNA baits covering the entire human genome. These baits were then hybridized to ancient DNA libraries in solution and pulled down with magnetic streptavidin-coated beads. Captured endogenous human DNA was then eluted and amplified for sequencing. RESULTS Figure 2. Ion Torrent low input fragment library workflow Small amount and highly degraded ancient DNA samples were end repaired and purified with Zymo DNA Clean & ConcentratorTM. These samples were then adapter ligated and purified with AMPure® XP beads to remove excess adapter dimer. Libraries were nick translated and amplified for 8 cycles. Final libraries were size selected for fragments range from100bp to 400bp by Pippin PrepTM follow by AMPure® XP beads cleanup. Figure 5. Mapping statistics comparisons High quality filtered reads were mapped to the human genome. Detected SNPs were cross-referenced with the 1000 Genomes reference panel. Figure 4. DNA damage profiles comparison DNA substitution patterns of (A) Illumina pre-capture, (B) Illumina post-capture, (C) Ion Proton™ pre-capture, (D) Ion Proton™ post-capture. Sample (P192-1) Total read number filtered (MQ30, length >30) Mapped % Duplicates % Position covered SNPs in 1000G Proton_precapture 312,510,164 6% 56% 528,158,981 6,981,671 Proton_postcapture 51,644,781 18% 80% 169,396,091 2,298,092 Illumina_precapture 705,234 4.3% 9% 2,248,978 30,081 Illumina_postcapture 829,256 23.2% 66% 50,003,999 67,221 CONCLUSIONS 1.We developed a library workflow specific for low input and highly degraded ancient DNA samples on an Ion Torrent Proton™ system. 2.Ion Proton™ and Illumina results show very similar substitution profiles. A less biased “truth” list can be generated by orthogonal verification. Figure 6. Principal component analysis of Proton pre- and post-capture runs Detected SNPs that are overlapped with HGDP 650K and population datasets from the Estonian Biocentre were used for principal component analysis. (A) Ion Proton™ pre-capture, (B) Ion Proton™ post-capture. For Research Use Only. Not for use in diagnostic procedures. © 2014 Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the property of Thermo Fisher Scientific and its subsidiaries unless otherwise specified. TaqMan is a registered trademark of Roche Molecular Systems, Inc., used under permission and license. DNA Clean & Concentrator is a trademark of Zymo Research. AMPure is a trademark of Beckman Coulter, Inc. Ancient DNA (6 to 39ng) End repair Adapter ligation Nick translation & PCR amplification Pippin Prep size selection Index2_2x150.merge_and_trim.mappedonly.rmdup.LongerThan30.rg Single−end read length distribution Occurences Read length 33 43 53 63 73 83 93 103 113 123 133 143 153 163 173 183 193 203 213 0 200 400 600 800 1000 Single−end read length per strand subplus$Length subplus$Occurences Occurences Read length 33 43 53 63 73 83 93 103 113 123 133 143 153 163 173 183 193 203 213 0 100 200 300 400 500 + strand − strand Index c(0, cumsum(subplus[, mut]/sum(subplus[, mut]))) 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 C>T Read position Cumulative frequencies + strand − strand Index c(0, cumsum(subplus[, mut]/sum(subplus[, mut]))) 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 G>A Read position Cumulative frequencies + strand − strand P1_ALL.rmdup.LongerThan30.rg Single−end read length distribution Occurences Read length 28 38 48 58 68 78 88 98 108 118 128 138 148 158 168 178 188 198 208 218 228 238 248 258 268 0 20000 40000 60000 80000 Single−end read length per strand subplus$Length subplus$Occurences Occurences Read length 28 38 48 58 68 78 88 98 108 118 128 138 148 158 168 178 188 198 208 218 228 238 248 258 0 10000 20000 30000 40000 + strand − strand Index c(0, cumsum(subplus[, mut]/sum(subplus[, mut]))) 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 C>T Read position Cumulative frequencies + strand − strand Index c(0, cumsum(subplus[, mut]/sum(subplus[, mut]))) 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 G>A Read position Cumulative frequencies + strand − strand Index2.merge_and_trim.hg19.mappedonly.rmdup.LongerThan30.rg Single−end read length distribution Occurences Read length 29 39 49 59 69 79 89 99 109 119 129 139 149 159 0 500 1000 1500 2000 2500 3000 Single−end read length per strand subplus$Length subplus$Occurences Occurences Read length 29 39 49 59 69 79 89 99 109 119 129 139 149 159 0 500 1000 1500 + strand − strand Index c(0, cumsum(subplus[, mut]/sum(subplus[, mut]))) 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 C>T Read position Cumulative frequencies + strand − strand Index c(0, cumsum(subplus[, mut]/sum(subplus[, mut]))) 0 10 20 30 40 50 60 70 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 G>A Read position Cumulative frequencies + strand − strand Figure 3. Read length comparison Library size distribution of (A) Illumina pre-capture, (B) Illumina post-capture, (C) Ion Proton™ pre-capture, (D) Ion Proton™ post-capture. A B C D A B C D A B