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ISSN 2469-9853
Journal of Next Generation Sequencing & Applications
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Identifying Polymorphisms in the Alzheimer's Related APP Gene Using the Minion Sequencer

Keeley Brookes*, Tulsi Patel, Gabriela Zapata-Erazo, Imelda Barber, Anne Braae, Naomi Clement, Tamar Guetta-Baranes, Sally Chappell and Kevin Morgan

Human Genetics Group, School of Life Sciences, University of Nottingham, Queen’s Medical Centre, Nottingham, UK

Corresponding Author:
Keeley Brookes
Human Genetics Group, School of Life Sciences
University of Nottingham, Queen’s Medical Centre, Nottingham, UK
Tel: +44115 823 0141
E-mail: [email protected]

Received date: March 31, 2016; Accepted date: May 11, 2016; Published date: May 13, 2016

Citation: Brookes K, Patel T, Zapata-Erazo G, Barber I, Braae A, et al. (2016) Identifying Polymorphisms in the Alzheimer's Related APP Gene Using the Minion Sequencer. Next Generat Sequenc & Applic 3:125. doi:10.4172/2469-9853.1000125

Copyright: © 2016 Brookes K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

The MinION is a bench top sequencer by Oxford nanopore technologies (ONT) that allows long reads of DNA sequence. Few studies have tested whether polymorphisms can be detected using this device. Several polymorphisms within the APP gene were used to test this capability. Library preparation and sequencing were performed using standard ONT protocols for samples harbouring five different mutations. Alignments to the reference sequence were analysed in MinoTour and basecalls were manually investigated using proportion of reference calls between samples to identify the variants. MinoTour’s algorithm for variant detection was unable to identify the polymorphisms due to high base calling error rate. By calculating the difference in reference basecall proportions along the amplicon, it was possible to identify the polymorphisms above a Bonferroni-corrected threshold (p<1 × 10-4). The MinION has potential for polymorphism detection when comparing samples; however careful interpretation is needed as high base calling error rates can mask the presence of polymorphisms.

Keywords

Nanopore technology; MinION; Sequencing; Polymorphism detection; Deletion

Introduction

Sequencing of DNA samples for genetic analysis has become common practice in molecular diagnostics. Over time, the cost and duration taken to sequence DNA has reduced but at the loss of read length from up to 1000 bp in 1st generation sequencing (Sanger) to only 200 bp reads in 2nd generation platforms (e.g. Illumina). The reduction in read length requires a greater depth of coverage to enable genome assembly. In addition, the inability to produce long reads results in reduced capability to observe tandem repeat polymorphisms and determine cis haplotypes. Although costs are continually decreasing, sequencing of entire genomes is still expensive.

Now 3rd generation sequencing could potentially rival standard platforms with use of nanopore technology to sequence DNA quickly, cheaply and with read lengths likely extending more than kilobases in size. The frontrunner for this technology has been Oxford nanopore technologies (ONT) in the form of its MinION device. The MinION, part of ONT’s arsenal of bench top sequencers, is being tested in a small number of laboratories worldwide as part of its MinION access programme (MAP). The device, only the size of a large memory stick, offers relatively cheap in-house sequencing with real-time data production. DNA tethered to motor proteins and adaptor sequencers is passed through a biological membrane pore and specific ion current changes for each base are detected. This allows base calls to be made using Metrichor software housed in ONT’s cloud-based server.

Several publications have documented the MinION’s sequencing ability and whilst the majority focus on accuracy of sequencing small bacterial genomes, few have looked at the ability of the MinION to detect polymorphisms within the genome [1]. A high error rate in base calling is observed with the device compared to current platforms, largely due to the influence of simultaneous, multiple adjacent nucleotides on the ion current, amongst other physical attributes such as the enzymes driving DNA through the pores too quickly for a current to be detected [2,3]. As a result, the presence of polymorphisms in the sequence is difficult to observe. The detection of structural variations <300 bp in size, with 500x amplicon coverage [4]. However, the detection of single nucleotide polymorphisms (SNPs) appears more problematic due to the high base calling error. The human CYP2D6, HLA-A and HLA-B loci to determine cis-haplotypes using the long sequence reads producible by the MinION [5]. Error rates were too high for variant calling using conventional tools such as GATK, therefore polymorphisms were simply identified by classing variant basecalls that occurred in more than a third of total reads as a true SNP.

Algorithms that are more complex have been used to control sequencing error and identify novel polymorphisms in comparison to a reference sequence. The M13mp18 phage genome and aligned basecalls to a reference sequence with computationally generated variation in an effort to detect variants with the MinION [6]. The algorithm was able to detect variations with an optimal F-score, 97% recall and precision using only 60 times coverage in order to call substitutions at 1% frequency. The increase in frequency of substitutions along the reference sequence reduced variant detection accuracy; likely due to the difficulty in aligning the experimental data to the mutated reference sequence. Despite the high sequencing error rate observed, theoretically SNPs could be readily identified.

Similarly the PoreSeq algorithm which considers ion current information using a statistical model of the underlying physical system, a source of error generation in basecalling, to increase sequencing accuracy [7]. PoreSeq was also examined for its ability to detect variants by altering the reference sequence and computing likelihood scores of wild type and mutant sequences. When the observed likelihood score was greater for the correct base than the altered reference base, a correct call was made. PoreSeq was able to detect variants even at low sequence coverage.

In this investigation, several polymorphisms, two rare variants (including one novel SNP) and two common variants within the APP gene were used to test the device’s ability to detect variation despite the high reported error rate (Figure 1). Amplicons known to be wild type, heterozygous or homozygous for these polymorphisms (validated by Sanger method) were sequenced using the MinION to determine whether the polymorphisms could be detected. Variations in the amplicons were analysed using the MinoTour tool detection algorithms [8]. In addition, as an alternative to algorithm detection, the proportions of reference basecalls at each position along the amplicon were compared between samples in order to test the hypothesis that the error rate between samples would be similar and therefore any detections for deviations in the proportion of reference basecalls between samples would indicate points of genetic variation.

Results

Five polymorphisms located within the APP gene (Figure 1A) were sequenced in homozygous and heterozygous samples on the MinION using three different amplicons. Wild type samples for the respective polymorphisms were also sequenced as a comparison with all genotypes. All samples were previously Sanger sequencing confirming their genotype and indicating no DNA variations other than the polymorphisms under investigation. Primers designed to amplify these regions with standard PCR protocol can be found in Figure 1B.

next-generation-sequencing-applications-Primers-designed

Figure 1: A) Schematic of the APP gene and location of the polymorphisms sequenced using the MinION. B) Primers designed for PCR amplification and sequencing of the APP polymorphisms (MAF from 1000 Genomes).

MinoTour generates a number of descriptives per sequencing run, summarised in Table 1. The number of total reads obtained from the data included all reads of the template, complimentary and reads with both template and complimentary strands (2d). Error rate and distribution across the data suggests that error rate in basecalling is not significantly different between samples of the same amplicon, and are therefore comparable. Later versions of the library preparation kits used to sequence amplicons harbouring the common SNP’s rs2830088 and rs2830051 show an improved error rate in basecalling. Kolmogorov- Smirnov (KS) tests suggest that the error rates of the samples are normally distributed, with a right-handed skew. Despite the range in clustering denoted by Kurtosis scores, all show a narrow clustering of data points about the mean. Interestingly, increasing the number of reads generated does not seem to greatly improve the error rate in basecalling, indicated by a non-significant Pearson’s correlation for both number of Total Reads/2d Reads and error rates (p=0.61 and p=0.97 respectively), however this would need further specific investigation to confirm this.

  Wildtype rs367709245 rs63750066 rs63749964 rs2830088 rs2830051
Major Het Minor Major Het Minor
Total Reads Generated 12562 1301 8223 1194 2783 2648 19630 5031 5028 5054
Total 2d Reads 10834 770 6779 969 2082 2002 16169 2019 2021 2109
Average basecalls per position 11654.5 680.1 6885.3 955.8 1910.2 1849.7 14973.3 1665.7 1252.4 1844.4
Average % Error Rate (SD) 20.1 25.6 22.9 24.5 21.3 20.2 20.3 16.1 15.2 17.4
-11.6 -12.5 -12.6 -13.1 -11.1 -10.9 -11.2 -6.9 -7 -8.2
KS Test 2.2 1.4 1.7 1.8 1.1 1.7 1.6 2.1 2.8 2.7
Skewness 1.1 0.5 0.8 0.6 0.5 1.2 2.1 1.7 2 2.1
Kurtosis 2.5 0.1 0.9 0.1 1.7 4.4 14.2 5.7 6.8 7.7

Table 1: Descriptive summary of sequencing reads for the five polymorphisms tested. Two-directional (2d) reads were used for all analyses due to greater sequence accuracy. The average error rates were similar across all samples, however were reduced for the latter runs prepared using the latest library kit. Higher read generation did not appear to improve error rates. Skewness, a kurtosis and Kolmogorov-Smirnov (KS) test were performed to determine the distribution of the data and suggest that the error rates across sequencing of the same amplicons are not significantly.

Deletion variant (rs367709245) detection

Analysis using the MinoTour tool yielded no consensus variants (see methods for description) in samples containing the deletion (rs367709245) or rare SNPs (rs63750066, rs63749964). This implies that the heterozygous minor alleles, expected in 50% of the basecalls, were not observed at a high enough frequency to be called as consensus variants.

MinoTour detected four single base deletion variations in the sample containing the 6 bp deletion; none of these were within the rs367709245 deletion region under study (Table 2). However all variants coincided with a mononucleotide run in the sequence. Although the 6 bp deletion was not detected in the sample by the MinoTour algorithm, visualisation of base coverage across the amplicon indicated an increase in proportion of deletion calls in the heterozygous sample compared to wild type within the deletion region (Figure 2). This observation suggests that comparing the difference in proportion of alleles between the samples could lead to detection of polymorphisms against the background error rate, which would be similar across all samples.

  Wildtype rs367709245 rs63750066 rs36750064
Ref Seq Ref Pos Deletion calls Total Read Percentage of Reads called as Deletion Deletion Calls Total
Read
Percentage of Reads called as Deletion Deletion Calls Total Read Percentage of Read called as Deletion Deletion Calls Total Read Percentage of Reads called as Deletion
T 35 6561 11064 59.3 281 599 46.9 3333 6429 51.8 431 884 48.8
T 366 5604 11425 49.1 287 659 43.6 2933 6698 43.8 413 923 44.7
T 397 5390 11341 47.5 - - - 3056 6639 46 407 913 44.6
A 398 7877 11340 69.5 395 654 60.4 4419 6639 66.6 593 913 65
A 399 5398 11341 47.6 288 654 44 3261 6639 49.1 440 913 48.2

Table 2: Potential deletion variations within the sequences amplified. The locations of these variations do not coincide with the position of the deletion polymorphism rs367709245, which resides in the heterozygous sample.

next-generation-sequencing-applications-Scaled-proportions

Figure 2: A) Scaled proportions of calls in the 2d reads of the 6bp deletion region colour coded for each call type at those positions. The graphic indicates that there is a higher proportion of deletion calls (black) in the heterozygous sample (right) than in the wild type (left). B) Scaled proportions of deletion calls from 2d run analysis of the wild-type (left) and heterozygous carrier of the 6bp deletion (right). Although proportions of deletion calls are generally higher in the heterozygous carrier, the patterns of deletion calls are similar between the two samples. However, within the 6bp deletion region (red) there is a substantial increase in deletion calls as would be expected.

The proportion of reference basecalls was calculated from the 2d reads provided for each position along the amplicon for the wild type sample and the rs367709245 6 bp deletion sample. The porportions were then compared between the two samples resulting in a percentage difference in reference base calls for each position. Percentage deviations ranged from 0% indicating a similar reference basecall rate between samples to 22% indicating significant deviations and therefore a potential polymorphism occurring in one of the samples. Four positions along the amplicon indicated a percentage difference of >20%, three of which were located within the deletion region of the polymorphism, the fourth was located at position 30. The base at amplicon position 30 was set between two mononucleotide runs in the sequence and therefore likely due to ‘slippage’ which is unsurprising. In addition to the three positions of high percentage difference already mentioned, the other three positions of the 6 bp deletion also displayed high percentage differences in the proportion of reference basecalls between the wild type sample and the deletion sample. The average percentage difference of reference basecalls within the 6 bp deletion region was 18.2%, with the average of the entire amplicon at 5.4% (Figure 3A) indicating a high level of difference between the two samples in this region. Proportions of reference calls at this position were subjected to Chi-square (χ2) tests, the results indicated that the proportion of reference base calls at these positions were significantly different when corrected for largest (482 bp) amplicon (p value <1 × 10-4) for 5 positions out of the 6 containing the deletion. Conversely there was also an increase in deletion calls made at these positions with an average increase of 13.3% in deletion call rate with the rs367709245 heterozygous sample.

Rare SNP (rs63750066, rs63749964) detection

Two rare SNP variants were located within the same amplicon as the deletion and two samples heterozygous for these SNPs (rs63750066, rs63749964) were also sequenced. MinoTour algorithms identified numerous potential variants in the samples heterozygous for SNPs rs63750066 and rs63749964 (Table 3). This suggested that several SNPs existed within the amplicon sequences, however as many of these variants were also found in the wild type sample; they are likely to be false positives. Indeed sequencing of the samples using Sanger sequencing confirmed this was the case. Although the rs63750066 and rs63749964 polymorphisms were amongst these, the background of multiple potential variants renders it difficult to distinguish the correct polymorphism due to the high rate of sequencing error.

    Wildtype rs367709245 rs63750066 rs63749964
Ref Seq Ref Pos A T G C A T G C A T G C A T G C
T 34 722 5798 891 236 48 304 63 31 413 3349 658 286        
T 35 200 3271 546 486 7 212 58 41 108 2098 502 388        
T 37                 66 2999 666 468 9 415 103 78
A 41         320 13 42 88                
T 44                         28 576 129 96
C 56 89 1370 1217 8126 19 72 99 412 96 852 890 4404 19 106 146 604
T 112         21 425 13 159 191 4018 116 1714 29 521 32 294
A 127                         614 62 72 152
T 134 62 6156 459 1943 5 337 28 137                
G 190 627 243 7281 1476 42 28 361 91 357 143 3807 1261 59 23 547 175
G 201 1462 383 7648 1229 109 21 387 79 913 168 4420 779 136 21 588 118
G 202 832 459 7821 1692 63 28 428 114 487 222 4446 1144 64 45 601 167
G 205         19 444 93 77                
T 226                 216 3605 823 393        
G 228                 1256 152 3238 396        
T 241                         32 472 250 45
C 242                         88 34 143 568
T 244                         60 614 170 34
C 245                         37 94 67 455
C 247 163 880 1316 7362                 20 80 151 571
T 279                         28 616 189 76
C 281 175 103 3318 7727 16 12 192 437 128 63 1947 4547 13 12 267 619
T 284 134 8601 1987 567                        
G 292 607 269 6188 1801 35 24 361 112                
G 307 454 137 7292 1937         339 92 3990 1285 43 9 561 193
A 309 6900 120 221 1794         3760 72 189 1293        
A 349                 3170 327 175 747        
T 362 148 6778 1676 449         113 3129 1002 273 18 454 149 48
T 366 116 3708 1027 970 7 222 61 82 100 2413 555 697 15 312 83 100
T 367 258 5306 625 1337 22 320 31 100 353 3146 501 774 35 421 65 112
T 370 619 7284 1146 709         600 3638 767 473 68 528 116 73
T 373 191 6751 1227 869 17 263 73 88 116 2836 920 585 23 359 114 85
A 374         326 6 65 93 3655 55 665 772 461 6 120 106
T 397 206 4514 625 606                        
A 398 1580 480 639 764 129 30 41 59 960 234 482 544 149 37 66 68
A 399 3945 622 681 695 224 33 48 61 1988 363 449 578 300 37 63 73
G 431 1132 496 8003 921                        
T 468         49 304 94 12 574 3149 869 141 80 376 106 23

Table 3: Potential variants detected by MinoTour across the different sample sequencing reads of each amplicon. Although several possible variants were observed due to their frequency occuring 2sd from the error mean, the only two real varaints are highlighted in red.

Percentage difference in homology to the reference sequence between wild type and heterozygote samples for rs63750066 was plotted along the amplicon (Figure 3B). Proportions of reference basecalls differed up to 16.4% with a single position displaying a difference of 39.9%. This position coincided with the position of the SNP (position 228), and was clearly above the average percentage difference (from wild type) for that sample (3.1%). A Chi-square test on this data indicated a highly significant signal with p value <1 × 10-4. This difference in the proportion of reference calls was mirrored by an increase in the proportion of the minor allele (A) occurring at this position of 16.4%, the largest increase in allele proportions across the amplicon.

next-generation-sequencing-applications-Red-boxes

Figure 3: Graph for the percentage difference of reference base call proportions across the amplicon between the wild type and heterozygous samples for rs367709245 (A), rs63750066 (B) and rs63749964 (C). Red boxes indicate the percentage difference peaks that correspond to the location of the known polymorphisms along the amplicon.

Common SNP (rs2830088 and rs2830051) detection

Two further amplicons harbouring common SNPs were analysed using the MinION. For each amplicon a sample homozygous for the major allele, heterozygous and homozygous for the minor allele were sequenced and analysed with the MinoTour Tool and by comparison of the proportion of reference basecalls between samples.

The MinoTour Tool algorithm was unable to detect consensus variants for either SNP in the heterozygous samples; however, consensus variants were detected for both homozygous minor allele samples and corresponded to the SNPs in question. In addition both homozygous and heterozygous samples yielded several potential variants (Table 4), which included the known polymorphisms present in the samples.

A
Ref Seq Ref Pos rs2830088 WT n=10 rs2830088 Het n=8 rs2830088 Mut n=9
A T G C Total A T G C Total A T G C Total
A 25 1320 67 103 182 1672 1340 68 85 143 1661 9816 595 806 1391 12608
C 54 46 61 186 1514 1807                    
C 71 141 148 189 1288 1766 107 151 194 1240 1763 1090 1318 1456 9843 13707
T 72 36 1521 205 74 1836           408 11624 1507 834 14373
T 73 39 1555 129 151 1874 30 1520 108 162 1893 378 11952 1087 1344 14761
T 76 54 1523 129 151 1857 41 1475 119 162 1873 500 11784 1105 1250 14639
A 100 1499 38 150 127 1814 1484 45 117 137 1883 11206 408 1472 1234 14320
C 125           18 704 16 965 1828 128 10011 305 3128 13572
C 157 82 145 79 1498 1804                    
A 214 1319 28 185 89 1621 1325 38 140 83 1800 10347 336 1485 766 12934
C 226 132 202 87 1058 1479 122 202 86 1051 1687 1169 1508 777 8047 11501
B
Ref Seq Ref Position rs2830051 WT n=19 rs2830051 Het n=20 rs2830051 Mut n=21
A T G C Total A T G C Total A T G C Total
T 25 41 1025 131 236 1433 23 773 85 161 1042 34 1121 136 249 1540
A 26 939 58 96 226 1319 649 37 64 179 929 963 77 97 230 1367
A 37 328 40 100 165 633 221 29 68 105 423 347 54 110 203 714
A 38 659 72 85 207 1023 466 53 61 101 681 738 111 69 176 1094
T 50 86 914 212 204 1416 45 725 121 171 1062 53 1114 172 247 1586
A 54 466 172 61 118 817 383 117 48 74 622 562 176 74 140 952
A 68 595 108 65 110 878 463 96 48 69 676 721 126 76 97 1020
T 152 32 1038 234 213 1517 28 771 173 150 1122 38 1125 265 228 1656
A 153           744 28 169 97 1038 1102 41 262 118 1523
A 179           652 49 57 149 907 1000 78 96 191 1365
C 200 181 264 26 1083 1554                    
T 214 63 951 309 107 1430 40 809 180 83 1112 54 1165 242 135 1596
A 215 974 25 241 256 1496 771 18 172 178 1139 1124 27 246 252 1649
T 231           24 622 76 372 1094 79 277 128 907 1391
C 242                     63 226 134 1218 1641
C 257 84 152 163 1002 1401                    
C 294 242 192 155 851 1440 144 131 129 677 1081 259 175 174 926 1534
T 314                     16 1208 139 265 1628
A 317 454 146 175 204 979 376 86 111 188 761 571 154 168 239 1132
A 329 726 128 75 114 1043 523 84 48 70 725 811 135 61 110 1117
T 351 55 516 221 61 853 41 382 132 55 610 43 570 170 74 857
T 382 109 355 212 57 733 53 300 131 45 529 102 439 210 78 829
C 418 96 107 190 996 1389 49 97 124 785 1055 86 157 169 1099 1511
A 425 1014 32 270 147 1463 808 28 163 97 1096          

Table 4: Potential variants called by MinoTour for the samples containing the rs2830088 (A) and rs2830051 (B) polymorphisms.

Proportions of reference basecalls along the amplicon harbouring the rs2830088 polymorphism were compared between the heterozygous sample and the homozygous major allele sample and between the homozygous minor allele sample and the homozygous major allele sample in order to determine the location of any sequence variation. Average percentage differences for the calls along the amplicon were 1.8% and 2% for the heterozygous and homozygous minor allele comparisons respectively. The same position (125) yielded the highest percentage difference in each comparison (Figure 4A), with the homozygous minor allele comparison displaying roughly twice the percentage difference of the heterozygous comparison (62.4% and 34.5% respectively). This coincided with the position of the SNP and displayed the largest difference across the amplicon in each comparison. Chi-square tests supported the difference giving a significant p value (p<1 × 10-4). Concomitantly the proportion of minor allele calls also increased to 34.4% in the heterozygote and 60.8% in the minor allele homozygote respectively.

A similar result was observed for the second common SNP, rs2830051. The average percentage differences in reference basecalls between the major allele homozygote and the heterozygote and minor allele homozygote was 1.1% and 1.2% respectively. Larger percentage differences were observed for a single position (231), which corresponded to the location of the polymorphism (Figure 4B). Increasing differences in proportion of reference basecalls was seen with comparison of the heterozygote (25.3%) and of the homozygous minor allele sample (44.8%). These differences, when tested were also significant at study-wide level (p<1 × 10-4). The proportion of basecalls for the minor allele of the SNP was also shown to increase to 26.8% in the heterozygote and 46.2% in the homozygous minor allele carriers (Table 5).

next-generation-sequencing-applications-minor-alleles

Figure 4: Graph of percentage difference for the proportion of reference base calls between major allele homozygotes and heterozygote (blue line) and minor allele homozygote (red line) samples. A) Percentage difference along the amplicon harbouring the rs2830088 SNP. Increases in percentage difference occur at the point of variation, increasing with the number of minor alleles present. B) Percentage difference along the amplicon harbouring the rs2830051 SNP. Increases in percentage difference occur at the point of variation, increasing with the number of minor alleles present. The areas surrounding the polymorphisms are enlarged and shown inset in boxes.

rs2830088% of Minor Allele (T) reads   rs2830051% of Minor Allele (C) reads
6.99 WT (0%) 1047
34.38 Het (50%) 26.76
60.79 Mut(100%) 46.23

Table 5: Percentage of basecalls for the minor allele for each common SNP across the samples. Both show the expected increase in the percentage of calls for the minor allele across the heterozygous and homozygous minor allele samples. However, neither of the heterozygous or homozygous minor allele samples show the expected 50% and 100% percentage of base calls.

Discussion

This investigation set out to determine whether polymorphisms could be detected by nanopore sequencing using the Oxford nanopore technology (ONT) MinION device. Variation detection was implemented using the MinoTour Tool algorithms and by direct sample comparison of the proportion of reference basecalls along the entire length of each respective amplicon. The hypothesis being that the error rate would be similar between samples and therefore any deviation would be indicative of a polymorphism, negating the use of complex algorithms that sought to control the error rate in basecalling, such as the MinoTour Tool.

MinoTour [8] has a user-friendly web interface that utilises the basecalls made by ONT’s Metrichor server to allow users to visualise and analyse their sequence data. Like many of the programme designed to observed polymorphisms within sequences determined by nanopore technology, it aims to control the high basecalling error rate to see whether known polymorphisms could be detected in samples harbouring the variants when aligned to a reference sequence. The tool was unable to detect consensus variants for the deletion and heterozygous samples due to the high background error rate, a factor that has been identified in numerous studies [4,5]. Despite the identification of numerous potential variants including the polymorphisms of interest, against a background of false positives it was not possible to successfully detect variants using this method. However when given a minor allele homozygote sample the MinoTour Tool was able to detect the polymorphism singularly with its consensus algorithm, proving that algorithms design to align sequences to a reference can determine variants above background error rates in some instances.

As an alternative, direct sample comparisons were used to account for sequencing error rate as it was found to be similar across samples. The sequencing of a confirmed wild type sample was used as a baseline to compare the proportion of reference basecalls against samples containing polymorphisms. In doing this, the base error rate in MinION sequencing was taken into consideration, providing clearer results to detect polymorphisms. Identifying positions with significant differences in the proportion of reference basecalls between the samples would be indicative of variation between samples suggesting the presence of a polymorphism. In all SNP cases, the polymorphism position displayed highly significant differences in the proportion of reference basecalls between wild type and minor allele carrying samples. The observation that the difference in proportion was significant beyond the study-wide level indicates that this analysis could be applied to de novo detection of polymorphisms in full genome sequencing of samples with unknown genotypes.

The underlying biochemistry behind the Nanopore sequencing is improving with lower average error rates and percentage difference of reference basecalls between samples. This was observed with the common polymorphism tests as these were sequenced using an updated library preparation kit (SQK-MAP006) and protocol. Our observations indicate that sequence might also play a part in the error rate as the amplicon containing the rs2830051 polymorphism had much lower error rates and discrepancies between samples than the amplicon containing the rs2830088 polymorphism, which was sequenced at the same time. In addition to this, the accuracy of the reads may also be influenced by the flowcell, as each amplicon was sequenced on a different flowcell.

What was surprising was that given a heterozygous sample and one that was homozygous for the minor allele, percentages of the minor allele did not reach the expected 50% or 100% of basecalls for the alternative allele (Table 5). For example, the minor allele (T) in the rs2830088 polymorphism was called in 34.4% of the reads in the heterozygote and 60.8% in the homozygote. Despite the proportion almost doubling as expected it is still shy of the expected proportions potentially showing a bias towards the reference sequence in basecalling. This may be due to the inadequate removal of DNA samples from the flowcells by the washing procedure. Given that the order of sequencing began with the wild type sample first, carryover would indicate a bias towards reference basecalling. Further investigation of this would prove useful.

The MinION offers the realistic vision of every lab having its own sequencer in the future. However, in its current form, although it can provide long-read analysis of genome coverage, the ability to reliably and easily detect polymorphisms is limited. There is a need to decrease the sequencing error rate before it can become a useful commodity. The MinION and its future reincarnations will only become more accurate in basecalling abilities. With reduced error rates, the possibility of identifying polymorphisms, both known and novel, will be greatly improved by alignment to a reference sequence. This investigation demonstrates that polymorphisms can be readily identified by comparing proportions of reference calls between wild type and mutant samples.

Currently the error rate is still high and creates too many false positives when detecting polymorphisms, which prevents novel SNPs from being detected against the background of spurious signals. Therefore, a highly stringent significance threshold should be used and the most significant results fully investigated and validated by an alternative approach. Although the basecalling error rate of nanopore technology might deter users from utilising it to identify polymorphisms when sequencing genomes, we demonstrate a simple way of distinguishing known polymorphisms above the background error by calculating the differences in basecalling rates and propose that potential novel variants could also be identified.

Methods

PCR

Five polymorphisms within the APP gene were sequenced using three different amplicons. Primers designed for amplification via standard PCR protocol are shown in Figure 1B. DNA was extracted from human blood samples and a single sample for each genotype was used in this experiment. Amplified products were cleaned with ExoSAP and pooled to total 1ug of PCR product in a volume of 80 μl as specified in the ONT SQK-MAP005 protocol for library preparation (rs367709245, rs63750066, rs63749964) or 1ug of PCR product in 45 μl for SQK-MAP006 (rs2830088, rs2830051). All samples were previously validated using traditional Sanger sequencing to confirm genotypes of all polymorphisms and absence of other polymorphisms.

Library preparation and sequencing

Samples prepared with SQK-MAP005 were end-repaired using standard NEB End-repair kits (New England Biolabs), followed by dA-tailing of the blunt-ended amplicons (New England Biolabs). In SQK-MAP006 NEBNext Ultra II End-repair/dA-tailing module was used (New England Biolabs), combining both reactions into a single mix. Subsequent purifications were carried out with AMPure XP Beads (Beckman Coulter). Samples were ligated to the ONT adaptors and purified using magnetic beads (SQK-MAP005 His-tag beads; SQKMAP006 MyOne C1 beads) prior to loading for sequencing. Each sample was run to minimum template read coverage of 1000x, ending the run when read generation had slowed to one per minute. Flowcells were flushed through with washing buffers before loading the next sample, sample order of sequencing maintained as wild type followed by heterozygote, and finally homozygote samples where applicable. A new flowcell was used for each amplicon to prevent contamination and spurious error rates caused by non-familiar amplicons.

Analysis

Basecalling of amplicon sequences from the MinION were made in real-time with ONT software Metrichor (V1.69) and simultaneously uploaded to the MinoTour (V0.46) analysis tool for visualisation of the data [8]. Alignment analysis was performed on 2-directional (2d) reads where both template and complement strands were read to produce a consensus, resulting in greater sequence accuracy. Details for the algorithm for the alignment tool can be found in reference [8]. MinoTour was used to detect variation from the given reference sequence for each amplicon, including those that were 100% match to the reference (wild type). The tool uses two methods to detect variants; a consensus variant occurs when a non-reference base has a greater base count than the reference allele and a potential variant is called when an alternate allele to the reference occurs more frequently than 2 standard deviations (SD) from the average error rate of the sequencing run.

The 2d counts for bases and indels at every position along the amplicons were obtained from MinoTour and subjected to manual calculation. Initial exploration of error rate for each amplicon was investigated using the percentage of non-reference basecalls. In order to observe the known polymorphisms, the proportion of reference basecalls from the total (inclusive of indels) at each position was compared between wild type and variant carrying samples. Calculating the percentage difference in these proportions allowed comparisons to be made, as a greater difference at any given location would be indicative of a potential polymorphism. To verify the increased percentage difference for reference basecalls proportions at the polymorphism site in variant samples, significance of this difference was calculated using a Chi-squared2) test for each position. Assuming the null hypothesis there would be no significant difference in the proportion of reference basecalls between samples. A study-wide corrected p-value for significance was calculated using the size of the largest amplicon studied (482 bp).

Acknowledgements

The work conducted was supported by Alzheimer’s Research UK. The NeuroScience Group and University of Nottingham School of Life Sciences provided studentship funding for TP. We thank Oxford Nanopore Technologies for reagents provided as part of the Early Access Programme and Matt Loose for his guidance on utilising the MinoTour programme suite for the sequencing analysis.

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