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`Author Manuscript
`Nat Methods. Author manuscript; available in PMC 2011 September 12.
`Published in final edited form as:
`Nat Methods. 2008 October ; 5(10): 887–893. doi:10.1038/nmeth.1251.
`
`IDENTIFICATION OF GENETIC VARIANTS USING BARCODED
`MULTIPLEXED SEQUENCING
`
`David W. Craig1,*, John V. Pearson1,*, Szabolcs Szelinger1,*, Aswin Sekar1, Redma Margot1,
`Jason J. Corneveaux1, Traci L. Pawlowski1, Trisha Laub1, Gary Nunn2, Dietrich A.
`Stephan1, Nils Homer, and Matthew J. Huentelman1
`1The Translational Genomics Research Institute, 445 N. 5th St, 5th Floor, Phoenix, AZ 85004
`2Illumina, 9885 Town Centre Drive, San Diego, CA 92121
`
`Abstract
`We developed a generalized framework for multiplexed resequencing of targeted regions of the
`human genome on the Illumina Genome Analyzer using degenerate indexed DNA sequence
`barcodes ligated to fragmented DNA prior to sequencing. Using this method, the DNA of multiple
`HapMap individuals was simultaneously sequenced at several ENCODE (ENCyclopedia of DNA
`Elements) regions. We then evaluated the use of Bayes factors for discovering and genotyping
`polymorphisms from aligned sequenced reads. If we required that predicted polymorphisms be
`either previously identified by dbSNP or be visually evident upon reinspection of archived
`ENCODE traces, we observed a false-positive rate of 11.3% using strict thresholds (Ks>1,000) for
`predicting variants and 69.6% for lax thresholds (Ks>10). Conversely, false-negative rates ranged
`from 10.8% to 90.8%, with those at stricter cut-offs occurring at lower coverage (< 10 aligned
`reads). These results suggest that >90% of genetic variants are discoverable using multiplexed
`sequencing provided sufficient coverage at the polymorphic base.
`
`Introduction
`Genome-wide association (GWA), candidate gene, and linkage studies have identified
`thousands of moderately sized genomic regions that are associated with human disease but
`where comprehensive resequencing is needed to identify the genetic variant causing the
`association. In particular, GWA studies have identified hundreds of disease-associated
`haplotypes, typically spanning 5 to 100kb1–3. A logical next step is to identify and
`resequence all genetic variants within the associated haplotype in order to identify the
`functional variants among the many non-functional evolutionarily linked neighboring
`polymorphisms. Next-generation DNA sequencing technologies are in principle well-suited
`to this task due to their capabilities for high-throughput low-cost sequencing. While these
`technologies offer massive sequencing capacity, it is still difficult, time-consuming, and/or
`expensive to resequence large numbers of samples across moderately sized genomic regions
`(5kb-1Mb).
`
`Corresponding Author: David W. Craig, Ph.D., Investigator, Neurogenomics Division, The Translational Genomics Research
`Institute, 445 North Fifth Street, Phoenix, AZ 85004, (602) 343-8747 (phone), (602) 343-8844 (fax), dcraig@tgen.org, www.tgen.org.
`*These authors contributed equally
`Author Contributions
`D.W.C., J.V.P., M.J.H., G.N. and D.A.S. contributed to initial experimental design. S.S., A.S., M.R., J.J.C., and T.L.P. contributed to
`development and execution of exact experimental protocols. J.V.P., D.W.C., and N.H. contributed to the development of
`bioinformatics and analysis pipelines.
`Competing Financial Interests
`G. Nunn has a commercial interest as an employee of Illumina.
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`Simultaneous resequencing of large numbers of individuals for a targeted region is possible
`by bar-coding or indexing the reads from each individual with a short identifying
`oligonucleotide4–7. While indexing has the obvious benefit of multiplexing samples within
`a run, DNA indexing offers two key additional advantages: direct measure of base-by-base
`error rate and reduction of array-to-array or day-to-day variability. Previous pioneering
`efforts to develop DNA indexing have shown considerable promise, however adoption is
`still in its infancy and considerable challenges remain, including the development of
`practical and cost-effective approaches for short-read platforms. Beyond these experimental
`challenges, there exist few analytical frameworks that are characterized for discovering and
`genotyping genetic variants across a targeted interval using multiplexed short-read sequence
`data from multiple individuals.
`
`In this manuscript we report an experimental and analytical approach for simultaneous
`sequencing of multiple individuals using DNA indexes on the Illumina Genome Analyzer
`(GA). We use a degenerate six-base index to evaluate optimal index size and we assess
`performance of the method by resequencing HapMap individuals across ENCODE regions
`that have previously been capillary sequenced. We develop a Bayesian analytical framework
`that leverages the inherent ability of indexing to measure error and to reflect variability in
`sequencing coverage.
`
`Results
`Experimental Design
`Our experimental protocol for indexing is summarized in figure 1 and further detailed in the
`supplementary methods. We amplified multiple 5kb regions (supplementary table 1 and 2)
`by long-range PCR, for 46 individuals genotyped by the ENCODE projects1,8. Amplicons
`were equimolar pooled for each individual, digested, blunt end-repaired, flanked by an
`adenosine overhang, and ligated to one of the 46 indexed adapters (supplementary table 3
`and 4). Following ligation, samples from all individuals were pooled into a single sample
`(referred to as an indexed library), purified, enriched by PCR, and sequenced on the Illumina
`GA on a single lane of an 8 lane flow-cell. We prepared two libraries, Library A, consisting
`of 10 5kb amplicons covering 50 kb, and Library B, consisting of 14 5kb amplicons
`covering 70kb (supplementary table 2). Library A contains both regions that were previously
`capillary sequenced and regions that were not sequenced within the ENCODE project,
`whereas Library B contains only regions previously sequenced within the ENCODE project.
`
`Index Design
`We used a six-base design, which allows us to control, tolerate, and measure error during
`base-calling of the index. The six-base index provides substantial degeneracy: only 46 of the
`4096 possible nucleotide combinations were utilized (see supplementary table 4 for
`indexes). Moreover, indexes were chosen so that 1, and in some cases 2, sequencing errors
`could be tolerated without an index being incorrectly identified as being a different valid
`index. While not implemented in this study, utilizing each of the four nucleotides within an
`index may provide for higher accuracy base-calling since each base would have to be
`correctly called at least once within a sequenced read.
`
`Using this design strategy, 48 of the 4096 possible 6-mers were synthesized and used as
`indexes for multiplexed sequencing. Perfect alignment of any index should occur at ~0.1%
`by chance. The 6th base of the index was an obligate thymidine necessary for ligation of the
`adenosine overhang. The first and fifth bases were identical to detect biases during
`normalization and calculation of the deconvolution matrix. In practice, we used 46 of the 48
`indexes to allow for plate layouts that included positive and negative controls.
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`Index Performance
`Typically, 3–10 million short-read (32 or 42 base) sequences were generated for each lane of
`an 8-lane flow cell, though early sequencing runs exhibited greater variability in the number
`of sequenced reads. After filtering using Illumina analysis pipeline defaults, approximately
`45–50% of the reads remained. We observed a large spread in the number of counts per
`index (figure 2). Although a systematic reason for the initial spread in index performance
`was not identified, weaknesses in index design were obvious in some cases. For example,
`‘AAAAAT’ which was frequently read as ‘AAAAAAT’, perhaps due to an oligonucleotide
`synthesis bias. A few indexes that were not well represented were complementary to other
`sections of the adapter sequence, possibly hindering adapter formation. Resequencing the
`same library gave nearly the identical distribution of reads regardless of run performance,
`indicating that the distribution is likely not due to a post-PCR enrichment step. Furthermore,
`recreating libraries and sequencing different individuals in additional sequencing runs did
`not substantially alter performance for indexes that were substantially under-represented or
`over-represented. Of the 46 initial indexes, 19 indexes varied by less than a factor of 5
`between the most and least common index and 13 indexes varying by less than a factor of 2.
`While some of the initial index variability was consistent between sequencing runs,
`retrospective analysis of gel images suggests that a portion of the index variance may be due
`to subtle differences in DNAse digestion of pooled amplicons, whereby the number of
`available ligation targets is higher for samples that are digested with higher efficiency. In
`runs subsequent to these initial libraries (data not shown), we observed that using gel-images
`of the PCR-enriched products or qPCR, to quantify the ligated adapter prior to pooling,
`reduced index variability such that the best covered index was observed 5-fold more
`frequently than the least covered index. By comparison the same ratio was 11-fold without
`quantification of the ligated primers prior to pooling. While future studies may improve
`index variability still further, it may be effectively managed without substantially affecting
`workflow, by requiring higher average coverage within a study, by sequencing on two lanes
`with different indexes, or by sequestering samples with deficient coverage for later runs.
`
`Index-level coverage
`As shown for a subset of library A, coverage across individual 5kb amplicons was even and
`generally free of large gaps (figure 3). We did observe base-to-base variability in the
`coverage, as expected from alignment of short reads. Both between amplicons and within an
`amplicon, some deviation from the expected Poisson distribution was observed. Clearly
`amplicon-to-amplicon variability contributes to some extent to the departure from the
`expected Poisson distribution. For a given index, we observed approximately a 1.5 to 2.0
`fold difference between the amplicons with the most and fewest number of reads. Inspecting
`gel images for selected amplicons confirmed that these observed differences within regions
`were largely due to uneven pooling of amplicons. The observed amplicon-to-amplicon
`variability is likely to be due to the fact that we utilized median concentrations across the
`plate when pooling amplicons for an individual, rather than individually pipetting each
`amplicon based on its specific concentration.
`
`Comparing a given amplicon across indexes (i.e. across individuals), there is clearly some
`base-level correlation in coverage based on the positions of spikes and valleys within the
`coverage plots (figure 3). Within a single amplicon there was also departure from a Poisson
`distribution, evident from the fact that the same bases had little or no coverage across
`individuals. Indeed, there is consistency between individuals with regard to bases that are
`under or over-represented. The rank correlation coefficient between indexes at a given base
`averaged 0.408, suggesting that local sequence (or base order) accounts for slightly less than
`half of the base-to-base variability in coverage.
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`Error reduction/Alignment strategy
`Depending on alignment rules, aligning a short read to a reference sequence reduces the
`sequencing error rate at the cost of limiting discovery. We aligned 35-base pair sequences,
`allowing for only a single error. We are thus essentially limited to identifying single base
`substitutions in an aligned read, while limiting error to 1/35 or 2.8% as explained below. We
`further required that two stretches of 11 or more consecutive bases match the reference
`sequence or that the read have at least one stretch of 15 consecutive matches to the reference
`sequence. In both cases, our aligner required that the final 2 bases match the reference
`sequence to insure we did not over-align an error at the final base. The rules for alignment
`were largely chosen to control error, and would falsely align a randomly generated sequence
`in less than 0.1% of alignments in a 100kb region. Given our tolerance for 1 error in
`alignment, we expect a maximum per-base error rate of 2-3% (1 error in 35bases ≈ 2.8%).
`
`Alignment of short-reads has advantages. For example, one would expect that we would
`have greater difficulty detecting closely neighboring single nucleotide polymorphisms
`(SNPs) since we mostly limit our aligner to 1 non-consecutive mismatch. However, the
`short-reads stochastically overlap and these various types of neighboring genetic variants are
`observed by alignment of multiple sequences not spanning both variants.
`
`Polymorphism discovery
`Polymorphism discovery is a primary goal for resequencing an association interval for a
`GWA study, particularly under the common variant hypothesis. Indeed, in some cases one
`may only wish to know which bases are polymorphic for custom-genotyping on a separate
`platform.
`
`We first provide an intuitive explanation of our analysis approach for polymorphism
`discovery, noting that detailed equations are provided in the methods. We utilized a Bayes
`factors to compare the probability that the distribution of mismatched bases arises from
`sequencing error to the probability that the distribution of mismatches arises from diploid
`polymorphism. For example, if 20% of reads for a given base were non-concordant with the
`reference sequence across all individuals, and the non-concordant bases were due to the
`presence of a SNP, one would expect each individual to be homozygous (0% or 100%
`concordance with reference) or heterozygous (concordance split 50/50). On the other hand,
`if the 20% non-concordant bases were due to sequencing error, then the number of non-
`concordant bases for each individual would follow a binomial distribution around 20% (e.g.
`person 1~20.5%, person 2~19.3%, person 3~20.7%, etc). As described below, the error
`estimates required to calculate the probability of a genetic variant being a true variant are
`readily obtainable when individuals are indexed and multiplex-sequenced. Further, indexed
`and multiplexed sequencing removes run-to-run biases which would confound these
`estimates if all aspects of experimental design were not properly randomized. Bayes factors
`are particularly effective for the uneven coverage inherent to short read sequencing, and
`provide a mechanism to control false positives in light of more or less evidence.
`
`Sequenced regions were analyzed base-by-base for all individuals by calculating a
`polymorphism discovery Bayes factor (defined as Ks in equation 2). An example plot of Ks
`across each base (of 50kb) is shown in figure 4 for Library A; a similar analysis was
`conducted for Library B (supplemental figure 1).
`
`We next evaluated false-positive and false negative rates to assess our experimental and
`analytical framework for variant discovery (table 1 for Library B and figure 5 for both
`Library A and B). False positives are particularly difficult to quantify since not all
`polymorphic sites are known, even in previously resequenced regions. In our analysis, to be
`defined as a false positive, a variant must not exactly match the location of variants within
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`dbSNP, and must not have trace sequencing data indicating a previously missed variant. In
`some cases trace sequence data was not available or unreliable. Consequently, the false
`positive rate is expected to be an upper estimate since the exact position must be validated as
`polymorphic by an existing database. False negative rates were determined by calculating if
`a base known to be polymorphic in our library of HapMap individuals reached previously
`specified Ks thresholds. This calculation of false-negative rates does have some bias, since it
`does not take into account coverage of the polymorphic base. Figure 5 plots the dependence
`of Ks on coverage.
`
`As expected, setting a higher threshold for Ks gives fewer false positives. In table 1 for
`Library A, as Ks increases from 10 to 1,000 the false positive rate decreases from 69.6% to
`11.3%. Likewise, with fixed coverage we observe the false negative rate increasing from
`10.8% to 90.8% as Ks increases from 10 to 1,000. A more detailed discussion of false-
`negative and false-positive rates is provided in the supplementary methods. Referring to
`figure 5, all the false negatives occur when the cumulative coverage of individuals with the
`rarer variant is less than 10 reads. Further highlighting the dependence of false-negatives on
`coverage, all polymorphisms that were covered by 20+ reads (summed across individuals
`known to differ from the reference) have a Ks >1,000. Overall, we observed that 90% of
`variants were detectable, though designing for >20 reads will be essential for controlling
`false negatives.
`
`Through the course of analyzing bases with a Ks > 100 for false positives, using NCBI
`archived ENCODE traces, new SNPs were discovered that were evident in visual
`reinspection of capillary traces, but that had not been annotated in dbSNP (figure 4f–h).
`These examples demonstrate that index-based resequencing can identify novel variants even
`in heavily sequenced and heavily annotated regions. Within Library B, it is intriguing to
`note that two variants with a Ks>100 were not SNPs but actually insertions (rs11279266 is a
`1bp insertion and rs10555419 is a 6bp insertion). Thus it is possible to identify genetic
`variants explicitly not allowed within the alignment scheme.
`
`Genotyping individuals at known polymorphisms
`Since false negatives are clearly tied to coverage, we explored the influence of coverage
`further by analyzing the above regions in an individual-by-individual analysis. Derived in
`Equation 3 within the methods, Ki is an analogous Bayes-factor for an individual having the
`rarer allele at a known polymorphic base. Conceptually, it can be thought of as a specific
`individual’s contribution to Ks. Shown in high granularity (figure 5c), we calculate the
`percentage of variants correctly identified in an individual given a certain number of reads.
`For example, when the coverage for a base was ~20 reads (averaging from 16 to 24), we
`detected >80–90% of the bases at Ki>10, with a false-positive rate of 1.6%. In comparison
`to polymorphism discovery, the low false-positive rates of genotyping at a known
`polymorphic base are due to the fact that we are no longer assessing thousands of bases for a
`rare event, but rather assessing a few dozen individuals for a more frequent event.
`
`Discussion
`Our experience suggests that achieving adequate coverage is one of the most important
`factors in the design of a multiplexed targeted resequencing experiment. Depending on
`assumptions made within the experiment, the desired coverage (and as a consequence, the
`cost) can vary substantially. Key considerations include whether the objective is (1)
`discovering genetic variants for genotyping by a separate method such as custom SNP
`genotyping, (2) conducting polymorphism discovery and variant calling within one
`sequencing experiment, and/or (3) exhaustively resequencing for all common and rare
`variants.
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`Exhaustive polymorphism discovery is the next major phase for GWA studies. Shown in
`Figure 4, indexing of short-reads is surprisingly robust at polymorphism identification. For
`example, even when a highly restrictive error alignment scheme was used, we were able to
`identify a novel coding SNP three bases from an annotated SNP. Additionally, it is
`encouraging to see the discovery of insertions using an alignment scheme only allowing
`substitutions. Finally, it is a highly encouraging that an automated analysis strategy for
`short-read data can uncover novel variants, even in regions that were previously sequenced
`for variant discovery using these HapMap individuals.
`
`Based on the false-positive and false-negative rates, a critical factor will be how to balance
`coverage and cost. In table 1, utilizing a threshold of Ks > 1,000, we observe a low false-
`positive rate (11.3%) and a high false-negative rate (90.8%). Since we required that the
`exact base must be polymorphic in an existing database, the actual false-positive rate may be
`lower. As evident in figure 5, false negatives are due to the additional coverage (>10 reads)
`is required for overcoming higher Ks thresholds. Considering the substantial base-to-base
`variability in figure 3, one would not simply want to design for an average coverage of 10
`reads. Rather, by designing for ~50 reads or more, one may minimize both the false negative
`and false-positive rates, given coverage variability of short-read sequencing.
`
`While whole-genome sequencing may be the primary motivator for improvements in
`sequencing technology, it is clear that next-generation technologies are immediately useful
`for focused hypothesis-driven sequencing of linkage peaks, groupings of candidate genes, or
`sequencing the entire known coding sequence of the human genome. In this report, we
`developed per-individual indexing of pooled PCR amplicons to carry out targeted
`sequencing. However, it is straightforward to envision using other sample preparation
`methods, such as genome partitioning 9–12. One could replace the pooled amplicons from
`our experimental outline (figure 1) with total genomic DNA and complete the partitioning
`by a hybridization approach after pooling ligated amplicons. Indeed, variation discovery
`through the resequencing of all candidate regions implicated in a disease across dozens, and
`possibly hundreds, of individuals could be significantly accelerated by merging multiplex
`capture, indexing, and next-generation sequencing approaches into a single protocol.
`
`Methods
`Amplification and pre-ligation sample preparation
`Two primary amplicon libraries (Library A and B, specific targeted regions listed in
`supplementary table 2) were constructed from individually amplified 5kb regions using
`long-range PCR. Regions composing a library were chosen from the ENCODE project and
`selected to provide a sampling of different genomic region types. Only a portion of the
`overall ENCODE regions have been previously sequenced as part of the SNP discovery
`portion of the ENCODE project. Library A was a composite of previously sequenced
`regions and regions not sequenced in ENCODE. Library B was entirely composed of
`regions that were previously sequenced. Flanking primers for each amplicon (supplementary
`table 5) were manually selected. While we did not screen for the existence of known
`polymorphisms within the primer sequences, such effort would be advisable in future
`efforts. A detailed description of region amplification, amplicon pooling, fragmentation,
`preparation of 48 adapters, end-repair, ligation, PCR enrichment, cluster generation and
`sequencing on the Illumina Genome Analyzer are provided in the supplementary methods.
`
`Analysis – Base calling and alignment
`Illumina GA images were analyzed using a modified Illumina GA (0.2.2.6) processing
`pipeline. Descriptions of the modifications and all scripts are available at
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`bioinformatics.tgen.org. A default matrix deconvolution file was used for base-calling by
`‘Bustard’ based on a control phi-X library provided by Illumina. Following base-calling a
`script was used to access all sequences, regardless of quality score. We deviated from the
`default cut-offs provided by Illumina’s ‘GERALD’ process since it was found that sequence
`quality was better controlled by matching to an index (46/4096 or 0.1% by chance) or
`matching with 1 or fewer errors to the reference sequence. Bases were aligned by
`progressively truncating the sequence at the 5’ end until a unique alignment was obtained
`with a probability of stochastic alignment of less than 1%. This approach is distinct from
`recently described short-read alignment schemes13, but clearly has some of the same
`features.
`
`Aligned sequences were summarized into a binomial of counts agreeing (ai) or disagreeing
`(bi) with the reference sequence for the ith individual of n total individuals for each base (s).
`The error rate (θs) is the percentage of reads disagreeing with the reference sequence across
`all samples. Model 1 (M1) assumes that the error rate for an individual equals the error rate
`for all individuals, or θi = θs. Therefore we can estimate θs as:
`
`Equation 1
`
`Model 2 (M2) assumes that for some individual i we have θi ≠ θs. To calculate this
`likelihood we use a hyperprior offset (σs) for three possible genotypes. The hyperprior can
`be thought of as conditioning on the zygosity of the individual and thus reflecting the
`uncertainty of the genotype of the individual at a given base. In this analysis we focus on the
`detection of biallelic SNPs but triallelic or other types of SNPs could be considered under
`more complex models. The Bayes factor for a base position is:
`
`Equation 2
`
`In equation 2, Ks is the Bayes factors across all individuals and is calculated for each SNP.
`The value for Ks is effectively identifying polymorphisms at a base, and determination of
`the individuals that show the rare variant is accomplished by Ki:
`
`Equation 3
`
`The analysis for rare variants in the above equation uses the prior probability of p(σs)
`derived from the distribution across all individuals and all bases initially assuming all other
`individuals are homozygous for the reference allele, which is a reasonable assumption given
`that novel variants are assumed to be rare. By successive iterations, p(σs) is then
`recalculated by calling variants AA, AB, and BB under a given threshold (e.g.
`Ki={3,10,100}), and recalculating p(σs) based on these variant calls. Plots of Bayes factors
`vs. error rates are provided in supplementary figure 2 to show distribution of Bayes factors
`and their correlation with error rate.
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`If a SNP is known it is also reasonable to use other prior information at a base, such as allele
`frequency in the population. However, prior information on the location and allele frequency
`of known SNPs was not used in this study in order to better evaluate the effectiveness of the
`underlying framework.
`
`Supplementary Material
`Refer to Web version on PubMed Central for supplementary material.
`
`References
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`
`Acknowledgments
`We wish to acknowledge funding from the state of Arizona, National Heart Lung and Blood Institute (U01
`HL086528) and the Stardust foundation.
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`Figure 1.
`Schematic describing the preparation of indexed libraries. The red box indicates the
`indexing step, where for each person a unique indexed adapter was ligated to the fragmented
`genomic DNA.
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`Figure 2. Comparison of index performance
`Index variability in initial sequencing runs (Library A) used for evaluating index
`performance are shown (top graph). Percentages of reads aligning to the reference sequence
`are listed by index, without introduction of normalization methods. A total of 30 indexes
`were present in >0.05% of all aligned reads. Highlighted in the blue box are 19 indexes with
`less than 5 fold difference in index frequencies, used in subsequence studies. Indexes
`matching with 0 errors are in blue bars and indexes with 1 error are in magenta bars. The
`bottom graph shows the location of errors by base, for each index.
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`Figure 3. Relationship between mean and local coverage
`Example coverage of 4 individuals sequenced within a single line of an 8-lane flow-cell for
`10 pooled amplicons as part of Library A. Amplicons are shown consecutively for each
`individual by the alternating shaded background. Index sequence and mean coverage for that
`individual are shown above each graph. The maximum and minimum coverage is shown for
`each amplicon in the top of the graph. Overlaying pie charts show the observed distribution
`of bases across all amplicons and the expected distribution determined from a Poisson
`distribution of the mean coverage, binned by 0 reads, 1–4 reads, 5–9 reads, 10–19 reads, and
`>20 reads.
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`Figure 4. Discovery of variant bases by simultaneous analysis of all individuals
`(a.) The Bayes-factor for polymorphism discovery(Ks) is plotted for each of the10
`sequenced 5kb amplicons from Library A. Exact positions matching known polymorphisms
`are colored as red spheres and the dbSNP identifier is provided for the most significant
`SNPs. Black bars at top indicate locations of documented SNPs. A magnified view of
`amplicon 1 (b.) and amplicon 6 (c.) is provided to compare variants predicted by indexed-
`multiplexed sequencing to previous deep capillary sequencing results for the same
`individuals as part of the ENCODE project. (d–e.) Examples of false-positives arising from
`sequence homology to elsewhere in the genome. (f–i.) Examples of sequence traces
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`validating the discovery of novel SNPs not previously annotated in ENCODE capillary
`sequencing traces. Similar analysis was conducted on Library B (shown in the
`supplementary figure 1).
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`Figure 5. Relationship between base-level coverage and Bayes-factor for polymorphism
`discovery and