BACKGROUND
whole genome sequencing  comprehensively investigates genome sequence changes such as single-nucleotide variants   <cit> , insertions and deletions  , chromosomal rearrangements  <cit> , and copy-number variation  <cit> , and so on.
however, whole exome sequencing  has become more popular because exons are more interpretable than other genomic regions and because the technique allows more samples to be analysed.
previous studies have analysed disease loci that segregate in families , and large disease cohorts , and validated their findings in wgs studies  <cit>  using exome sequencing technology.
other groups have assessed the clinical application of next generation sequencing  by target sequencing .
with recent advances in ngs technology , it is now possible to sequence the whole genomic or exonic dna of an individual.
compared with traditional single nucleotide polymorphism  arrays  <cit> , wgs can generate target dna sequences and identify substantially more genetic variations, thus explaining a larger fraction of human phenotypic diversity  <cit> .
currently, the most widely used sequencing platform in human genome sequencing research is the illumina hiseq series of instruments , which use highly-parallel optical sensing of polymerization reactions to achieve an ultra-high throughput .
life technologies has also released a new version of the semiconductor sequencing platform, ion proton , which provides researchers with an alternative sequencing platform.
proton has a medium-throughput, cost effectiveness and rapid turnaround time .
thus, proton is an attractive means of validating the variants called in whole genomes by other sequencing platforms  <cit> , sequencing of whole exomes  <cit> , screening cancer-related genes in solid tumours  <cit> , or conducting sequencing-based clinical applications such as prenatal diagnosis which has a strict turn-around time requirement  <cit> .
however, given the differences between sequencing technologies and subsequent variant calling pipelines applied by hiseq  <dig> and proton, it is necessary to comprehensively compare the two platforms.
previously, the variant calling performance of the proton sequencer was assessed by comparing it with variants called by hiseq  <dig>  <cit> , complete genomics, and illumina snp microarray.
another team used the proton sequencer to validate the whole exome variants called by wgs on the hiseq  <dig> sequencer  <cit> .
in the present study, we comprehensively compared the differences between variants called by hiseq  <dig> with the agilent sureselect human all exon v <dig> kit  and proton with the ion targetseq™ exome enrichment kit , and validated the variants by sanger sequencing.
our results show that there is a significant discrepancy between sureselect-hiseq and targetseq-proton sequencing strategies, and provide some guidance for analysing personal genome on different sequencing platforms.
RESULTS
data summary of exome sequencing
exonic dna from four individual chinese genomic dna samples was captured by the ion targetseq™ exome enrichment kit using probes of various lengths , and subsequently sequenced by the proton sequencing platform .
we obtained approximately 40× average sequence coverage on targeted regions .
for all samples, proton reads covered more than 90 % of the targeted region with ≥10× reads coverage.table  <dig> statistics of reads and alignment to reference genome for four samples’s exome sequencing on targetseq-proton/sureselect-hiseq platform
exonic dna from the same four samples was separately enriched by sureselect human all exon v <dig> with 120 bp probes, then sequenced on hiseq  <dig> with 2*100 bp read lengths.
we obtained ≥33× sequence coverage on targeted regions , and more than 86 % of target regions had ≥10× reads coverage.
definition of the evaluation region
we chose the overlapping  <dig>  mb exonic regions as an evaluation region between the ion targetseq™ exome enrichment kit and the agilent sureselect v <dig> kit.
a total of  <dig> ,  <dig> ,  <dig> , and  <dig>  variant loci were detected by proton and hiseq  <dig> in samples s <dig>  s <dig>  s <dig>  and s <dig>  respectively .
the co-detected rates of total variant loci were  <dig>  %,  <dig>  %,  <dig>  % and  <dig>  %, respectively, on two sequencing platforms for four samples .
the analyses of the four samples were consistent when evaluating the numbers and co-detection rates of loci observed.
therefore, we randomly chose to describe the results of sample s <dig> in this report.table  <dig> variant loci detected by targetseq-proton and sureselect-hiseq sequencing
atotal loci: all variant loci in the overlapping regions detected by hiseq  <dig> or ion proton sequencing, which include the concordant, disconcordant, targetseq-hiseq-specific and sureselect-proton-specific loci
bco-detected loci: the variant loci co-detected by targetseq-hiseq and sureselect-proton sequencing, which include concordant and disconcordant loci.
the number in parentheses is percentage
cconcordant loci: the variant loci with the same genotype detected by between targetseq-hiseq and sureselect- proton sequencing
ddisconcordant loci: the loci with different variant genotype detected by between targetseq-proton and sureselect-hiseq.
hom/het refers to the loci whose variant genotype is homozygotes detected by targetseq-proton, but heterozygotes detected by sureselect-hiseq.
hom/hom, het/hom and het/het refer to analogous variant genotype
when considering snp loci, we evaluated the ratio of transitions to transversions  because unusually high or low ratios may be indicative of false positive variants.
overall, ti/tv was  <dig>  in the total detected snps of sample s <dig> .
for concordant snps, the ti/tv ratio was  <dig> .
by contrast, notable differences were observed in the ratios of hiseq 2000-specific  or proton-specific  snps, regardless of whether they were novel or known in dbsnp .
comparison of variants-detecting platforms
for sample s <dig>  a total of  <dig>  variant loci were detected by proton or hiseq  <dig>  of which  <dig>  loci were also detected by proton and hiseq  <dig> concurrently .
the concordance of  <dig>  co-detected variant loci reached  <dig>  % .
concordance was determined by the loci with the same variant genotype.
among the  <dig>  concordant variants,  <dig>  snps and  <dig> small indels were identified.
of the snps,  <dig>  %  were reported in dbsnp, while  <dig>  %  of the small indels were also reported in this database .
of the  <dig> total variants only detected by hiseq  <dig>   <dig>  % of  <dig> snps and  <dig>  % of  <dig> small indels were reported in dbsnp.
however, this was true of only  <dig>  % of the  <dig> snps and  <dig>  % of the  <dig> small indels among the  <dig> total variants specific to proton.table  <dig> pairwise comparison of variants called for four samples by targetseq-proton and sureselect-hiseq
athe numbers of parentheses refer to known or unknown variant loci in dbsnp databases
we observed a notable difference in the size distribution of indels calling by the two sequencing platforms as well as the percentage that had been previously reported in dbsnp.
figure  <dig> shows the size distributions of both concordant and platform-specific indels.
among all concordant small indels,  <dig>  %  were 1-bp, which is similar to that of hiseq 2000-specific .
however, this value was  <dig>  % for proton-specific small indels.
analysis of the composition and homopolymer size of 1-bp indel loci flanking sequences showed that 1-bp indels called by proton were biased toward homopolymer types g and c .fig.
<dig> distribution of size and classification of small indel called by exome sequencing on sureselect-hiseq and targetseq-proton in sample s <dig>  four classes of small indel were defined as concordant novel, concordant known, specific novel and specific known.
novel refers to indels not reported in dbsnp build  <dig>  known refers to indels previously reported in dbsnp build  <dig>  a showed the size and classification of small indels called by targetseq-proton.
b showed the size and classification of small indels called by sureselect-hiseq
among the discordant variant loci, the majority  were heterozygote calls by proton but homozygote calls by hiseq  <dig> .
additionally,  <dig> discordant loci were homozygous calls by proton to heterozygous calls by hiseq  <dig>  a few discordant loci consisted of different heterozygotes  and different homozygotes  co-detected by proton and hiseq  <dig>
validation by sanger sequencing
to validate variants called by the two sequencing platforms, we pcr-amplified genomic dna fragments containing selected snps and small indels, then sequenced them.
a total of  <dig> snps of all four samples were randomly selected for validation:  <dig> hiseq 2000-specific,  <dig> proton-specific and  <dig> concordant snps.
of all  <dig> snps,  <dig>  % were successfully amplified and sequenced.
the validation rate was  <dig>  % for concordant snps,  <dig>  % for hiseq 2000-specific and  <dig>  % for proton-specific snps .table  <dig> sanger sequencing validation comparison on variant subsets of targetseq-proton and sureselect-hiseq data calls
a total of  <dig> snps and  <dig> 1-bp indels from four samples were randomly selected for sanger sequencing validation, with  <dig> loci from the set of targetseq-proton-specific,  <dig> from the set of sureselect-hiseq-specific, and  <dig> from the set of concordance between two platforms
as the small indels biased toward 1-bp indels , we selected  <dig> concordant,  <dig> hiseq 2000-specific, and  <dig> proton-specific 1-bp indels for sanger sequencing validation.
of all  <dig> indels,  <dig>  %  were successfully amplified and sequenced.
concordant and hiseq 2000-specific indels had validation rates of  <dig>  %  and  <dig>  % , respectively .
however, the validation rate of  <dig> proton-specific indels was only  <dig>  % .
comparisons of variants-detecting pipelines
for the proton sequencing platform, the bwa-gatk pipeline were shown to call more than twice as many variants  as the torrent variant caller  pipeline .
variant concordance between the two pipelines was only  <dig>  %.
we also noted that the concordance  of novel variants was much lower than that of known variants .
although the concordance of snps between the two pipelines was  <dig>  % among all  <dig>  snps, the indel concordance was extremely low, at just  <dig>  % for all  <dig>  indels .
for the hiseq  <dig> sequencing platform, we also investigated the differences between variants detected by bwa-gatk and stampy-gatk pipelines.
a  <dig>  % concordance rate for all  <dig>  variants was observed, with the overall concordance of novel variants  shown to be much lower than that of known variants  .
the  <dig>  % concordance of snps was also higher than the  <dig>  % concordance of indels.
validated rates of three calling pipelines which differ only in read mapping, were also compared: bwa-se refers to bwa mapping of hiseq  <dig> reads to the human reference with the single-end reads mode, bwa-pe uses the paired-end read mode, and stampy-se uses stampy- <dig> .
<dig> software  with the single-end read mode  <cit> .
in the sets of sanger sequencing validated variants, the bwa-pe pipeline called  <dig> snps, which was more than bwa-se and stampy-se, which called  <dig> and  <dig> snps, respectively.
additionally, the validated rate of snps called by the bwa-pe pipeline  was higher than that of bwa-se  and stampy-se  .table  <dig> comparison of the validation rates of variants called by different pipelines for sureselect-hiseq data
note: abwa-pe, bwa mapping with paired-end reads mode
bbwa-se, bwa mapping with single-end reads mode
cstampy-se, stampy- <dig> .
<dig> software mapping with single-end reads mode
discussion
following important advances in ngs technologies and target dna enrichment techniques  <cit> , wes is being used to identify variants associated with disease .
however, few studies have comprehensively investigated the accuracy of variant calling across different sequencing platforms.
this report focused on the variants detected by proton and hiseq  <dig> combined with different exome enrichment kits.
because of differences in the target regions between the ion targetseq™ exome enrichment kit and sureselect human all exon v <dig> kit, we considered only  <dig>  mb of overlapping regions between the two kits and evaluated the accuracy of three kinds of variant in four samples: concordant, targetseq-proton-specific and sureselect-hiseq-specific snvs.
the results showed that ~70 % of variants were concordant and ~30 % were platform-specific.
additionally, the validation rate of concordant snps and 1-bp indels was found to be highest, up to  <dig>  % and  <dig>  %, respectively, followed by sureselect-hiseq-specific , then targetseq-proton-specific at just  <dig>  % for snps and  <dig>  % for 1-bp indels .
parla et al.
previously observed that the seqcap ez human exome kit  and sureselect human all exon kit performed similarly in target capturing and sequencing  <cit> , whereas chilamakuri et al.
reported that four exonic dna enrichment kits  performed well in a systematic comparison of exome database coverage, target capturing efficiency, and gc bias  <cit> .
in the present report, the agilent sureselect v <dig> kit used 120-bp rna probes with a gc content of  <dig>  ±  <dig>  %, whereas the ion targetseq™ exome enrichment kit used variable length dna probes of  <dig>  ±  <dig>  bp with a gc content of  <dig>  ±  <dig>  %.
these differences may affect the gc content of reads  and the coverage of specific loci, which can influence variant calling, although the global coverage was similar at the level of 10× sequencing depth .
the notable discrepancy of variant calling between sureselect-hiseq and targetseq-proton sequencing platforms can be explained in part by different capturing efficiency of exome enrichment kits and the inadequate sequencing depth of platform-specific loci.
for example, of  <dig> sureselect-hiseq-specific variants in sample s <dig>  only  <dig>  %  were not covered and  <dig>  %  were covered at ≤10× by targetseq-proton reads; among  <dig> targetseq-proton-specific variants,  <dig>  %  were not sequenced and  <dig>  %  were sequenced at ≤10× by the sureselect-hiseq strategy.
thus, partial one platform-specific variants can also be detected by another platform when sequencing coverage increases.
the discrepancy mainly results from other factors such as characters intrinsic in sequencing platforms, read alignment and variant calling methods.
although the detailed indel error rate was unavailable in our study, the proton sequencing platform biases indel errors because its underlying sequencing principle is the same as that of ion torrent personal genome machine .
in untrimmed bases of pgm, the error rate varies from  <dig>  to  <dig>  % for insertion errors and from  <dig>  to  <dig>  % for deletion errors  <cit> .
to minimise the impact of indel errors produced by the proton sequencing platform, base calling using torrent suite software was performed with fairly stringent filters.
this decreased the number of variants detected by proton reads, as shown by the fact that several sureselect-hiseq-specific validated variants were not detected by targetseq-proton although they were covered by proton reads.
compared with variants called by torrent suite software, about 90 % of variants  called only by the bwa-gatk pipeline were novel small indels, which represents a high possibility of false positives .
this shows that the tvc pipeline, optimised to deal with varied length reads and error profiles specific to proton system, processed the proton data much better than the bwa-gatk pipeline.
characterization of the flanking 10-bp reference regions of the 1-bp small indels showed that ~70 % loci were in homopolymer regions .
moreover, hiseq  <dig> detected 1-bp indels more sensitively than proton , even in the region with a homopolymer size of ≥10 bp.
by contrast, the homopolymer size of indel regions detected by proton rarely exceeded  <dig>  our observation that proton reads were slightly biased to indel errors occurring in homopolymer types a and t  was less than that previously shown for hiseq reads  <cit> .
this suggests that a more accurate variant calling method should be developed for use of the proton platform to detect small indels.
as a biologically relevant and prevalent form of genetic variation  <cit> , more than  <dig>  indels in a diverse population have been mapped to known genes, some of which can be associated with genetic diseases  <cit> .
our analysis revealed a substantial difference in the indels detection ability between proton and hiseq  <dig>  which was also observed in previous studies  <cit> .
similarly, low concordant indels called by different pipelines have also been reported previously  <cit> .
the low validation rate of variants specific to targetseq-proton showed that proton has a high false positive rate of calling small indels or snps.
recently, a new open source algorithm, scalpel, has been developed  <cit> .
this combines mapping, assembly, and repeat analysis, and is coupled with a self-tuning k-mer strategy for the sensitive and specific discovery of indels in exome capture data.
scalpel outperforms other indel calling approaches  for indel discovery, particularly in regions containing near-perfect repeats, and has the power to detect long  transmitted events as well as enriching likely gene-disrupting indels in autistic children.
however, it is unknown whether scalpel is suitable for proton fragment reads because it was developed for illumina hiseq  <dig> paired-end reads.
our comparison of different snv calling pipelines for hiseq  <dig> data revealed that two single-end mapping methods for hiseq  <dig> reads slightly decreased the number and accuracy of snps .
this suggests that paired-end sequencing and mapping should be performed if possible.
our data also demonstrated that hiseq  <dig> and proton platforms are partially complementary for variant detection.
to obtain truly comprehensive exonic variants, wes should be performed on different platforms with deep paired-end coverage.
CONCLUSIONS
we detected snps and small indels of four whole exomes using torrent suite software  <dig> .
<dig> for targetseq-proton data and using bwa-gatk for sureselect-hiseq data.
we observed a notable discrepancy in variant calling between hiseq  <dig> and proton sequencing platforms.
a more comprehensive set of variants could be obtained by combining deep sequencing from hiseq  <dig> and proton.
among the different subsets of variants, the sanger sequencing validation of concordant variants was higher than that of variants specific to sureselect-hiseq or targetseq-proton sequencing strategies.
for sequencing platform-specific variants, sureselect-hiseq achieved a higher level of accuracy in variant calling than targetseq-proton, specifically for indel detection.
the combination of deep paired-end sequencing on different sequencing platforms, alongside the development and application of multiple variant calling tools, will effectively maximise the sensitivity and specificity of variant detection in biomedical applications.

 whole genome sequencing comprehensively investigates genome sequence changes such as single-nucleotide variants, insertions and deletions, chromosomal rearrangements, and copy-number variation, and so on. however, whole exome sequencing has become more popular because exons are more interpretable than other genomic regions and because the technique allows more samples to be analysed. previous studies have analysed disease loci that segregate in families, and large disease cohorts, and validated their findings in wgs studies using exome sequencing technology. other groups have assessed the clinical application of next generation sequencing by target sequencing. with recent advances in ngs technology, it is now possible to sequence the whole genomic or exonic dna of an individual. compared with traditional single nucleotide polymorphism arrays, wgs can generate target dna sequences and identify substantially more genetic variations, thus explaining a larger fraction of human phenotypic diversity. currently, the most widely used sequencing platform in human genome sequencing research is the illumina hiseq series of instruments, which use highly-parallel optical sensing of polymerization reactions to achieve an ultra-high throughput. life technologies has also released a new version of the semiconductor sequencing platform, ion proton, which provides researchers with an alternative sequencing platform. proton has a medium-throughput, cost effectiveness and rapid turnaround time. thus, proton is an attractive means of validating the variants called in whole genomes by other sequencing platforms, sequencing of whole exomes, screening cancer-related genes in solid tumours, or conducting sequencing-based clinical applications such as prenatal diagnosis which has a strict turn-around time requirement. however, given the differences between sequencing technologies and subsequent variant calling pipelines applied by hiseq and proton, it is necessary to comprehensively compare the two platforms. previously, the variant calling performance of the proton sequencer was assessed by comparing it with variants called by hiseq, complete genomics, and illumina snp microarray. another team used the proton sequencer to validate the whole exome variants called by wgs on the hiseq sequencer. in the present study, we comprehensively compared the differences between variants called by hiseq with the agilent sureselect human all exon v kit and proton with the ion targetseq™ exome enrichment kit, and validated the variants by sanger sequencing. our results show that there is a significant discrepancy between sureselect-hiseq and targetseq-proton sequencing strategies, and provide some guidance for analysing personal genome on different sequencing platforms.  data summary of exome sequencing exonic dna from four individual chinese genomic dna samples was captured by the ion targetseq™ exome enrichment kit using probes of various lengths, and subsequently sequenced by the proton sequencing platform. we obtained approximately 40× average sequence coverage on targeted regions. for all samples, proton reads covered more than 90 % of the targeted region with ≥10× reads coverage.table statistics of reads and alignment to reference genome for four samples’s exome sequencing on targetseq-proton/sureselect-hiseq platform exonic dna from the same four samples was separately enriched by sureselect human all exon v with 120 bp probes, then sequenced on hiseq with 2*100 bp read lengths. we obtained ≥33× sequence coverage on targeted regions, and more than 86 % of target regions had ≥10× reads coverage. definition of the evaluation region we chose the overlapping  mb exonic regions as an evaluation region between the ion targetseq™ exome enrichment kit and the agilent sureselect v kit. a total of,,, and variant loci were detected by proton and hiseq in samples s s s and s respectively. the co-detected rates of total variant loci were  %,  %,  % and  %, respectively, on two sequencing platforms for four samples. the analyses of the four samples were consistent when evaluating the numbers and co-detection rates of loci observed. therefore, we randomly chose to describe the results of sample s in this report.table variant loci detected by targetseq-proton and sureselect-hiseq sequencing atotal loci: all variant loci in the overlapping regions detected by hiseq or ion proton sequencing, which include the concordant, disconcordant, targetseq-hiseq-specific and sureselect-proton-specific loci bco-detected loci: the variant loci co-detected by targetseq-hiseq and sureselect-proton sequencing, which include concordant and disconcordant loci. the number in parentheses is percentage cconcordant loci: the variant loci with the same genotype detected by between targetseq-hiseq and sureselect- proton sequencing ddisconcordant loci: the loci with different variant genotype detected by between targetseq-proton and sureselect-hiseq. hom/het refers to the loci whose variant genotype is homozygotes detected by targetseq-proton, but heterozygotes detected by sureselect-hiseq. hom/hom, het/hom and het/het refer to analogous variant genotype when considering snp loci, we evaluated the ratio of transitions to transversions because unusually high or low ratios may be indicative of false positive variants. overall, ti/tv was in the total detected snps of sample s. for concordant snps, the ti/tv ratio was. by contrast, notable differences were observed in the ratios of hiseq 2000-specific or proton-specific snps, regardless of whether they were novel or known in dbsnp. comparison of variants-detecting platforms for sample s a total of variant loci were detected by proton or hiseq of which loci were also detected by proton and hiseq concurrently. the concordance of co-detected variant loci reached  %. concordance was determined by the loci with the same variant genotype. among the concordant variants, snps and small indels were identified. of the snps,  % were reported in dbsnp, while  % of the small indels were also reported in this database. of the total variants only detected by hiseq  % of snps and  % of small indels were reported in dbsnp. however, this was true of only  % of the snps and  % of the small indels among the total variants specific to proton.table pairwise comparison of variants called for four samples by targetseq-proton and sureselect-hiseq athe numbers of parentheses refer to known or unknown variant loci in dbsnp databases we observed a notable difference in the size distribution of indels calling by the two sequencing platforms as well as the percentage that had been previously reported in dbsnp. figure  shows the size distributions of both concordant and platform-specific indels. among all concordant small indels,  % were 1-bp, which is similar to that of hiseq 2000-specific. however, this value was  % for proton-specific small indels. analysis of the composition and homopolymer size of 1-bp indel loci flanking sequences showed that 1-bp indels called by proton were biased toward homopolymer types g and c.fig. distribution of size and classification of small indel called by exome sequencing on sureselect-hiseq and targetseq-proton in sample s four classes of small indel were defined as concordant novel, concordant known, specific novel and specific known. novel refers to indels not reported in dbsnp build known refers to indels previously reported in dbsnp build a showed the size and classification of small indels called by targetseq-proton. b showed the size and classification of small indels called by sureselect-hiseq among the discordant variant loci, the majority were heterozygote calls by proton but homozygote calls by hiseq. additionally, discordant loci were homozygous calls by proton to heterozygous calls by hiseq a few discordant loci consisted of different heterozygotes and different homozygotes co-detected by proton and hiseq validation by sanger sequencing to validate variants called by the two sequencing platforms, we pcr-amplified genomic dna fragments containing selected snps and small indels, then sequenced them. a total of snps of all four samples were randomly selected for validation: hiseq 2000-specific, proton-specific and concordant snps. of all snps,  % were successfully amplified and sequenced. the validation rate was  % for concordant snps,  % for hiseq 2000-specific and  % for proton-specific snps.table sanger sequencing validation comparison on variant subsets of targetseq-proton and sureselect-hiseq data calls a total of snps and 1-bp indels from four samples were randomly selected for sanger sequencing validation, with loci from the set of targetseq-proton-specific, from the set of sureselect-hiseq-specific, and from the set of concordance between two platforms as the small indels biased toward 1-bp indels, we selected concordant, hiseq 2000-specific, and proton-specific 1-bp indels for sanger sequencing validation. of all indels,  % were successfully amplified and sequenced. concordant and hiseq 2000-specific indels had validation rates of  % and  %, respectively. however, the validation rate of proton-specific indels was only  %. comparisons of variants-detecting pipelines for the proton sequencing platform, the bwa-gatk pipeline were shown to call more than twice as many variants as the torrent variant caller pipeline. variant concordance between the two pipelines was only  %. we also noted that the concordance of novel variants was much lower than that of known variants. although the concordance of snps between the two pipelines was  % among all snps, the indel concordance was extremely low, at just  % for all indels. for the hiseq sequencing platform, we also investigated the differences between variants detected by bwa-gatk and stampy-gatk pipelines. a  % concordance rate for all variants was observed, with the overall concordance of novel variants shown to be much lower than that of known variants. the  % concordance of snps was also higher than the  % concordance of indels. validated rates of three calling pipelines which differ only in read mapping, were also compared: bwa-se refers to bwa mapping of hiseq reads to the human reference with the single-end reads mode, bwa-pe uses the paired-end read mode, and stampy-se uses stampy-. software with the single-end read mode. in the sets of sanger sequencing validated variants, the bwa-pe pipeline called snps, which was more than bwa-se and stampy-se, which called and snps, respectively. additionally, the validated rate of snps called by the bwa-pe pipeline was higher than that of bwa-se and stampy-se.table comparison of the validation rates of variants called by different pipelines for sureselect-hiseq data note: abwa-pe, bwa mapping with paired-end reads mode bbwa-se, bwa mapping with single-end reads mode cstampy-se, stampy-. software mapping with single-end reads mode discussion following important advances in ngs technologies and target dna enrichment techniques, wes is being used to identify variants associated with disease. however, few studies have comprehensively investigated the accuracy of variant calling across different sequencing platforms. this report focused on the variants detected by proton and hiseq combined with different exome enrichment kits. because of differences in the target regions between the ion targetseq™ exome enrichment kit and sureselect human all exon v kit, we considered only  mb of overlapping regions between the two kits and evaluated the accuracy of three kinds of variant in four samples: concordant, targetseq-proton-specific and sureselect-hiseq-specific snvs. the results showed that ~70 % of variants were concordant and ~30 % were platform-specific. additionally, the validation rate of concordant snps and 1-bp indels was found to be highest, up to  % and  %, respectively, followed by sureselect-hiseq-specific, then targetseq-proton-specific at just  % for snps and  % for 1-bp indels. parla et al. previously observed that the seqcap ez human exome kit and sureselect human all exon kit performed similarly in target capturing and sequencing, whereas chilamakuri et al. reported that four exonic dna enrichment kits performed well in a systematic comparison of exome database coverage, target capturing efficiency, and gc bias. in the present report, the agilent sureselect v kit used 120-bp rna probes with a gc content of  ±   %, whereas the ion targetseq™ exome enrichment kit used variable length dna probes of  ±   bp with a gc content of  ±   %. these differences may affect the gc content of reads and the coverage of specific loci, which can influence variant calling, although the global coverage was similar at the level of 10× sequencing depth. the notable discrepancy of variant calling between sureselect-hiseq and targetseq-proton sequencing platforms can be explained in part by different capturing efficiency of exome enrichment kits and the inadequate sequencing depth of platform-specific loci. for example, of sureselect-hiseq-specific variants in sample s only  % were not covered and  % were covered at ≤10× by targetseq-proton reads; among targetseq-proton-specific variants,  % were not sequenced and  % were sequenced at ≤10× by the sureselect-hiseq strategy. thus, partial one platform-specific variants can also be detected by another platform when sequencing coverage increases. the discrepancy mainly results from other factors such as characters intrinsic in sequencing platforms, read alignment and variant calling methods. although the detailed indel error rate was unavailable in our study, the proton sequencing platform biases indel errors because its underlying sequencing principle is the same as that of ion torrent personal genome machine. in untrimmed bases of pgm, the error rate varies from to  % for insertion errors and from to  % for deletion errors. to minimise the impact of indel errors produced by the proton sequencing platform, base calling using torrent suite software was performed with fairly stringent filters. this decreased the number of variants detected by proton reads, as shown by the fact that several sureselect-hiseq-specific validated variants were not detected by targetseq-proton although they were covered by proton reads. compared with variants called by torrent suite software, about 90 % of variants called only by the bwa-gatk pipeline were novel small indels, which represents a high possibility of false positives. this shows that the tvc pipeline, optimised to deal with varied length reads and error profiles specific to proton system, processed the proton data much better than the bwa-gatk pipeline. characterization of the flanking 10-bp reference regions of the 1-bp small indels showed that ~70 % loci were in homopolymer regions. moreover, hiseq detected 1-bp indels more sensitively than proton, even in the region with a homopolymer size of ≥10 bp. by contrast, the homopolymer size of indel regions detected by proton rarely exceeded our observation that proton reads were slightly biased to indel errors occurring in homopolymer types a and t was less than that previously shown for hiseq reads. this suggests that a more accurate variant calling method should be developed for use of the proton platform to detect small indels. as a biologically relevant and prevalent form of genetic variation, more than indels in a diverse population have been mapped to known genes, some of which can be associated with genetic diseases. our analysis revealed a substantial difference in the indels detection ability between proton and hiseq which was also observed in previous studies. similarly, low concordant indels called by different pipelines have also been reported previously. the low validation rate of variants specific to targetseq-proton showed that proton has a high false positive rate of calling small indels or snps. recently, a new open source algorithm, scalpel, has been developed. this combines mapping, assembly, and repeat analysis, and is coupled with a self-tuning k-mer strategy for the sensitive and specific discovery of indels in exome capture data. scalpel outperforms other indel calling approaches for indel discovery, particularly in regions containing near-perfect repeats, and has the power to detect long transmitted events as well as enriching likely gene-disrupting indels in autistic children. however, it is unknown whether scalpel is suitable for proton fragment reads because it was developed for illumina hiseq paired-end reads. our comparison of different snv calling pipelines for hiseq data revealed that two single-end mapping methods for hiseq reads slightly decreased the number and accuracy of snps. this suggests that paired-end sequencing and mapping should be performed if possible. our data also demonstrated that hiseq and proton platforms are partially complementary for variant detection. to obtain truly comprehensive exonic variants, wes should be performed on different platforms with deep paired-end coverage.  we detected snps and small indels of four whole exomes using torrent suite software. for targetseq-proton data and using bwa-gatk for sureselect-hiseq data. we observed a notable discrepancy in variant calling between hiseq and proton sequencing platforms. a more comprehensive set of variants could be obtained by combining deep sequencing from hiseq and proton. among the different subsets of variants, the sanger sequencing validation of concordant variants was higher than that of variants specific to sureselect-hiseq or targetseq-proton sequencing strategies. for sequencing platform-specific variants, sureselect-hiseq achieved a higher level of accuracy in variant calling than targetseq-proton, specifically for indel detection. the combination of deep paired-end sequencing on different sequencing platforms, alongside the development and application of multiple variant calling tools, will effectively maximise the sensitivity and specificity of variant detection in biomedical applications. 
with regard to 1-bp small insertions and deletions, the sanger sequencing validated rates of concordant variants and sureselect-hiseq-specific were higher than those of targetseq-proton-specific. however, for the sequencing of platform-specific variants, the accuracy of variant calling by hiseq was higher than that of ion proton, specifically for the indel detection. ion proton, the latest updated semiconductor-based sequencing instrument from life technologies, is designed to provide investigators with an inexpensive platform for human whole exome sequencing that achieves a rapid turnaround time. the rates of shared variant loci called by two sequencing platforms were from to  % in four samples, whereas the concordance of co-detected variant loci reached 99 %. in the sequencing of exonic regions, a combination of using of two sequencing strategies increased the variant calling specificity for concordant variant loci and the sensitivity for variant loci called by any one platform. however, few studies have comprehensively compared and evaluated the accuracy of variant calling between ion proton and illumina sequencing platforms such as hiseq which is the most popular sequencing platform for the human genome. the ion proton sequencer combined with the ion targetseq™ exome enrichment kit together make up targetseq-proton, whereas sureselect-hiseq is based on the agilent sureselect human all exon v kit and the hiseq sequencer. moreover, the variant calling software also influences the detection of snps and, specifically, indels in ion proton exome sequencing. here, we sequenced exonic dna from four human blood samples using both targetseq-proton and sureselect-hiseq. to promote the clinical application of next-generation sequencing, it is important to obtain accurate and consistent variants of target genomic regions at low cost.
 to promote the clinical application of next-generation sequencing, it is important to obtain accurate and consistent variants of target genomic regions at low cost. ion proton, the latest updated semiconductor-based sequencing instrument from life technologies, is designed to provide investigators with an inexpensive platform for human whole exome sequencing that achieves a rapid turnaround time. however, few studies have comprehensively compared and evaluated the accuracy of variant calling between ion proton and illumina sequencing platforms such as hiseq which is the most popular sequencing platform for the human genome. the ion proton sequencer combined with the ion targetseq™ exome enrichment kit together make up targetseq-proton, whereas sureselect-hiseq is based on the agilent sureselect human all exon v kit and the hiseq sequencer.  here, we sequenced exonic dna from four human blood samples using both targetseq-proton and sureselect-hiseq. we then called variants in the exonic regions that overlapped between the two exome capture kits. the rates of shared variant loci called by two sequencing platforms were from to  % in four samples, whereas the concordance of co-detected variant loci reached 99 %. sanger sequencing validation revealed that the validated rate of concordant single nucleotide polymorphisms was higher than the snps specific to targetseq-proton or specific to sureselect-hiseq. with regard to 1-bp small insertions and deletions, the sanger sequencing validated rates of concordant variants and sureselect-hiseq-specific were higher than those of targetseq-proton-specific.  in the sequencing of exonic regions, a combination of using of two sequencing strategies increased the variant calling specificity for concordant variant loci and the sensitivity for variant loci called by any one platform. however, for the sequencing of platform-specific variants, the accuracy of variant calling by hiseq was higher than that of ion proton, specifically for the indel detection. moreover, the variant calling software also influences the detection of snps and, specifically, indels in ion proton exome sequencing. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords exome sequencingvariant callingaccuracyissue-copyright-statement© the author 2015 