Abstract The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications.

Citation: Kavak P, Yüksel B, Aksu S, Kulekci MO, Güngör T, Hach F, et al. (2015) Robustness of Massively Parallel Sequencing Platforms. PLoS ONE 10(9): e0138259. https://doi.org/10.1371/journal.pone.0138259 Editor: Junwen Wang, The University of Hong Kong, HONG KONG Received: June 2, 2015; Accepted: August 27, 2015; Published: September 18, 2015 Copyright: © 2015 Kavak 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 Data Availability: All SRA files are available from the NCBI database (accession number(s) SRP021510).: http://www.ncbi.nlm.nih.gov/sra/?term=SRP021510. Funding: The project is supported by the Republic of Turkey Ministry of Development Infrastructure Grant (no: 2011K120020) and BILGEM—TUBITAK (The Scientific and Technological Research Council of Turkey) (grant no: T439000) to M.S.S. and B.Y., and a Marie Curie Career Integration Grant (303772) to C.A. The funder Republic of Turkey Ministry of Development Infrastructure (Award Number: 2011K120020) provided financial support in the form of Illumina HiSeq 2000 sequencing machine and preparation kits for data production and the funder BİLGEM–TÜBİTAK (The Scientific and Technological Research Council of Turkey) (Award Number: T439000) provided support in the form of salaries for authors (PK, MOK, MŞS), and the funder Marie Curie Career Integration Grant (Award Number: 303772) provided support in the form of salaries for author (CA) but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the “author contributions” section. Competing interests: The authors have declared that no competing interests exist. One of the author’s current affiliation is a commercial company. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.

Introduction The robustness and the reproducibility are the sine qua non of every data intended to be used for clinical applications. These factors have been the main issue hindering large scale applicability of array-based technologies for clinics. High throughput sequencing (HTS) offers alternative solutions to array based technologies with respect to genotyping, and HTS data are considered to be more robust and comprehensive. The performance of HTS platforms has been tested in various studies [1–3], but the robustness of HTS platforms still need to be systematically assessed. More specifically, it is of crucial importance to obtain accurate single nucleotide polymorphism (SNP), indel, and structural variation (SV) call sets in the sense that the calls made for specific SNPs or SVs should be solely dependent on the actual genotypes of sequenced individuals but not the location, time, or the platform of choice of the study. Here we investigate the robustness of the Illumina HiSeq platform, currently the most widely used HTS technology in genome sequencing. In order to achieve this, we resequenced the genomes of two individuals from the Turkish Genome Project [4] twice. The two genomes were previously sequenced once [4], using the Illumina HiSeq 2000 platform in BGI Shenzhen, and a second time through the same platform set up at the Turkish Advanced Genomics and Bioinformatics Research Group (TÜBİTAK İGBAM). Although the same model sequencing machines were used, roughly the same level of coverage was achieved, and identical tools were used with identical parameters, independent analysis of the SNP and indel calls revealed significant number of differences between the two trials. In particular, we noticed that roughly 280 thousand of the 3 million SNPs genotyped by the GATK [5] tool in one trial (e.g. BGI) or the other (e.g. TÜBİTAK) are unique to only one callset—implying that the reproducibility rate of SNP calls is ∼ 92%. Interestingly, the multisample calling option of GATK that jointly analyzes two WGS datasets simultaneously does not seem to substantially improve the reproducibility and thus accuracy of the results. In this study, we explore the “sources” of this loss of accuracy as a function of both quality scores and coverage levels in each of the samples. Although increase in coverage levels in each sample typically decreases the differences between the GATK calls for specific loci on the two samples, there are still some cases in which differences can not be attributed to low coverage or quality score differences. Our main contribution in this paper is a detailed investigation of the types and causes of exclusive variants within the call sets that are expected to be substantially the same. In addition, we try to identify strategies to handle such discrepancies when there is a second WGS dataset generated from the genome of the same donor. With further technological advancements and the cost improvements, sequencing a sample many times can be expected to be prevalent, as storing the data may become more expensive than resequencing the same sample. Here the same donor sample is sequenced twice, to evaluate the outcome of this highly possible situation in the future. For such cases, when there are more than one WGS sequence of the same donor, we state our remarks on how to exploit all the data fruitfully. In Section 1, we describe the methods used in the study. In Section 2, we present the results of the study and show the shared and exclusive sets of different SNP groups. And finally, in Section 3, we provide our remarks on the results and conclude.

1 Methods 1.1 DNA Samples and Ethics Statement Genomic DNA from two individuals were collected and purified in 2011, only once from the blood of two volunteers for a previously published study [4]. The source (i.e. blood), DNA extraction time and location are the identical. As indicated in [4], institutional review board permission was obtained from INAREK (Committee on Ethical Conduct in Studies Involving Human Subjects at the Boğaziçi University) before data collection, and all participants including those that are included in this study provided informed consent. 1.2 Sequencing The genomes of the two individuals were already sequenced using Illumina HiSeq2000 in 2011 at BGI Shenzhen [4]. The same samples were resequenced for a second time using another Illumina HiSeq2000 in 2012 at the TÜBİTAK İGBAM located in Kocaeli, Turkey. For the first sequencing data set, DNA samples were fragmented to 500bp, and paired-end sequencing data were generated with a read length of 90bp. For the second sequencing experiment at TÜBİTAK, we used the same protocols and sheared the DNA to 500bp fragments, and sequenced 104bp paired-end reads. In the remainder of the paper, we refer to the data generated at BGI as S 1B (first individual) and S 2B (second individual), and the data generated at TÜBİTAK for the same individuals as S 1T and S 2T . 1.3 Alignment, coverage, GC content To discover SNPs and short indels, we mapped the reads to the human reference genome (NCBI GRCh37) using the BWA aligner (version 0.6.2) [6], in paired-end mode (“sampe”) and default options. We converted the mapping output to sorted, duplicate-removed, and indexed BAM files using SAMtools [7]. We calculate the expected coverage as: Next, SAMtools and BEDtools [8] were used to calculate the effective coverage: Finally, we used the FASTQC tool (version 0.10.1) [9] to collect basic statistics of the genomic sequence data (Table 1). PPT PowerPoint slide

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larger image TIFF original image Download: Table 1. Summary of the sequence datasets. https://doi.org/10.1371/journal.pone.0138259.t001 1.4 Variant calling SNP and indel detection. After the initial alignment and the PCR-duplicate removal, we realigned the indel-containing reads to the reference genome using GATK Realigner tool. We then used the GATK UnifiedGenotyper tool to generate the SNP and indel call sets. We also used the GATK HaplotypeCaller as an alternative approach for variant calling. Next, we eliminated likely false positives using the GATK Variant Quality Score Recalibration (VQSR) tool with GATK resource bundle v2.5. Finally, we further filtered the call sets using the GATK VariantFiltration to remove low confidence calls (SnpCluster filter to remove SNPs if there are more than 3 SNPs in a 10 bp window). We applied the same variant calling pipeline to each of the four datasets separately: S 1B , S 1T , S 2B and S 2T . Pooled SNP and indel calling. As a second experiment, we tested whether pooling data from multiple sequencing runs for the same samples improve callset reproducibility. Our main question here was to understand if the slight differences in the coverage and depth of the datasets could be ameliorated by merging data for discovery, and if this would improve genotyping accuracy. For this purpose, we applied the SNP/indel detection pipeline to both samples by pooling two sequencing datasets (i.e. S 1BT , and S 2BT ) generated at BGI and TÜBİTAK. However, we named the two datasets from the same sample as if they were generated from different genomes. In the remainder of the paper, we denote the SNP/indels genotyped within the BGI data from S 1 as B 1 , and the SNP/indels genotyped within the TÜBİTAK data from S 1 as T 1 for this experiment. Similarly, we have B 2 and T 2 for the sample S 2 . 1.5 Variant annotation We used the ANNOVAR [10] tool (version 2013-02-21) to annotate SNPs and indels. 1.6 Data Availability We had previously deposited the sequence reads obtained from BGI to the SRA read archive (SRP021510). Primary run IDs relevant to this study are: SRR839600 for S 1B and SRR849493 for S 2B . Datasets generated at TÜBİTAK are also available as “secondary sequencing” data sets with sample IDs SRR2128004 and SRR2128088 respectively within the same SRA archive. We also released our scripts we used to map the reads and call the variants at https://github.com/pinarkavak/robust, and the VCF files for the call sets are available at http://alkanlab.org/paper-data/Kavak_RobustNGS/.

3 Discussion and Conclusion With the improvements in cost efficiency, speed, and analysis algorithms, HTS platforms are now being considered to be used routinely as part of health care. This assumption prompted a pilot project called ClinSeq [12] that aims to investigate the strength and potential pitfalls of using HTS data in the clinic. However, the HTS technologies continue to evolve and new platforms are introduced almost every month. This, coupled with changes and updates of algorithms to analyze HTS data raises questions about the maturity and robustness of HTS platforms for accurate discovery and genotyping of genomic variants. In an effort to answer this question, we analyzed the genomes of two individuals, each sequenced twice using the same technology, albeit at different locations. Since our aim was to investigate the maturity of sequencing platforms in this study, we used the same tools to characterize both single nucleotide and short indel variants. Under the assumption of 100% robustness, one would expect to characterize the same set of variants in both sequencing datasets from the same genomes, however, this is not what we found. We believe multiple factors contribute to this effect. First, since the library preparation is different, one may expect difference in GC% bias, as clearly seen in Table 1 of the manuscript. This leads to differences in read depth over different regions of the genome, which in turn causes discrepancies in variation calls. The GC% effect can also explain the over-representation of repeats and segmental duplications in terms of SNP discrepancies, as common repeats are high in GC content (41.45% GC within common repeats vs 40.33% GC in unique regions), together with difficulties in mapping to repeats and duplications. Second, although the make and model of the sequencing instruments are the same, they are individually different machines, which may account for slight differences in base calling errors. Third, mapping biases against repeats and duplications incur additional problems in terms of mapping and calling. We note that we used the same mapping and calling tools with the same parameters for all datasets in this study, therefore the tools should not be the reason for discrepancies. Although orthogonal methods are needed for definitive validations, we suggest that when there are more than one data set, one should use all the available data for higher accuracy. Sequencing machines, alignment and genomic variant discovery and genotyping algorithms change rapidly, and one must be careful when interpreting results. Here we demonstrated potential problems that may arise within HTS-based studies. Discrepancies between call sets generated from the same genomes may be complementary false positives and false negatives in each callset, in addition to common genotyping errors. Luckily, much of the differences were found within non-genic regions and common repeats, which are of less importance for most studies.

Acknowledgments We would like to thank Turkish Human Genome Project (TGP) members for sharing the DNA sample and data of the project, otherwise this study could not exist. TGP members include Mehmet Somel1,#a, Omer Gokcumen2, Serkan Uğurlu3, Ceren Saygi3, Elif Dal4, KuyaŞ Bugra3, Nesrin Özören3, and Cemalettin Bekpen3,#b. The lead authors of this project were Nesrin Özören (nesrin.ozoren@boun.edu.tr) and Cemalettin Bekpen (bekpen@evolbio.mpg.de). 1 Department of Integrative Biology, University of California, Berkeley, CA, USA 2 Department of Biological Sciences, University at Buffalo, Buffalo, NY, USA 3 Department of Molecular Biology and Genetics, Boğaziçi University, İstanbul, Turkey 4 Department of Computer Engineering, Bilkent University, Ankara, Turkey #a Current Address: Department of Biology, Middle East Technical University, Ankara, Turkey #b Current Address: Max-Planck Institute for Evolutionary Biology, August-Thienemannstrasse 2, Plön, Germany

Author Contributions Conceived and designed the experiments: MŞS SCŞ BY MOK. Performed the experiments: BY SA. Analyzed the data: PK CA MŞS. Contributed reagents/materials/analysis tools: PK CA MŞS FH. Wrote the paper: CA PK MŞS SCŞ BY TG.