The ICR142 NGS validation series: a resource for orthogonal assessment of NGS analysis

To provide a useful community resource for orthogonal assessment of NGS analysis software, we present the ICR142 NGS validation series. The dataset includes high-quality exome sequence data from 142 samples together with Sanger sequence data at 704 sites; 416 sites with variants and 288 sites at which variants were called by an NGS analysis tool, but no variant is present in the corresponding Sanger sequence. The dataset includes 293 indel variants and 247 negative indel sites, and thus the ICR142 validation dataset is of particular utility in evaluating indel calling performance. The FASTQ files and Sanger sequence results can be accessed in the European Genome-phenome Archive under the accession number EGAS00001001332.

analysed with multiple aligner/caller combinations [1][2][3][4][5][6] . To date we have generated Sanger sequence data for 704 sites amongst the 142 individuals. These sites include variants called by only one aligner and caller combination, increasing the representation of sites which can discriminate performance between methods.
To generate the Sanger sequence data, we performed PCR reactions using the Qiagen Multiplex PCR kit, and bidirectional sequencing of resulting amplicons using the BigDye terminator cycle sequencing kit and an ABI3730 automated sequencer (ABI PerkinElmer). All sequencing traces were analysed with both automated software (Mutation Surveyor version 3.10, SoftGenetics) and visual inspection.
To determine if a variant was present we visually inspected each Sanger sequence with Chromas software v2.13. For each site we selected an ENST from release 65 as the reference sequence. We reviewed at least 100 base pairs of sequence flanking each variant site to allow for position/annotation errors. We considered a base substitution to be confirmed if the correct variant was called at the exact position and the variant base signal was accompanied by a corresponding reduction in the reference base signal. There were 123 confirmed base substitution variants. We considered an indel variant to be confirmed if an indel variant was present in the region of interest and the indel variant allele signal was present along the complete length of the region of interest. There were 293 confirmed indel variants.
We considered a site negative for a base substitution if the specific base substitution was not present, resulting in 41 negative base substitution sites. We considered a site negative for an indel if no indel, of any kind, was detected in the 200 base pair region of interest, resulting in 247 negative indel sites ( Figure 1). We annotated confirmed variants with the HGVS-compliant CSN standard using CAVA (version 1.1.0) according to the transcripts designated in Supplementary table 1 7 .
We have also generated high-quality exome sequencing data for the ICR142 NGS validation series. We prepared DNA libraries from 1.5 µg genomic DNA using the Illumina TruSeq sample preparation kit. DNA was fragmented using Covaris technology and the libraries were prepared without gel size selection. We performed target enrichment in pools of six libraries (500 ng each) using the Illumina TruSeq Exome Enrichment kit. The captured DNA libraries were PCR amplified using the supplied paired-end PCR primers. Sequencing was performed with an Illumina HiSeq2000 (SBS Kit v3, one pool per lane) generating 2×101 bp reads. CASAVA v1.8.1 (Illumina) was used to demultiplex and create FASTQ files per sample from the raw base call files.
All of the 704 sites had at least 15× coverage in the exome data, defined as at least 15 reads of good mapping quality (mapping score ≥20). Because these sites are well covered, we can readily assess the variant calling performance of any software tool by applying the pipeline to the exome sequencing data and comparing the variant calls with the Sanger sequencing dataset.

Introduction
Next-generation sequencing (NGS) approaches have greatly enhanced our ability to detect genetic variation. Over the past decade NGS hardware, software, throughput, data quality and analytical tools have evolved dramatically. Thorough evaluation of each new laboratory and analytical development is challenging but necessary to fully understand how pipeline modification can impact results. To fully assess performance, NGS analysis tools should ideally be run on samples with pre-determined positive and negative sites assessed through orthogonal experimentation such as Sanger sequencing.
Over the past five years, we have generated extensive data on thousands of samples using different NGS instruments, sequencing chemistry, gene panels, exome captures and variant calling tools. Fortuitously, during this process we have generated orthogonal validation data using Sanger sequencing for a core set of 142 samples that were included in the majority of our experiments. We now formally use these samples, which we call the ICR142 NGS validation series, to evaluate NGS variant calling performance after any change to experimental or analytical protocols. This series has proved an extremely useful resource for our assessment of NGS analysis in both the research and clinical settings. We believe that it may also have utility for others, and hence are making it available here.

Materials and methods
We used lymphocyte DNA from 142 unrelated individuals. All individuals were recruited to the BOCS study and have given informed consent for their DNA to be used for genetic research. The study is approved by the London Multicentre Research Ethics Committee (MREC/01/2/18).
Over the last five years we have generated data from the ICR142 validation series using different exome captures which we have

Data availability
We have deposited the FASTQ files for all 142 individuals in the European Genome-phenome archive (EGA). The accession number is EGAS00001001332.
Researchers and authors that use the ICR142 NGS validation series should reference this paper and should include the following acknowledgement: "This study makes use of the ICR142 NGS validation series data generated by Professor Nazneen Rahman's team at The Institute of Cancer Research, London".
Author contributions N.R. and E.Ru. designed the experiment. A.R., E.Ra. and SH generated the exome data. E.Ru. and A.E. undertook data management, S.S., A.R., and K.S. undertook sample management and Sanger validations. M.C. and A.S. undertook the data and administrative management required for data to be accessible. E.Ru. and N.R. wrote the manuscript. All authors contributed to the final manuscript.

Grant information
We acknowledge NHS funding to the NIHR Biomedical Research Centre at The Royal Marsden and the ICR. This study was funded by the Institute of Cancer Research, London.
I confirm that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

UK
The authors describe ICR142, a publicly available set of fastq files and confirmed true and false variants for validating analysis pipelines. This is an incredibly useful community resource that complements existing efforts like the Genome in a Bottle project by providing a set of validated, difficult regions to evaluate variant detection tools. I appreciate the efforts to make these test sets public; instead of having validation sets like these developed internally at clinical laboratories, we can collaborate and improve them publicly.
In collaboration with Oliver Hofmann at the Wolfson Wohl Cancer Research Center ( ) we obtained access to the data and were able to run a validation using bcbio https://twitter.com/fiamh variant calling ( ). In doing this, we tried to address a couple of http://bcbio-nextgen.readthedocs.io challenges for other users wanting to make immediate use of this data in their own in hour validation work: The truth sets are not easy to plug into existing validation frameworks. Most validation tools like rtg vcfeval and hap.py work from VCF format files, while this truth set is in a custom spreadsheet format with a mixture of methods for describing changes. You can use Platypus positions for many but need to use CSN descriptions or evaluated position for the remainder.
The truth sets don't appear to describe if we expect calls to be homozygous or heterozygous calls at each position.
Many existing validation approaches expect a single (or few) samples so coordinating checking and validation for all these samples can be a challenge.
As part of this review, we generated a set of configuration files and scripts to help make running validations with ICR142 easier ( ). https://github.com/bcbio/icr142-validation This comparison work also includes a set of comparisons with common callers (GATK HaplotypeCaller, FreeBayes and VarDict). Several of the Sanger validated regions without variants are false positives in at least 2 of the callers tested, so this dataset exposes some common issues with calling and filtering. It would be useful to hear the author's experience with validating callers using this benchmark set and if they have additional filters used to avoid these problems. Knowing a baseline expectation for results would help ensure that the users understand how correctly they've setup the validation resources.
No competing interests were disclosed. Competing Interests: I found the NGS dataset to be easily accessible, on request, from the European Genome-phenome archive and it comprises paired end fastq sequencing files generated by an Illumina sequencing system on the stated 142 individuals. The Sanger sequencing dataset is available as supplementary table 1 of the manuscript. This is a useful resource for evaluating variant calling pipelines.
No competing interests were disclosed.

Competing Interests:
I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias You can publish traditional articles, null/negative results, case reports, data notes and more The peer review process is transparent and collaborative Your article is indexed in PubMed after passing peer review Dedicated customer support at every stage For pre-submission enquiries, contact research@f1000.com