Literature DB >> 31913291

Evaluating sequence data quality from the Swift Accel-Amplicon CFTR Panel.

Marco L Leung1, Deborah J Watson2, Courtney N Vaccaro2, Fernanda Mafra2, Adam Wenocur2, Tiancheng Wang2, Hakon Hakonarson2,3, Avni Santani4,5.   

Abstract

Cystic fibrosis (CF) is one of the most common genetic diseases worldwide with high carrier frequencies across different ethnicities. Next generation sequencing of the cystic fibrosis transmembrane conductance regulator (CFTR) gene has proven to be an effective screening tool to determine carrier status with high detection rates. Here, we evaluate the performance of the Swift Biosciences Accel-Amplicon CFTR Capture Panel using CFTR-positive DNA samples. This assay is a one-day protocol that allows for one-tube reaction of 87 amplicons that span all coding regions, 5' and 3'UTR, as well as four intronic regions. In this study, we provide the FASTQ, BAM, and VCF files on seven unique CFTR-positive samples and one normal control sample (14 samples processed including repeated samples). This method generated sequencing data with high coverage and near 100% on-target reads. We found that coverage depth was correlated with the GC content of each exon. This dataset is instrumental for clinical laboratories that are evaluating this technology as part of their carrier screening program.

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Year:  2020        PMID: 31913291      PMCID: PMC6949293          DOI: 10.1038/s41597-019-0339-4

Source DB:  PubMed          Journal:  Sci Data        ISSN: 2052-4463            Impact factor:   6.444


Background & Summary

Cystic fibrosis (CF) is considered one of the most common genetic diseases, affecting 1 in 2500–3500 live births in Caucasian populations[1]. Over 1500 mutations have been previously reported in the CFTR gene. Due to the high carrier rates, the American College of Obstetricians and Gynecologists (ACOG) suggests CF carrier testing for all women who are considering pregnancy or are currently pregnant[2-4]. In 2004, the American College of Medical Genetics and Genomics (ACMG) published a guideline on testing 23 CFTR mutations with high carrier frequencies across different ethnicities[3]. However, to increase the detection rate, it has become a common practice for clinical laboratories to expand the CFTR panel to more than 100 mutations, and even full gene analysis[5-7]. In the past three decades, the detection of CFTR mutations has evolved through various molecular methods, including reverse dot blot, restriction fragment length polymorphism (RFLP), and Sanger sequencing[8,9]. The advent of next generation sequencing (NGS) leads to a higher clinical sensitivity by screening more targeted CFTR mutations and sequencing of the exonic gene regions, as well as a higher throughput by multiplexing many samples into one sequencing run[10,11]. While NGS excels at generating large amount of data, it is time-consuming and less cost-effective for sequencing few targets and low volume of samples. Recently, Swift Biosciences released a pre-designed amplicon/library preparation kit that can amplify the CFTR gene using 87 amplicons in one reaction. Combined with Illumina MiSeq Nano kit v2 (300-cycles), this protocol allows for quick turnaround time, low sample volume, and cost effectiveness. While a previous study had demonstrated that this method could detect frequent and rare CFTR mutations when compared to other methods, the technical specifications were not analysed[12]. Here we examine the Accel-Amplicon CFTR Panel using CF-positive samples by assessing the performance of this assay. We processed seven CF-positive samples that represent across the CFTR mutation spectrum (missense, nonsense, splicing and indels), and these mutations are recommended in the ACMG guideline[3]. The first run included one normal sample and three CF-positive samples, and the second run included all samples from the first run, with additional four CF-positive samples (Table 1).
Table 1

Coverage statistics by samples.

Run:1111Run 1 Average2222222222Run 2 Average
Sample name:Sample 1Sample 2Sample 3Sample 4Sample 1Sample 2Sample 3Sample 4-1Sample 4-2Sample 4-3Sample 5Sample 6Sample 7Sample 8
Read % on target:98.3298.3898.5198.3198.3899.2699.2499.2999.2699.2499.2199.2599.2099.2099.1499.23
Mean coverage depth384540133598115535752992766168016471356147314051340143813441344
% of targeted region >20x100.00100.00100.00100.00100.0099.7399.7399.7399.7399.7399.7399.7199.7199.7199.7399.72
Number of reads33824435159032145010190245075779068670252154784148982124390135350129530123800132844122072123269
Coverage statistics by samples. Using the MiSeq Nano v2 kit, the sequencing coverage depth averages for run 1 (four samples) and run 2 (ten samples) are 5753x and 1344x, respectively, with almost 100% of the CFTR target region being more than 20x (Table 1). As expected for amplicon sequencing, 98–99% of sequencing reads are on-target. We analysed the sequencing performance on the exon level. The coding region, 5′UTR and 3′UTR of the CFTR gene has 6123 bp, while the amplicon covers these regions with more than 3000 bp padded region (targeted amplicon size = 9666 bp), with additional amplicons covering four intronic regions (introns 1, 12, 22, and 25) (Table 2). The number of amplicons for each exon correlates with the size of the exons (R2 = 0.9766%) (Fig. 1).
Table 2

Coverage statistics by exons.

ExonLegacy exons# of ampliconschromstartendamplicon sizeexon size%GC per exonMean coverage in run 1Mean coverage in run 2
5′UTR/exon 15′UTR/exon 13711711996211712027631518549.0661671614
intron 1intron 11711713831611713839782n/an/a23361241
exon 2exon 21711714428011714447019111141.441938318
exon 3exon 32711714905311714931726510935.782649609
exon 4exon 43711717088511717119330921643.06105232370
exon 5exon 5271171742571171745472919034.442802677
exon 6exon 6a2711717524211717552228116451.2295522095
exon 7exon 6b2711717654711717678624012636.5138401087
exon 8exon 74711718010611718046936424744.1362551518
exon 9exon 8271171820011171822292299336.561558352
exon 10exon 92711718864011718888124218338.82021377
exon 11exon 102711719945611719973928419238.543458707
exon 12exon 11171172277471172279141689542.1182581781
intron 12intron 1127117229400117229594195n/an/a2062730
exon 13exon 12271172303791172305521748728.742472685
exon 14exon 138711723191411723275684372440.8880441770
exon 15exon 14a3711723485611723517331812937.982200572
exon 16exon 14b171172428411172429781382852.63104283268
exon 17exon 153711724355411724388733425141.04112542708
exon 18exon 16271172466321172468652348037.52422653
exon 19exon 17a2711725054211725081327215139.073944901
exon 20exon 17b3711725151711725199547922840.793318660
exon 21exon 181711725460911725480419610142.573741691
exon 22exon 194711726753911726788534725042.9772651831
intron 22intron 191711727995011728004798n/an/a46771872
exon 23exon 203711728246711728275528915644.8771791671
exon 24exon 21271172927961172930762819032.222681510
exon 25/intron 25exon 22/intron 223711730458611730496638117349.7190762349
exon 26exon 232711730545811730579333610634.915435831
exon 27/3′UTRexon 24/3′UTR1871173068911173087551865175852.2468761555
Fig. 1

Correlation of amplicon numbers and exon size. The numbers of amplicons for each exon is plotted against the exon size, except intron 1, 12, and 22. A trendline is plotted from the data and R2 is calculated to be 0.9766.

Coverage statistics by exons. Correlation of amplicon numbers and exon size. The numbers of amplicons for each exon is plotted against the exon size, except intron 1, 12, and 22. A trendline is plotted from the data and R2 is calculated to be 0.9766. Using the manufacturer’s recommended bioinformatic pipeline, we were able to detect all the mutations in the CF-positive samples. No pathogenic variants were detected in sample 1 (normal control) in both runs. Repeated samples in the inter- and intra-run analyses were found to be concordant (See technical validation section for more details). Here, we provide the FASTQ files for each of the samples in this validation study. Tables 1 and 2 provide the coverage summary for each sample and each exon. Furthermore, in the method and technical validation section, we describe the steps and quality control (QC) performed to ensure the accuracy and precision of the assay. To our knowledge, no previous studies have critically evaluated the sequencing performance of the Accel-Amplicon CFTR panel. As analytical performance of the methodology is vital for a clinical test, the data generated in this study can be evaluated by clinical genetic laboratories that are interested in employing the Accel-Amplicon CFTR panel to screen CF carriers. As carrier screening becomes more well-known and consumer demand increases, this method fulfils the need of an affordable and time-sensitive approach to screen CFTR mutations in general population carrier screening with a maximum detection rate.

Methods

Validation samples acquisition and DNA quantification

The following DNA samples (samples 1–3, 5–8) were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research (see the corresponding Coriell naming convention in Table 3). Sample 4 was acquired from a patient; an informed consent was obtained for research using an IRB protocol (06-004886) at the Center for Applied Genomics at the Children’s Hospital of Philadelphia. The consent agreement states that genotype data may be shared with public data repositories for research purposes, and that the patient’s personal information would be kept private and unidentifiable in any publication or presentation. DNA concentration was calculated using a Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, catalogue number Q32851). Samples were diluted down to 5 ng/mL with Pre-PCR TE buffer and a final volume of 10 μL containing 20 ng input DNA was used.
Table 3

Sample manifest.

Sample:Sample 1Sample 2Sample 3Sample 4Sample 5Sample 6Sample 7Sample 8
CFTR Allele 1:n/ap.Arg117Hisc.489 + 1G > Tp.Phe508delp.Ile507delc.2657 + 5G > Ap.Arg1162Xc.3528delC
CFTR Allele 2:n/ap.Phe508delc.579 + 1G > Tn/an/ac.2657 + 5G > An/ap.Phe508del
Catalog Number:NA12878NA13591NA11280CF_Sample_4NA11277NA11859NA12585NA11275
Sample manifest.

Library preparation

Library preparation was performed using the Accel-Amplicon CFTR panel (Swift Bioscience, catalogue number AL-55048) in accordance with the manufacturer’s protocol. In brief, multiplex PCR was performed on the sample DNA using the reagents provided by the Accel-Amplicon panel kit for 4 cycles of 10 sec at 98 °C, 5 min at 63 °C, 1 min at 65 °C and 22 cycles of 10 sec at 98 °C, 1 min at 64 °C. Size selection and clean-up were performed using SPRIselect beads (Beckman Coulter, catalogue number B23318) with a ratio of 1.2. Indexing sequencing adapters were then ligated to each library at 37 °C for 20 minutes. A second clean-up step was performed using SPRIselect beads at a ratio of 0.85 and rediluted with 20 mL of Post-PCR TE buffer. Quantification of adapted libraries was performed by qPCR using KAPA Library Quantification Kit (KAPA Biosystems, catalogue number 07960140001).

Next-generation sequencing

Illumina MiSeq Nano Reagent Kit V2 was used to sequence the samples (Table 1). The final pooled concentration of 2 nM (5 μL was used) was mixed with 0.2 N NaOH (5 μL). The mixture was then mixed with 990 μL of pre-chilled HT1 to obtained a 10 pM denatured library mixed. No PhiX spike-in was used.

Bioinformatic analysis

Sequencing data was analysed based on the bioinformatic pipeline recommended and provided by Swift Biosciences. In short, adapter-trimmed paired-end FASTQ files were generated by the Illumina MiSeq upon completion of the sequencing run (Note: adapter trimming can be done post FASTQ generation). For each sample, an alignment in Sequence Alignment Map (SAM) format was generated from the pair of FASTQ files using Burrows-Wheeler Aligner (BWA) and hg19 human genome reference. The SAM file was further modified by SAMtools to sort the file by name for Swift primerclip preparation. Due to the presence of synthetic primer sequences at the start or end of reads, the primerclip tool was used to remove these sequences before proceeding with downstream analysis. With both Picard’s AddOrReplaceReadGroups tool and SAMtools, the primer-clipped SAM file was converted to BAM format and an indexed BAM file was generated. Variant calling was performed using GATK HaplotypeCaller. To determine quality metrics at the sample and interval level, Picard’s CollectTargetPcrMetrics was used.

Sanger sequencing

Pathogenic variants were confirmed using Sanger sequencing. PCR was performed using QIAGEN Fast Cycling PCR kit (#203743) with primers flanking the variants of interest (Tables 4 and 5). The PCR conditions were: 5 minutes at 95 °C, 35 cycles [5 seconds at 96 °C, 5 seconds at 58 °C, 40 seconds at 68 °C], 1 minute at 72 °C. PCR products were purified using Applied Biosystems ExoSAP-IT PCR Product Cleanup Reagent (#78201.1.ML). Sequencing reactions were performed using Applied Biosystems BigDye Terminator v1.1 Cycle Sequencing Kit (#4337449), and were purified using Applied Biosystem Centri-Seq. 8-Well Strips (#4367820). Sanger sequencing was performed using Applied Biosystems 3500 Genetic Analyzer (#4440462).
Table 4

Primer sequences for variant detection.

Primer NameSequences (5′ to 3′)Variants detected
Exon 4 FTGGCCACTATTCACTGTTTAACTTp.Arg117His; c.489 + 1G > T
Exon 4 RGAGGCAGTTTACAGAAGATACTCAA
Exon 5 FTTGAAAGAAACATTTATGAACCTGAc.579 + 1G > T
Exon 5 RCTATTATCTGACCCAGGAAAACTC
Exon 10 FCACTTCTGCTTAGGATGATAATTGGp.Ile507del; p.Phe508del
Exon 10 RCAGTAGCTTACCCATAGAGGAAACA
Exon 14b FCAGGAACACAAAGCAAAGGAAc.2657 + 5G > A
Exon 14b RCAGGAATGTGTCACCTCACC
Exon 19 FTGAAAAGCCCGACAAATAACCp.Arg1162X; c.3528delC
Exon 19 RACTTGTTTGGCAGAATGGAAC
Table 5

Sample file names as listed in SRA.

SampleRun1Run2
1SRR8945290_1_1.fastqSRR10164005_1_1.fastq
SRR8945290_1_2.fastqSRR10164005_1_2.fastq
2SRR8945291_3_1.fastqSRR8945291_2_1.fastq
SRR8945291_3_2.fastqSRR8945291_2_2.fastq
3SRR8945292_4_1.fastqSRR8945292_6_1.fastq
SRR8945292_4_2.fastqSRR8945292_6_2.fastq
4SRR8945293_2_1.fastqSRR8945293_3_1.fastq
SRR8945293_2_2.fastqSRR8945293_3_2.fastq
SRR8945293_4_1.fastq
SRR8945293_4_2.fastq
SRR8945293_5_1.fastq
SRR8945293_5_2.fastq
5SRR8945286_7_1.fastq
SRR8945286_7_2.fastq
6SRR8945287_8_1.fastq
SRR8945287_8_2.fastq
7SRR8945288_9_1.fastq
SRR8945288_9_2.fastq
8SRR8945289_10_1.fastq
SRR8945289_10_2.fastq
Primer sequences for variant detection. Sample file names as listed in SRA.

BWA-MEM alignment

bwa mem ${FASTA} ${Sample_ID}_R1.fastq.gz ${Sample_ID}_R2.fastq.gz -U 17 -M -t 32 > ${Sample_ID}_bwa.sam.

SAMtools sort SAM

samtools sort -n ${Sample_ID}_bwa.sam -o ${Sample_ID}_bwa_nsorted.sam.

Primerclip

primerclip Accel-Amplicon_CFTR_masterfile.txt ${Sample_ID}_bwa_nsorted.sam ${Sample_ID}_bwa_primertrimmed.sam.

SAMtools convert SAM to BAM

java -jar picard.jar AddOrReplaceReadGroups I=${Sample_ID}_bwa_primertrimmed.sam O=${Sample_ID}_bwa_primertrimmed.bam SO=coordinate RGID=snpID LB=swift SM=${Sample_ID} PL=illumina PU=miseq VALIDATION_STRINGENCY=STRICT. samtools index ${Sample_ID}_bwa_primertrimmed.bam ${Sample_ID}_bwa_primertrimmed.bam.bai.

Picard CollectPcrMetrics tool

samtools view -H ${Sample_ID}_bwa_primertrimmed.bam > ${Sample_ID}_bwa_header.txt. cat ${Sample_ID}_bwa_header.txt cftr_180313_nonmerged_targets_5col.bed > ${Sample_ID}_bwa_fullintervals. cat ${Sample_ID}_bwa_header.txt cftr_180313_nonmerged_targets_5col.bed > ${Sample_ID}_bwa_noprimerintervals. java -jar picard.jar CollectTargetedPcrMetrics I=${Sample_ID}_bwa_primertrimmed.bam O=${Sample_ID}_bwa_targetPCRmetrics.txt AI=${Sample_ID}_bwa_fullintervals TI=${Sample_ID}_bwa_noprimerintervals R=${FASTA} PER_TARGET_COVERAGE=${Sample_ID}_bwa_perTargetCov.txt VALIDATION_STRINGENCY=STRICT.

GATK variant calling

java -jar GenomeAnalysisTK.jar -T HaplotypeCaller -R ${FASTA} -I ${Sample_ID}_bwa_primertrimmed.bam -stand_call_conf 20 -stand_emit_conf 20 -mbq 20 -L CFTR_merged_5col.bed -o ${Sample_ID}_bwa_gatkHC.vcf.

Data Records

There are eight unique samples in our cohort. Samples 1–4 were analysed in both runs. Samples 5–8 were analysed in run 2. Sample 4 was run in triplicate in the second run. fastq can be accessed from the Sequence Read Archive (SRA) repository under SRA: SRP193469[13]. Direct FASTQ files can be downloaded via SRA Toolkit using command line “fastq-dump–split-3 -G SRR#” (Table 5). BAM files can be downloaded at (10.6084/m9.figshare.11341958.v1), and VCF files can be downloaded at (10.6084/m9.figshare.10565513.v1)[14,15].

Technical Validation

Library quantitation

To evaluate whether the DNA samples were successfully processed using this Swift Accel Amplicon protocol, we used the KAPA Library Quantification Kit to measure the library concentration. During qPCR, primers bound to the Illumina P5 and P7 flow cell oligo sequences and the concentrations of the samples were assessed by measuring the SYBR green fluorescence intensity; this method specifically measures the adapted DNA, excluding any unadapted DNA fragments generated during the PCR step. The concentration of each sample in both runs are listed in Table 6.
Table 6

Sequencing quality assessment.

RunSampleConcentration (nM)Cluster Density (k/mm2)% Q30
1Sample 15.6807 ± 198.08
1Sample 24.5
1Sample 36.7
1Sample 42.6
2Sample 116.5534 ± 898.05
2Sample 214.6
2Sample 314.2
2Sample 4-114.5
2Sample 4-216.9
2Sample 4-316.0
2Sample 515.0
2Sample 619.1
2Sample 710.7
2Sample 812.5
Sequencing quality assessment.

Sequencing data assessment

Pooled libraries were sequenced using Illumina MiSeq Nano Reagent Kit V2 kit (300 cycles). The cluster densities for run 1 and 2 were 807 ± 1 k/mm2 and 534 ± 8 k/mm2, with 98.08% and 98.05% of reads of Q30 score or more, respectively (Table 6). Further analyses of the FASTQ files using MultiQC showed that the majority of the base positions had mean quality value of Q38, while the first five bases of reads have lower quality scores (at around Q33) (Fig. 2a). For all FASTQ files, the majority of the reads had quality value of Q38 (Fig. 2b)[16]. Overall coverage depth of all processed samples is demonstrated in Table 1. As expected, the mean coverage depth in run 1 (5753x) is higher than those of run 2 (1344x), as there are fewer samples pooled into one flow cell in run 1 (Table 1). Moreover, all samples from run 1 have 100% of regions with more than 20x coverage depth (Table 1). For run 2, all samples have less than 20x coverage at the 3′UTR region (chr7:117308320–117308346; CFTR:c.*1158_*1184). This region has no known pathogenic variants described in HGMD or in ClinVar. In addition, samples 5, 6, and 7 have no coverage for two bases in intron 8 (chr7:117188661–117188662; CFTR:c.1210-13_1210-12). This is a common TG repeat deletion that is present in 22.92% of general population according to gnomAD. Next, we assessed the coverage depth per exon, and investigated the inter-exonic depth variability (Tables 2 and 7). We found that the coverage depth was higher as the GC content of the exon was closer to 50% for both runs (Fig. 2c,d). As expected for amplicon sequencing, the majority of sequencing reads (98–99%) were aligned to the targeted regions (See Supplementary File 1 for BED file).
Fig. 2

Sequence quality and coverage depth per exon. Sequence quality was assessed using MultiQC. Each green line represents one FASTQ file. (a) Mean quality value across each base position in the read. (b) Number of reads with average quality score. (c,d) For both runs, the coverage depth of exons increases as the GC content approaches 50%.

Table 7

Sequencing coverage depth per exon for each sample.

Run:11112222222222
Swift exon annotationSample 1Sample 2Sample 3Sample 4Sample 1Sample 2Sample 3Sample 4Sample 4Sample 4Sample 5Sample 6Sample 7Sample 8
5′UTR/exon 137284312400412623119389819531729152018681886166219021525
intron 11424123519124771101173415841228126114261415123513681146
exon 2136713599584068208181395352331463289311287363
exon 31737215015365175476382705636628761637653616597
exon 4683669436411219021756129528652555236529202630240325962312
exon 51857191215235914508398756766739944627636685713
exon 6647763725699196621524113625022250214627302139206322212242
exon 7259022582304820875861613381149121514351074110610441136
exon 841094346378612778115882418271703154018481622160316571393
exon 9106110389593174264204477441351479334347316304
exon 101384166412173817283192451442423469374412416307
exon 112434251218647022547394828840807907712634741664
exon 12580657384783167031232102622021874178824461716177317761974
intron 121404143414963916573415884797708901750775768726
exon 131703174715824855511346872786695857674663703746
exon 14550759975398152721288101022561799166019751987185119981880
exon 151551167013704210417324747625609699570587604536
exon 16707561917432210162308202842773478328339843353290934623600
exon 17777881377004220962029154234882881255131032926277730212763
exon 181652168114854868488371757735691824667676704621
exon 1926592826211781746465201082103189610299718851021930
exon 202298238519946595461382812748686825635644685725
exon 212556275019697690490367798788731888632697651871
exon 22474950514783144771400108822482067198921561841187819491689
intron 2228442607336098951500111722812157190823541980182619541641
exon 2346204976432514794130497221031932176620611702160716911575
exon 241739212615335327385302615568521659515485543511
exon 25/intron 25592261585744184821761131829922695233829062521222324682266
exon 263775404030411088462645898810389691152730799780774
exon 27/3′UTR45554687442513837112689620091702156019251602152716621539
Sequence quality and coverage depth per exon. Sequence quality was assessed using MultiQC. Each green line represents one FASTQ file. (a) Mean quality value across each base position in the read. (b) Number of reads with average quality score. (c,d) For both runs, the coverage depth of exons increases as the GC content approaches 50%. Sequencing coverage depth per exon for each sample.

Assay validation of CF-positive samples

Samples used in this validation study have known pathogenic CFTR mutations (Table 3), and they were used to validate this Swift Accel-Amplicon CFTR Panel for usage in a clinical laboratory setting. Analytical validation is a vital component in the process of launching a clinical genetic test, as it demonstrates the quality and performance of the testing method and the accuracy of the assay result. Here, we evaluate the capability of this assay by assessing the variants that were detected in each sample. As expected, there were no pathogenic variants detected in the control sample (sample 1) for both runs. The pathogenic variants of samples 2–8 were confirmed by the manufacturer-recommended bioinformatic pipeline. These genotypes can be visualized using Integrative Genome Viewer (IGV), and they have also been confirmed using Sanger sequencing (Fig. 3); this yields a 100% sensitivity. Furthermore, samples 1–4 were sequenced in both runs, and sample 4 was sequenced three times in run 2. All results were concordant and matched to the referenced genotypes, hence the repeatability and reproducibility is 100%. Additionally, since there can be non-pathogenic variants in CFTR, we provide a table of all the variants detected in each VCF file for each sample in both run (Online-only Table 1). HGVS nomenclature and GnomAD frequencies for each variant are also listed. Of note, the VCF for sample 1 in run 2 contains a variant that is not present in run 1. This variant is a common two-nucleotide deletion of a TG-repeat stretch in intron 8. This dinucleotide repeat is adjacent to a poly-T stretch that also has common deletions and duplications. This discrepancy may be due to the fact that NGS alignment and annotation tools cannot reliably detect small insertions/deletions at repetitive regions. Sanger sequencing is still the preferred method to reliably detect variants at this repeat.
Fig. 3

Variant visualization using IGV and Mutation Surveyor. The variants for each corresponding sample are confirmed by visualizing the BAM files in Integrative Genomic Viewer (IGV). The Sanger sequence traces visualized using MutationSurveyor are also shown for each variant of each sample.

Online-only Table 1

Variants listed in each VCF file for each sample.

RunSampleChromPOSIDRefAltNomenclatureGnomad Freq
127117120145.GCc.-4G > C0.00007433
127117171029.GAc.350G > A0.001438
127117176568.AGATTAc.744-9_744-6del0.2648
127117176738.CTc.869 + 11C > T0.06933
127117188682.GTc.1210-13G > T0.0752
127117188684.TGc.1210-11T > G0.008495
127117199457.AGc.1393-61A > G0.267
127117199533.GAc.1408G > A0.4865
127117199644.ATCTAc.1521_1523del0.007172
127117229537.TAc.1680-870T > A0.5654
137117171169.GTc.489 + 1G > T0.00006857
137117174420.GTc.579 + 1G > T0.0000462
137117176568.AGATTAc.744-9_744-6del0.2648
137117176738.CTc.869 + 11C > T0.06933
137117188682.GTc.1210-13G > T0.0752
137117199457.AGc.1393-61A > G0.267
137117199533.GAc.1408G > A0.4865
137117229537.TAc.1680-870T > A0.5654
137117307286.GTGc.*133del0.4942
147117176568.AGATTAc.744-9_744-6del0.2648
147117176738.CTc.869 + 11C > T0.06933
147117188682.GTc.1210-13G > T0.0752
147117199457.AGc.1393-61A > G0.267
147117199533.GAc.1408G > A0.4865
147117199644.ATCTAc.1521_1523del0.007172
147117229537.TAc.1680-870T > A0.5654
147117246636.GAc.2909-92G > A0.1755
147117307108.GAc.4389G > A0.2206
147117307286.GTGc.*133del0.4942
147117308413.CTc.*1251C > T0.2566
217117188660.ATGAc.1210-13_1210-12del0.2292
227117120145.GCc.-4G > C0.00007433
227117171029.GAc.350G > A0.001438
227117176568.AGATTAc.744-9_744-6del0.2648
227117176738.CTc.869 + 11C > T0.06933
227117188682.GTc.1210-13G > T0.0752
227117188684.TGc.1210-11T > G0.008495
227117199457.AGc.1393-61A > G0.267
227117199533.GAc.1408G > A0.4865
227117199644.ATCTAc.1521_1523del0.007172
227117229537.TAc.1680-870T > A0.5654
237117171169.GTc.489 + 1G > T0.00006857
237117174420.GTc.579 + 1G > T0.0000462
237117176568.AGATTAc.744-9_744-6del0.2648
237117176738.CTc.869 + 11C > T0.06933
237117188682.GTc.1210-13G > T0.0752
237117199457.AGc.1393-61A > G0.267
237117199533.GAc.1408G > A0.4865
237117229537.TAc.1680-870T > A0.5654
237117307286.GTGc.*133del0.4942
24-17117176568.AGATTAc.744-9_744-6del0.2648
24-17117176738.CTc.869 + 11C > T0.06933
24-17117188682.GTc.1210-13G > T0.0752
24-17117199457.AGc.1393-61A > G0.267
24-17117199533.GAc.1408G > A0.4865
24-17117199644.ATCTAc.1521_1523del0.007172
24-17117229537.TAc.1680-870T > A0.5654
24-17117246636.GAc.2909-92G > A0.1755
24-17117307108.GAc.4389G > A0.2206
24-17117307286.GTGc.*133del0.4942
24-17117308413.CTc.*1251C > T0.2566
24-27117176568.AGATTAc.744-9_744-6del0.2648
24-27117176738.CTc.869 + 11C > T0.06933
24-27117188682.GTc.1210-13G > T0.0752
24-27117199457.AGc.1393-61A > G0.267
24-27117199533.GAc.1408G > A0.4865
24-27117199644.ATCTAc.1521_1523del0.007172
24-27117229537.TAc.1680-870T > A0.5654
24-27117246636.GAc.2909-92G > A0.1755
24-27117307108.GAc.4389G > A0.2206
24-27117307286.GTGc.*133del0.4942
24-27117308413.CTc.*1251C > T0.2566
24-37117176568.AGATTAc.744-9_744-6del0.2648
24-37117176738.CTc.869 + 11C > T0.06933
24-37117188682.GTc.1210-13G > T0.0752
24-37117199457.AGc.1393-61A > G0.267
24-37117199533.GAc.1408G > A0.4865
24-37117199644.ATCTAc.1521_1523del0.007172
24-37117229537.TAc.1680-870T > A0.5654
24-37117246636.GAc.2909-92G > A0.1755
24-37117307108.GAc.4389G > A0.2206
24-37117307286.GTGc.*133del0.4942
24-37117308413.CTc.*1251C > T0.2566
257117188660.ATGTGA,ATGc.1210-13_1210-12del0.2292
257117199533.GAc.1408G > A0.4865
257117199640.TATCTc.1519_1521del0.00004246
257117229536.AGc.1680-871A > G0.01043
257117229537.TAc.1680-870T > A0.5654
257117235055.TGc.2562T > G0.3901
257117246636.GAc.2909-92G > A0.1755
257117307108.GAc.4389G > A0.2206
257117307286.GTGc.*133delT0.4942
257117308413.CTc.*1251C > T0.2566
267117188660.ATGTGA,ATGc.1210-13_1210-12del0.2292
267117199533.GAc.1408G > A0.4865
267117229537.TAc.1680-870T > A0.5654
267117235055.TGc.2562T > G0.3901
267117242922.GAc.2657 + 5 G > A0.0000707
267117246636.GAc.2909-92 G > A0.1755
267117307108.GAc.4389G > A0.2206
267117307286.GTGc.*133delT0.4942
267117308413.CTc.*1251C > T0.2566
277117188660.ATGAc.1210-13_1210-12del0.2292
277117199533.GAc.1408G > A0.4865
277117229537.TAc.1680-870T > A0.5654
277117235055.TGc.2562T > G0.3901
277117246636.GAc.2909-92G > A0.1755
277117267591.CTc.3484C > T0.00005674
277117307108.GAc.4389G > A0.2206
277117307286.GTGc.*133del0.4942
277117308413.CTc.*1251C > T0.2566
287117176568.AGATTAc.744-9_744-6del0.2648
287117176738.CTc.869 + 11C > T0.06933
287117188682.GTc.869 + 11C > T0.06933
287117199457.AGc.1393-61A > G0.267
287117199533.GAc.1408G > A0.4865
287117199644.ATCTAc.1521_1523del0.007172
287117229537.TAc.1680-87 T > A0.5654
287117267633.ACAc.3528del0.0001559
Variant visualization using IGV and Mutation Surveyor. The variants for each corresponding sample are confirmed by visualizing the BAM files in Integrative Genomic Viewer (IGV). The Sanger sequence traces visualized using MutationSurveyor are also shown for each variant of each sample.

Supplementary information

Supplementary File 1
Measurement(s)amplicon sequencing
Technology Type(s)DNA sequencing
Factor Type(s)CFTR mutations
Sample Characteristic - OrganismHomo sapiens
  8 in total

1.  A New Targeted CFTR Mutation Panel Based on Next-Generation Sequencing Technology.

Authors:  Marco Lucarelli; Luigi Porcaro; Alice Biffignandi; Lucy Costantino; Valentina Giannone; Luisella Alberti; Sabina Maria Bruno; Carlo Corbetta; Erminio Torresani; Carla Colombo; Manuela Seia
Journal:  J Mol Diagn       Date:  2017-07-20       Impact factor: 5.568

2.  Serum growth hormone, LH and prolactin in the pregnant cow.

Authors:  W D Oxender; H D Hafs; L A Ederton
Journal:  J Anim Sci       Date:  1972-07       Impact factor: 3.159

3.  Targeted next-generation sequencing effectively analyzed the cystic fibrosis transmembrane conductance regulator gene in pancreatitis.

Authors:  Eriko Nakano; Atsushi Masamune; Tetsuya Niihori; Kiyoshi Kume; Shin Hamada; Yoko Aoki; Yoichi Matsubara; Tooru Shimosegawa
Journal:  Dig Dis Sci       Date:  2014-12-10       Impact factor: 3.199

4.  Clinical Sensitivity of Cystic Fibrosis Mutation Panels in a Diverse Population.

Authors:  Erin E Hughes; Colleen F Stevens; Carlos A Saavedra-Matiz; Norma P Tavakoli; Lea M Krein; April Parker; Zhen Zhang; Breanne Maloney; Beth Vogel; Joan DeCelie-Germana; Catherine Kier; Ran D Anbar; Maria N Berdella; Paul G Comber; Allen J Dozor; Danielle M Goetz; Louis Guida; Meyer Kattan; Andrew Ting; Karen Z Voter; Patrick van Roey; Michele Caggana; Denise M Kay
Journal:  Hum Mutat       Date:  2015-12-02       Impact factor: 4.878

5.  Genomic sequencing in cystic fibrosis newborn screening: what works best, two-tier predefined CFTR mutation panels or second-tier CFTR panel followed by third-tier sequencing?

Authors:  Robert J Currier; Stan Sciortino; Ruiling Liu; Tracey Bishop; Rasoul Alikhani Koupaei; Lisa Feuchtbaum
Journal:  Genet Med       Date:  2017-05-04       Impact factor: 8.822

6.  Detecting Common CFTR Mutations by Reverse Dot Blot Hybridization Method in Cystic Fibrosis First Report from Northern Iran.

Authors:  Mohammad-Reza Esmaeili Dooki; Haleh Akhavan-Niaki; Ali Ghabeli Juibary
Journal:  Iran J Pediatr       Date:  2011-03       Impact factor: 0.364

7.  Evidence for decline in the incidence of cystic fibrosis: a 35-year observational study in Brittany, France.

Authors:  Virginie Scotet; Ingrid Duguépéroux; Philippe Saliou; Gilles Rault; Michel Roussey; Marie-Pierre Audrézet; Claude Férec
Journal:  Orphanet J Rare Dis       Date:  2012-03-01       Impact factor: 4.123

8.  Correction: Sequencing as a first-line methodology for cystic fibrosis carrier screening.

Authors:  Kyle A Beauchamp; Katherine A Johansen Taber; Peter V Grauman; Lindsay Spurka; Jeraldine Lim-Harashima; Ashley Svenson; James D Goldberg; Dale Muzzey
Journal:  Genet Med       Date:  2019-10       Impact factor: 8.822

  8 in total

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