Amplicon-based next-generation sequencing (NGS) has been widely adopted for genetic variation detection in human and other organisms. Conventional data analysis paradigm includes primer trimming before read mapping. Here we introduce BAMClipper that removes primer sequences after mapping original sequencing reads by soft-clipping SAM/BAM alignments. Mutation detection accuracy was affected by the choice of primer handling approach based on real NGS datasets of 7 human peripheral blood or breast cancer tissue samples with known BRCA1/BRCA2 mutations and >130000 simulated NGS datasets with unique mutations. BAMClipper approach detected a BRCA1 deletion (c.1620_1636del) that was otherwise missed due to edge effect. Simulation showed high false-negative rate when primers were perfectly trimmed as in conventional practice. Among the other 6 samples, variant allele frequencies of 5 BRCA1/BRCA2 mutations (indel or single-nucleotide variants) were diluted by apparently wild-type primer sequences from an overlapping amplicon (17 to 82% under-estimation). BAMClipper was robust in both situations and all 7 mutations were detected. When compared with Cutadapt, BAMClipper was faster and maintained equally high primer removal effectiveness. BAMClipper is implemented in Perl and is available under an open source MIT license at https://github.com/tommyau/bamclipper.
Amplicon-based next-generation sequencing (NGS) has been one of the major platforms for high-throughput germline1, 2 and somatic mutation detection3,4,5,6,7. Various experimental strategies8, including traditional or multiplex polymerase chain reaction (PCR) and selective circularization probes, all converge to a common molecular design of NGS sequencing library (Fig. 1A).
The NGS reads usually contain both gene-specific primers (from the reagent) and region of interest (ROI, from DNA sample of interest) and have fixed start positions defined by primer sites. Common read mapping tools map primer sequences in the same way as ROI because of their equally high similarity to reference genome sequence. Common bioinformatics workflows may thus include a primer trimming step before mapping to remove primer sequence from NGS reads and avoid potential interference (approach 1, Fig. 1B), for example variant allele frequency (VAF) dilution by apparently wild-type primer sequences. However, variant calling edge effect occurred for single-nucleotide variants (SNV) such that SNV in ROI near primer sites were prone to escape variant calling9.
In some specific situations, primer trimming is not necessary (approach 2, Fig. 1B). Some genes of clinical importance have well-known hotspot mutational profile10 (e.g. gain-of-function mutations of EGFR and KRAS) and primers are designed to avoid any potential edge effect. In case of short exons or ROI, amplicon tiling is not needed so that ROI of one amplicon is not overlapped with and not interfered by primer of another amplicon. However, there are genes having diverse mutational profile10 (e.g. loss-of-function mutations of BRCA1 and BRCA2) or less characterized profile and full exons are usually covered by tiling amplicons. For gene panels having these 2 kinds of genes, both approaches 1 and 2 are needed to complement each other for comprehensive mutation detection. Mutation detection accuracy is maximized, but computing and manual interpretation efforts are doubled.
In this study, we devised primer clipping after mapping as an alternative approach of primer removal (approach 3, Fig. 1B). Existing tools of approach 3 include PcrClipReads (https://github.com/lindenb/jvarkit/wiki/PcrClipReads) that performs primer clipping on non-overlapping amplicons by design. GATK11 ClipReads performs primer clipping based on exact sequence matching regardless of its position within sequencing reads. Thus, GATK ClipReads will improperly handle overlapping amplicons due to sequence cross-matching and cannot recognize primers in case of any mismatch (sequencing errors for example). MiSeq Reporter Custom Amplicon Workflow (https://www.illumina.com/systems/miseq/software/miseq-reporter.html) properly clips primers from overlapping amplicons but the software is proprietary and supports specific sequencing platforms and commercial amplicon designs only. We implemented a tool called BAMClipper, which natively supports overlapping amplicons and any gene panel design. During evaluation of a breast and ovarian cancer gene panel for peripheral blood and breast cancer tumour tissue, BAMClipper approach detected BRCA1 and BRCA2 mutations that could otherwise be missed in conventional approaches due to edge or dilution effect. By experiments and simulation, insertion or deletions (indels) were shown to be susceptible to variant calling edge effect as in SNV. In addition to improving mutation detection accuracy, BAMClipper was more efficient in computation and maintained equally high primer removal effectiveness when compared with conventional primer trimming approach.
Variant calling edge effect when primers are trimmed
During evaluation of an amplicon-based breast and ovarian cancer gene panel with positive controls, we encountered a false-negative result for a BRCA1 17 nt deletion (c.1620_1636del). BWA-MEM alignments of the corresponding ROI were found to be soft-clipped when gene-specific primers were trimmed from NGS sequencing reads before read mapping (approach 1, Fig. 2). The soft-clipping removed wild-type sequence adjacent to the deletion site from read alignments of deletion allele. Alignment pileup therefore did not contain any deletion events for conventional variant callers to report. When primer trimming was skipped (approach 2) or primer clipping was performed after mapping (approach 3), the deletion was properly aligned and detected by variant callers (Fig. 2). These observations remained the same when the alignment algorithm was replaced with BowTie 2 (see Supplementary Fig. S1). Since SNV were known to be susceptible to variant calling edge effects in amplicon sequencing9, we hypothesized that indels were also susceptible to similar edge effects.
We simulated NGS reads representing 420 unique insertions and 420 unique deletions of different lengths (1 to 20 nt) at 21 different positions of the aforementioned BRCA1 amplicon (Fig. 3A). All three approaches of primer handling were applied to the simulated reads for variant calling. In approaches 1, 2 and 3, 40%, 100% and 93% of insertions were detected and 30%, 100%, 98% of deletions were detected, respectively (Fig. 3B). If primers were trimmed from sequencing reads before mapping, indels were susceptible to variant calling edge effects. Longer indels and indels closer to gene-specific primer boundary were more susceptible to the edge effects (Fig. 3C). Regarding the 29 insertions (7%) and 7 deletions (2%) missed in approach 3, although BAMClipper clipped the primer sequences properly and the corresponding indels remained in both alignments and alignment pileup, they were not called by variant caller SAMtools.
To explore whether the edge effects also applied to overlapping amplicons, we simulated NGS reads for 68045 unique insertions and 64637 unique deletions similarly (length 1 to 20 nt) at 3446 different positions for the entire BRCA1 exon 11. A total of 38 overlapping amplicons covered the region. In approaches 1, 2 and 3, 88.0%, 96.7% and 96.5% of insertions were detected and 76.9%, 96.7%, 93.2% of deletions were detected, respectively (see Supplementary Fig. S2). Similar edge effect was observed even in overlapping amplicon setting.
VAF dilution effect when primers are not removed
Since higher indel detection rate was demonstrated in approaches 2 and 3, we evaluated these approaches with 6 additional BRCA1/BRCA2 positive controls (Table 1). Although all 6 mutations were detected in both approaches 2 and 3, the variant allele frequencies (VAF) of approach 2 (range: 9–60%) were consistently lower than approach 3 (range: 46–74%). Five of the 6 mutations were found to overlap with a gene-specific primer of another overlapping amplicon owing to the tiling amplicon design (Fig. 4). Primer sequences remained in approach 2 contributed to wild-type allele frequency and in turn underestimated VAF within region of interest of other overlapping amplicons. VAF underestimation ranged from 17% to 82% (Fig. 4) and VAF was not affected by BAMClipper if the mutation did not overlap with other primer site (Table 1). Since approach 3 properly handled primer sequences in NGS reads, downstream VAF determination was not interfered by overlapping primer sites. Overall, although approach 2 was robust to detect indels near primer, it was susceptible to VAF dilution effect. Approach 3 was versatile to detect indels near primer and mutations overlapping with other primer sequences.
Effective primer removal and efficient computing
Performance characteristics of BAMClipper were further evaluated by various scenarios of primer clipping. Cutadapt (a representative tool of approach 1) was also applied to the same scenarios for benchmarking. BAMClipper removed 99.96% of gene-specific primers without affecting the regions of interest (i.e. without over-clipping), achieving comparable primer removal efficiency as Cutadapt (99.82% of gene-specific primers removed) (see Supplementary Fig. S3). For genes with well-known hotspot mutation profile, mutation detection sensitivity of approach 3 was as good as approaches 1 and 2 (see Supplementary Table S3), with one notable difference: the 52 nt hotspot deletion in CALR was detected in approaches 2 and 3 but not approach 1 due to similar edge effect. BAMClipper was consistently faster than Cutadapt, particularly for larger amplicon panel, longer sequencing reads and more sequencing reads (see Supplementary Fig. S3).
Amplicon-based next-generation sequencing has been widely adopted for genomic DNA mutation detection in both clinical diagnostics and research settings. Common bioinformatics workflows utilize tools originally designed for random fragment sequencing library (e.g. whole-genome sequencing, exome sequencing by hybridization capture). Although the tools are largely compatible with amplicon library, bioinformatics challenges exist in some scenarios, such as the detection of FLT3 internal tandem duplication in acute myeloid leukaemia by amplicon sequencing3. In this study, we presented the bioinformatics challenges encountered during evaluation of a breast and ovarian cancer gene panel, namely variant calling edge effect and variant allele frequency dilution effect when handling gene-specific primers in amplicon sequencing library.
Edge effect led to a false-negative result of a BRCA1 17 nt deletion if primer trimming was performed according to common bioinformatics workflows. We further showed that such edge effect was not specific to this particular mutation but generally applicable to both single amplicon setting (65.1% false-negative among 840 unique indels) and overlapping amplicon setting (17.4% false-negative among 132682 unique indels). Indels were thus shown to be susceptible to similar edge effect reported for single-nucleotide variants9.
When primer trimming was skipped for a panel with amplicon tiling, evaluation of 6 additional BRCA1/BRCA2 mutations revealed that 5 of the mutations were susceptible to VAF dilution effect (under-estimation by 17% to 82%). The dilution effect applied to both SNV and indels. Since the evaluated breast and ovarian cancer panel used amplicon tiling to cover protein-coding regions, 31% of ROI are overlapping with primer of other amplicon(s) and susceptible to the dilution effect. Whether a mutation affected by dilution effect would be missed by variant calling might depend on the variant calling algorithms (e.g. any VAF threshold) and sequencing depth uniformity among overlapping amplicons. Even though the 6 tumour DNA samples were derived from patients with a germline mutation (VAF >30% expected), VAF of a mutation dropped to below 10%. The situation represented possible false-negative scenarios of two common primer handling approaches in germline and somatic mutation detection due to the two effects.
Suitability of primer trimming depends on the genes of interest. For genes with well-known hotspot mutational profile (e.g. gain-of-function mutations of EGFR and KRAS) and their hotspot regions well covered by primers without amplicon tiling, primer trimming is not mandatory. However, for genes with diverse mutational profile (e.g. loss-of-function mutations of BRCA1 and BRCA2) that necessitated their whole coding regions to be covered by amplicon tiling, primer trimming avoided dilution effect at the expense of sensitivity of mutations near primers due to edge effect. Taking the evaluated breast and ovarian cancer panel as an example, 40 nt of region of interest (20 nt of each end) for each amplicon was susceptible to edge effects if indels up to 20 nt are targeted. Since the mean ROI length is 110 nt for this panel to accommodate somatic mutation detection from degraded DNA (e.g. formalin-fixed paraffin-embedded tissue), 36% of ROI are susceptible to edge effect. When a single panel comprise both kinds of genes or genes with less characterized mutational profile, both primer handling approaches were needed to complement each other. We demonstrated that primer clipping was a general approach for mutation detection in both genes with diverse mutational profile and genes with well-known hotspot mutational profile (Fig. 2 and Supplementary Table S1). Although some commercial gene panel products implemented proprietary primer clipping solution to handle both kinds of genes properly (e.g. MiSeq Reporter results of TruSight Myeloid panel did not need redundant primer clipping by BAMClipper), there was a lack of general bioinformatics tool for primer clipping of most other gene panel designs.
We implemented BAMClipper as a novel approach of primer handling. By clipping primers after mapping, mutation detection accuracy was improved by minimizing false-negative results caused by edge effect and dilution effect. Well-established variant calling and quality control tools (e.g. SAMtools and bam-readcount) were tested to be compatible with this approach. Although the junction of primer and ROI was shown to be a variant calling blind spot by simulation of a single amplicon (even though the primers were properly clipped), the junction was covered by another amplicon in the evaluated panel that ROI overlapped by at least 1 nt. Since amplicon tiling was a common practice in commercial or custom panel design, few existing panels were actually affected.
The choice of primer handling approaches was shown to affect germline and somatic mutation detection accuracy. False-negative BRCA1/BRCA2 mutation could affect the management of a high-risk cancer patient and his/her family members and could result in a missed opportunity of targeted therapy (e.g. poly(ADP-ribose) polymerases inhibitors in breast and ovarian cancers12).
Since BAMClipper was implemented in Perl and did not involve new file format, it could be deployed in existing bioinformatics workflows with minimal efforts. It was computation-efficient because the primers were already aligned by mapping tools and BAMClipper manipulated the alignments. Computing time saved by mapping shorter trimmed reads in conventional approach was usually exceeded by the trimming process. Even though advances in trimming algorithms could result in perfect trimming with minimal computing time, edge effect could persist as shown in the simulation. BAMClipper maintained the same high level of primer removal effectiveness as conventional trimming approach. We expected the new primer handling approach could improve amplicon-based germline and somatic mutation detection workflows.
To conclude, primers should be removed after mapping original sequencing reads for optimized genetic variation detection by amplicon sequencing. BAMClipper is an open-source, fast and effective tool to remove primers from mapped alignments of amplicon sequencing reads.
Implementation of BAMClipper algorithm
The BAMClipper algorithm was implemented mainly in Perl to read mapped alignments (standard SAM/BAM format13) and corresponding primer design (standard BEDPE format14) and return modified alignments in the same format. Gene-specific primer sequences were identified by comparing mapped positions of input alignments and reference positions of primer design (no de novo sequence alignment process was involved and thus the algorithm tolerated sequencing errors in observed primer sequences). Input alignments (a series of Concise Idiosyncratic Gapped Alignment Report or CIGAR operators in the alignment field of SAM/BAM format) were modified so that primer sequences were represented as S operators instead of M, I or D operators, as if they were sequencing adapters not matching to the reference genome and hence became soft-clipped during mapping. Output primer-clipped alignments remained in the standard BAM format that no modification of downstream variant calling (e.g. SAMtools13 and GATK11) or quality control (e.g. coverage-depth analysis by bam-readcount) was needed for analysis.
BAMClipper was tested to be sequencing platform-independent by clipping primers from amplicon sequencing reads from both Illumina MiSeq and Ion Torrent PGM platforms. It was shown to be aligner-independent by processing BWA-MEM and BowTie 2 alignments of same sequencing data. It was tested on CentOS Linux 5.5 and depended on the built-in Perl 5 installation of any Linux/Unix-like environment without compilation. A multi-threaded pipeline was also implemented (a single shell script bamclipper.sh) to leverage multiple processor cores and required SAMtools13 and GNU Parallel (https://www.gnu.org/software/parallel/).
Germline and somatic mutation detection by a breast and ovarian cancer panel
Totally 7 positive control DNA samples were employed to evaluate the Qiagen GeneRead DNAseq v2 Human Breast Cancer Panel, which comprised 2915 amplicons targeting 44 genes. DNA was extracted from 1 peripheral blood sample and 6 frozen tumour tissue samples of 7 breast cancer patients with a known germline BRCA1/BRCA2 mutation recruited from Hong Kong Hereditary and High Risk Breast Cancer Programme and samples obtained from the tissue bank of Hong Kong Hereditary Breast Cancer Family Registry1. PCR amplicons were prepared according to manufacturer’s instructions followed by sequencing library preparation based on KAPA Library Preparation Kit for Illumina platforms version 1.13. Paired-end sequencing runs were performed on a MiSeq with reagent kit v3 (2 × 230 nucleotides (nt)). Primer sequences were trimmed from sequencing reads by Cutadapt15 version 1.4.1 (parameters -m 0 -e 0.1 -n 2). Original sequencing reads or primer-trimmed reads were mapped to reference genome sequence using BWA-MEM16 version 0.7.7 (default parameters) or BowTie 217 version 2.2.9 (default parameters). Variant calling and variant allele frequency (VAF) measurement was performed by SAMtools13 version 1.3 and assisted by manual alignment examination using IGV18 version 2.1.30.
Simulation of sequencing reads for insertions and deletions
In silico simulation targeted the single BRCA1 amplicon from the breast and ovarian cancer panel covering the BRCA1 mutation c.1620_1636del (amplicon: chr17:41245872-41246030; ROI excluding both gene-specific primers chr17:41245895-41246009). A single pair of sequencing reads was simulated for each unique insertion or deletion with 0% error rate and 100% VAF. Insertion sequence was based on a template without known similarity to reference genome (5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′) and 3′ trimmed to desirable length. Simulated reads of 840 unique indels were mapped to reference genome by BWA-MEM version 0.7.7 (default parameters) followed by variant calling using SAMtools version 1.3.
Another similar simulation was performed for the entire BRCA1 exon 11 (chr17:41243442-41246887) in the same breast and ovariant cancer panel. Sequencing reads of all 38 overlapping amplicons were simulated for each unique insertion or deletion with 0% error rate and 100% VAF. Indels were excluded for simulation if they overlapped with primer binding site and sequencing reads could not represent the mutations.
Performance of BAMClipper and Cutadapt was characterized using amplicon NGS datasets of a myeloid neoplasm gene panel with 568 amplicons (Illumina TruSight Myeloid sequencing panel). Data based on peripheral blood sample of a de-identified healthy adult with normal complete blood profile3 was used to assess computing time with variable amplicon count, sequencing read length and sequencing read count, while data of the HapMap sample NA12878 (sample 2 of the public datasets “MiSeq v3: TruSight Myeloid (Coriell & HorizonDx, Pool 1)” available from https://basespace.illumina.com) was used to assess primer removal effectiveness. Data based on 8 mutation control samples from a previous study3 were used to compare the mutation detection sensitivity of all 3 primer handling approaches. Identical Cutadapt parameters as in the previous Methods were employed to work on FASTQ sequencing reads. Default BAMClipper parameters were used to work on corresponding BWA-MEM alignments (default parameters with BWA-MEM computing time ignored). Wall-clock computing time of BAMClipper and Cutadapt was measured based on 16 CPU cores (Intel Xeon E5 processors) (see Supplementary Note).
For primer removal effectiveness, BWA-MEM alignment depth of original sequencing reads (2 × 151 nt) of ROI or gene-specific primers was measured by bam-readcount (https://github.com/genome/bam-readcount) and served as reference. Reference positions of depth <500X were excluded from the following analysis. Alignment depth of BAMClipper and Cutadapt final BAM files was also measured similarly to calculate median decrease of depth after primer removal for each amplicon. Data from each amplicon were handled separately so that depth measurement was not interfered by any overlapping amplicons.
For amplicon count, one million (1 M) read pairs (2 × 275 nt) were first randomly sampled. Full panel (568 amplicons, 5 replicates) or random sample of 50 to 550 amplicons (5 random sampling without replacement for each amplicon count) was passed to BAMClipper and Cutadapt for primer removal of the same 1 M read pairs. For sequencing read length, the same 1 M read pairs were further trimmed to pairs of 100, 125, 150, 175, 200, 225 or 250 nt sequencing reads using Seqtk (https://github.com/lh3/seqtk) (trimfq -q 1). For sequencing read count, 1000 to 1000000 sequencing read pairs (2 × 275 nt; 5 random sampling without replacement for each read count) were randomly sampled.
Human reference genome sequence: GRCh37/hg19, BRCA1: NM_007294.3, BRCA2: NM_000059.3. Variants were described according to the recommendations of Human Genome Variation Society (HGVS)19. Variant descriptions were checked by Mutalyzer Name Checker19.
The study involving the breast and ovarian cancer panel was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority West Cluster and other contributing hospitals in Hong Kong. The study involving the myeloid neoplasm panel was approved by the Research Ethics Committee of Hong Kong Sanatorium & Hospital (reference number: REC-2015-02). All participants gave written informed consent; with the exception that informed consent is not needed for the use of a pre-existing peripheral blood sample from a de-identified healthy adult and 8 de-identified mutation control samples. All methods were performed in accordance with the relevant guidelines and regulations of the institution.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by the Hong Kong Hereditary Breast Cancer Family Registry and Dr. Ellen Li Charitable Foundation.
Electronic supplementary material
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.