Research Article
Considerations for clinical read alignment and mutational profiling using next-generation sequencing [v1; ref status: indexed, http://f1000r.es/MsY1QZ]
Gavin R Oliver
Author affiliations
Grant information: The author(s) declared that no grants were involved in supporting this work.
3783

## Abstract

Next-generation sequencing technologies are increasingly being applied in clinical settings, however the data are characterized by a range of platform-specific artifacts making downstream analysis problematic and error prone. One major application of NGS is in the profiling of clinically relevant mutations whereby sequences are aligned to a reference genome and potential mutations assessed and scored. Accurate sequence alignment is pivotal in reliable assessment of potential mutations however selection of appropriate alignment tools is a non-trivial task complicated by the availability of multiple solutions each with its own performance characteristics. Using BRCA1 as an example, we have simulated and mutated a test dataset based on Illumina sequencing technology. Our findings reveal key differences in the performances of a range of common commercial and open source tools and will be of importance to anyone using NGS to profile mutations in clinical or basic research.

## 1 Introduction

Since emergence in 2005, next-generation sequencing (NGS) technologies have proven prolific tools in the research setting, permeating a variety of scientific disciplines and demonstrating a range of applications that seems to be limited only by the imagination of the sequencing community. The technology continues to develop at a rapid pace with established instrument manufacturers regularly augmenting their product portfolios and an increasing number of start-up companies promising to disrupt the market. Beyond basic research applications, NGS technologies are now increasingly being applied in the clinical environment, driven partly by their rapid maturation and the arrival to market of smaller, cheaper sequencing platforms.

The potential clinical application of NGS has a broad scope ranging from full human genome profiling1 to investigation of the microbiome2 and includes applications such as biomarker discovery, patient diagnosis, prediction of drug response and patient stratification for clinical trials. Such applications often involve the targeted profiling of genes known to be of clinical relevance. These genes harbor diagnostically relevant variants including single nucleotide polymorphisms (SNPs), and small insertions and deletions (INDELs). Individual genes have previously been interrogated in clinical testing using traditional techniques such as Sanger sequencing however NGS technologies have already begun to supplant the previous tools of choice in these areas, offering increased speed and throughput with reduced running costs.

Despite many successes and increasing uptake, the data generated by NGS analyzers is not perfect, with each platform yielding characteristic errors and biases. Furthermore, NGS technologies produce reads that are much shorter than those traditionally produced by Sanger sequencing methods and this can complicate matters further, especially in genomes containing a large proportion of repetitive elements3. The effect of these problems is most visible in large scale studies such as genome-wide sequencing where a recent study reported a 1 million variant platform-based discrepancy for a single genome4. This fact bestows responsibility on both algorithm and software developers and downstream users to develop deep understanding of the various data types and their idiosyncrasies and to apply this appreciation in their analysis and interpretation, in order to correct or compensate for potential errors. Despite forming an area of active research, data interpretation remains an issue and is no doubt a factor in feeding the inertia of many clinical facilities that are reluctant to adopt the new technologies5.

Two major computational steps in variant detection from NGS data are read alignment whereby the data are mapped to corresponding locations on a target genome, and mutation calling whereby nucleotides differing from the target genome are assessed and scored on their likelihood of representing a genuine mutation versus an error. While these two stages of analysis may be supplemented with various pre or post processing techniques they represent the most crucial steps and therefore the area of most active software and algorithm development.

Aligners of choice have begun to emerge6,7 however their strengths are often application specific and different tools are recommended depending on the sequencing platform and individual study goals. Aligners generally fall into the categories of being based on either hash tables or suffix trees8. Suffix tree-based aligners such as Heng Li’s BWA9 are characterized by their speed and memory-efficiency whilst generally achieving lower sensitivity than hash-based counterparts such as Stampy10. There is thus a necessary trade-off between speed and sensitivity at the read alignment stage with speed often being prioritized due to the volumes of data produced by NGS technologies and the corresponding time required for analysis. Aligners can be further classified as gapped or ungapped based on their ability to produce successful alignments in the presence of small INDELs. Aligners including BWA and Stampy have been shown to produce alignments with a fair degree of success for reads containing a range of INDEL sizes10 however such abilities will vary based on a range of complicating factors including size and location of the INDEL. As well as generating continuous controversy11, the BRCA1 gene presents a particularly interesting set of alignment challenges due to a disproportionately high concentration of INDELs greater than eight nucleotides in length. In fact, 3% of known deleterious mutations in the Breast Cancer Information Core (BIC) database12 fall into this category and represent a significant over-representation when compared to a healthy genome. Furthermore the BRCA1 gene contains multiple areas of high shared identity in the form of tandem repeats, posing another difficulty in achieving accurate read mapping. Challenges like this pose particular difficulties in the clinical setting where errors have the potential to translate to misdiagnosis or mistreatment, directly affecting and endangering the lives of patients. No gold-standard clinical alignment tools yet exist and numerous publicized examples of early translational work appear to base their choice of tool on user-friendliness or availability of a graphical-user-interface rather than assessments of performance. We have investigated the performance of a range of popular alignment tools and assessed their ability to accurately detect known mutations. Several of these tools are already being used as components of diagnostic workflows in the clinical setting. Here we present data generated using simulated reads derived from the human BRCA1 gene. Our findings demonstrate the widely varying abilities of common read alignment tools and their impact on downstream variant calling. Furthermore the results suggest a need for careful and thorough evaluation of all tools being used in a particular analysis pipeline through simulation and analysis of data of known constitution.

## 2 Methods

### 2.1 Aligners

A range of open source and commercial alignment tools were selected for assessment based on their reported ability to facilitate detection of both SNPs and INDELs as well as frequency of citation in both scientific and commercial literature. The aligners included in the comparison were: BWA (0.5.9-r16), Bfast (0.7.0a)13, Smalt (0.5.8), Stampy (1.0.14), Mosaik (2.1.33), CLC Genomics Workbench’s (5.0.1) NGS and Beta aligner, Novocraft’s Novoalign (V2.07.18)14, Omixon’s Variant Toolkit (2.1.3), Bowtie 2 (2.0.0-beta5)15 and Softgenetics Nextgene (2.2) aligner.

Stampy was used to simulate sixty-seven groups of 200,000 90bp paired-end FASTQ Illumina reads from the human BRCA1 gene (hg19) with an appropriate error profile. Each sequence grouping was mutated in-silico with custom scripts used to introduce a combination of 20 SNPs and 13 INDELs. These were selected from a test set of 2211 (1299 unique) known BRCA1 variants containing 1340 SNPs, 320 insertions and 551 deletions.

Reads were aligned to hg19 chromosome 17 on a HP DL585 G6 server with 4 six-core AMD Opteron 2.8Ghz processors and 256GB of RAM. Multi-threading with the maximum number of threads supported by the aligner was utilized. Each aligner was run with parameters as close as possible to default. Each aligner was run in both single-end and paired-end alignment mode with half of the paired-end reads being used to simulate a single-end read dataset. Run-times were benchmarked based on the wall-clock time taken to produce a SAM format alignment for all 67 sets of FASTQ reads.

### 2.4 Calling of SNPs and INDELs

Each SAM formatted file was converted to BAM format and processed to ensure downstream compatibility with GATK16 using a combination of tools from the Picard collection. (SamFormatConverter, AddOrReplaceReadGroups, SortSam and BuildBamIndex respectively). BAM files were then processed in a GATK-based pipeline. The pipeline consisted of local realignment around INDELs (RealignerTarget Creator and IndelRealigner), quality score recalibration (CountCovariates and TableRecalibrator) and finally variant calling (Unified-Genotyper). The wrapper scripts sam2bam.sh and gatk.sh are provided and can be used to recreate the processing steps from alignment files (SAM format) to variant call (VCF format).

## 3 Results/Discussion

### 3.1 Mutation panel

The reads and mutation panel utilized here represent a challenging and multi-functional test set with widely varying INDEL sizes (Table 1) and an extensive range of SNPs providing a useful means of assessing the aligners’ single and paired-end modes. The reads were created to contain only known mutations from the human BRCA1 gene thus facilitating downstream assessment of mutation profiling accuracy whilst remaining comparable to real-world data. Reads were simulated to closely match the error profile of Illumina’s sequencing technology enabling a further level of realism to be captured in the simulated test-set. Only homozygous variants at relatively high levels of sequence coverage (70–140x) were included in the test set to ensure testing of the alignment tools’ abilities rather than the quality of the downstream variant calling methods.

### Table 1. Range of INDEL sizes (in base pairs) in the BRCA1 mutation panel.

A representative range of SNPs and small INDELs corresponding to known mutations were distributed between 67 groups of simulated Illumina reads in order to test the read aligners’ abilities to accurately align mutation-containing reads.

Mutation size # Insertions # Deletions
1215261
252121
3840
41160
5527
6116
747
8310
902
1109
1210
1301
1430
1511
1610
1702
1810
1901
2440
2901
6201
6401

### 3.2 Run-times

Run-times varied widely from seconds to hours and are shown in Table 2. Novocraft’s Novoalign software performed fastest and was closely followed by BWA and Bowtie 2 in both single and paired-end mode whilst Bfast’s paired-end mode represented the slowest run-time by almost half a day. Nextgene was excluded from this comparison due to the fact it is Windows-based software and it was not possible to assess run-times on the same hardware as for the other aligners. The criticality of program speed is largely dependent on end-application. For example, targeted sequencing experiments will generally involve much smaller data volumes than whole human genome sequencing. It should also be borne in mind that read generation by the sequencing machine itself is a rate limiting step and sequence generation and alignment steps can be run in parallel. With the exception of the paired-end run with Bfast, most alignment times recorded here might be considered manageable for most purposes.

### Table 2. Aligner run times in wall-clock hours.

Each aligner was tested by aligning all 67 groups of simulated Illumina reads in single and paired-end mode. The maximum number of threads utilizable by each aligner were used in testing.

Utilizable (of 24)
PE Time to
align
SE Time to
align
BWA240.280.08
Bfast2419.797.01
Smalt86.448.25
Stampy16.283.50
Mosaik242.641.56
CLC241.410.88
CLCBeta240.670.60
Novoalign240.110.04
Omixon244.831.26
Bowtie2240.300.11
Nextgene24NANA

### 3.3 Sensitivity of detection

The greatest overall sensitivities of detection were achieved by Novoalign and Omixon’s Variant Toolkit in paired-end and single-end mode respectively (Figure 1, Table 3). Stampy also enabled highly sensitive mutation detection with Bfast performing least favorably. Sensitivities were also assessed based on the category of mutation (Table 4).

### Figure 1. Overall sensitivity of detection.

Graphical representation of the total percentage of mutations detected across of 67 groups of simulated Illumina reads in single-end and paired-end mode.

### Table 3. Overall detection sensitivities.

The total percentage of mutations detected by each aligner across the 67 groups of simulated Illumina reads was recorded for single and paired-end run-modes. Each read group contained 13 INDELs of varying sizes as well as 20 SNPs.

Aligner Sensitivity PE Sensitivity SE
BWA99.2394.53
Bfast90.6887.83
Smalt98.5598.51
Stampy99.4699.05
Mosaik97.0695.07
CLC98.0197.92
CLCBeta96.8896.74
Novoalign99.5999.00
Omixon99.4199.14
Bowtie298.9198.19
Nextgene98.6498.69

### Table 4. Detection sensitivities by mutation type.

Total percentage of SNPs and INDELs detected across the 67 groups of simulated Illumina reads by each aligner in single and paired-end mode.

Aligner % SNPs
found PE
% SNPs
found SE
% Insertions
found PE
% Insertions
found SE
% Deletions
found PE
% Deletions
found SE
BWA99.4899.4899.3889.3898.5585.48
Bfast92.6989.8583.1378.7590.2088.20
Smalt99.4099.4096.5696.2597.6497.64
Stampy99.4899.2599.3898.1399.4699.09
Mosaik97.0196.3496.2590.0097.6494.92
CLC99.4099.4096.5696.2595.4695.28
CLCBeta97.6197.6195.3195.0096.0195.64
Novoalign99.7099.48100.0098.1399.0998.37
Omixon99.4098.9699.3899.3899.4699.46
Bowtie299.5099.5097.5096.6098.4096.00
Nextgene99.0099.0096.6096.6099.1099.10

Some aligners such as Smalt, Bowtie 2 and CLC were clearly seen to perform more strongly on detection of SNPs than INDELs in general. Nextgene performed similarly for SNPs and deletions but had lower sensitivity of insertion detection. BWA showed obvious decreases in ability to detect INDELs when shifting from paired-end to single-end mode. In contrast, Novoalign, Omixon and Stampy performed well regardless of run-mode or mutation type. Overall Novoalign was the best performer in paired-end mode while Omixon achieved the highest sensitivities in single-end mode. In a clinical environment, sensitivity will likely represent the most important metric in evaluating alignment software, however other factors may also be of importance, depending on the test in question. Specificity values are not included here as they were non-discriminatory in this context due to low numbers of false positives relative to the high number of true negatives.

### 3.4 Incorrect identification of mutations

Controlling the rate of false positive results is clinically important in avoiding unnecessary treatment, expense and patient anxiety. The number of incorrectly identified mutations varied widely dependent on aligner and run-mode (Figure 2, Table 5). The highest rates were obtained using Bfast, followed by CLC Bio’s Beta aligner, Nextgene, Mosaik, Stampy, and Bowtie 2 while Novoalign and Smalt were the strongest performers followed closely by Omixon and CLC. Number of false positives alone is of limited utility in assessing aligner performance, however positive predictive value (PPV) provides a useful metric which combines counts of both true and false positives in a single value (Table 6). PPV was calculated based on the equation:

$PPV=True PositivesTrue Positives+False Positives$

### Figure 2. Graphical representation of the total number of false positives expressed as a percentage of the true positive mutations detected by each aligner in single and paired-end mode across 67 groups of simulated Illumina reads.

Each read group contained 20 SNPs and 13 INDELs.

### Table 5. Raw numbers of false positive and negative mutations detected across 67 groups of simulated Illumina reads by all aligners in single and paired-end mode.

Each read group contained 20 SNPs and 13 INDELs.

Aligner # False
positives
PE
# False
positives
SE
# False
negatives
PE
# False
negatives
SE
BWA2316817121
Bfast884759206269
Smalt10143233
Stampy263951221
Mosaik3147665109
GLG21234446
GLGBeta1195666972
Novoalign610922
Omixon39211319
Bowtie2249662440
Nextgene3951173029

### Table 6. Positive predictive values (PPVs) calculated for each aligner in single and paired-end mode based upon detection of mutations across 67 groups of simulated Illumina reads each containing 20 SNPs and 13 INDELs.

In this case PPV is the quotient of the total number of true positives divided by the sum of the total number of true positives and true negatives.

Aligner PPV SE PPV SE
BWA98.9692.56
Bfast69.4071.90
Smalt99.5499.36
Stampy89.3295.84
Mosaik87.2496.51
CLC99.0498.95
CLCBeta94.7479.08
Novoalign99.7399.55
Omixon98.2699.05
Bowtie289.7897.05
Nextgene84.6794.91

No inferences were made about prevalence in calculating the values. Novoalign had the highest PPV in both single and paired-end mode. Smalt, CLC and Omixon’s Variant Toolkit also performed strongly on this metric. Notably Stampy performed relatively poorly in contrast to its high sensitivity.

### 3.5 Paired-end vs. single-end reads

Notably Bfast, Nextgene, Stampy, Mosaik and Bowtie 2 all showed obvious increases in the number of false positives detected in paired-end mode. Conversely, switching from paired-end to single-end reads had varying adverse effects on the sensitivities of all aligners except for Nextgene. The worst affected was BWA which retained all SNP calls but demonstrated a clear dependency on the information offered by paired-ends by losing 13% of INDEL calls. Notably Nextgene and Omixon’s Variant Toolkit were the only software to retain all INDEL calls when switching from paired to single-end mode. Omixon’s Variant toolkit was the most sensitive of all the aligners in single end mode with a 99.14% overall detection sensitivity.

This strong performance in single-end mode is relevant not only from a diagnostic standpoint, but also from a clinical cost-saving perspective as paired-end protocols ultimately incur extra costs per run vs single-end protocols. While paired-end reads generally represent a saving in terms of cost per megabase, they effectively double sequencing output and this may not be a cost-effective option depending on the logistics of the individual run. Furthermore, researchers who outsource sequencing will often see the available protocol for their relatively small sequencing project dictated by the larger projects they are multiplexed alongside.

### 3.6 Effect of INDEL size on detection

Not unexpectedly there appeared to be a general trend of INDEL size affecting most aligners ability to facilitate downstream calling (Figure 3). The size of the effect varied by aligner with BWA and Novoalign showing good detection rates for all but the largest deletions while others such as Bowtie 2 and the CLC aligners were not successful far beyond a 10bp INDEL size. Nextgene showed better sensitivity for insertions than deletions. Stampy and Omixon’s Variant Toolkit were the only two aligners to detect the largest deletions. This highlights a need for those involved with testing and analysis to develop an appreciation of the various mutations that might exist in their target genes and to select their analysis tools appropriately. INDELs in the range represented by the BRCA1 mutation panel have real-world relevance in genetic disorders and strong aligner performance on larger INDELs appears to be an exception rather than a rule.

## 4 Summary and conclusions

Using a simulated, targeted sequencing scenario with Illumina read data, the work presented here highlights several important considerations regarding aligner choice in studies involving profiling of mutations. Furthermore the data presented goes some way to characterizing the performance of a comprehensive selection of commonly used aligners and should represent a useful resource for anyone focused on similar scientific studies.

Whilst the dataset used in this study was engineered to include a challenging range of mutations and efforts were made to simulate the error profile of Illumina sequencing technology, it is nevertheless a simplified representation of real-world data. Experimental artifacts such as PCR stutter have the potential to present further challenges to alignment algorithms and there is no consideration of such issues here. Furthermore, the test dataset used in this study produced a uniform, high coverage of the target gene and only homozygous variants were simulated. Finally, the use of only a single variant-caller in the study means that some of the errors encountered may not be due to alignment issues. The aim is to follow up the current study by focusing on an expanded gene-set, alternative variant-callers, homo and heterozygous mutations and different sequence formats. Nonetheless, this focused study demonstrates the utility of simulated data in assessing program performance.

With the exception of Bfast, all aligners performed relatively well on the BRCA1 dataset. Clinical applications necessitate the use of the most highly accurate solutions, however. Only Novoalign, Omixon’s Variant Toolkit and Stampy achieved 99% or greater sensitivity in both paired-end and single-end mode. Omixon and Stampy were the only two aligners to detect the longest deletions in the dataset however Stampy’s performance was let down by a false positive rate which would be considered unacceptably high for most applications. While Novoalign did not detect the largest deletions, it was the fastest aligner and the most sensitive in paired-end mode. Nevertheless, assuming the longer run-times are not an issue, Omixon’s superior sensitivity in single-end read mode likely makes it the best option when paired-end protocols are not possible. It should also be mentioned that as a freely available, open-source tool, BWA’s paired-end performance is laudable and goes some way to justifying its widespread use and popularity.

While the tests here produce some clear winners, they also serve to highlight that program performance can vary widely based on the fine-details of a particular run. Even the strongest overall performer can be found lacking in some respects and this means that researchers should be vigilant in their selection of tools for a particular application. In certain instances it may even be necessary to combine two or more approaches to ensure that all relevant aspects of a given dataset are sufficiently characterized and any approach will still require some level of visual inspection and quality control in a clinical setting. The data presented here should facilitate and expedite selection of the correct aligner for a particular task but they do not obviate the requirement for careful consideration nor further testing and analysis on the part of the end-user.

# Current Referee Status:

## Referee Responses for Version 1

Mihaela Pertea, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Steven Salzberg, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Not Approved: 27 July 2012 Ref Report: 27 Jul 2012 In this paper the author sets out to investigate the performance of several alignment tools and to assess their ability to accurately detect known mutations when used in a variant calling pipeline. This is an important issue to address before ...