Benchmarking short sequence mapping tools
© Hatem et al.; licensee BioMed Central Ltd. 2013
Received: 9 August 2012
Accepted: 28 May 2013
Published: 7 June 2013
The development of next-generation sequencing instruments has led to the generation of millions of short sequences in a single run. The process of aligning these reads to a reference genome is time consuming and demands the development of fast and accurate alignment tools. However, the current proposed tools make different compromises between the accuracy and the speed of mapping. Moreover, many important aspects are overlooked while comparing the performance of a newly developed tool to the state of the art. Therefore, there is a need for an objective evaluation method that covers all the aspects. In this work, we introduce a benchmarking suite to extensively analyze sequencing tools with respect to various aspects and provide an objective comparison.
We applied our benchmarking tests on 9 well known mapping tools, namely, Bowtie, Bowtie2, BWA, SOAP2, MAQ, RMAP, GSNAP, Novoalign, and mrsFAST (mrFAST) using synthetic data and real RNA-Seq data. MAQ and RMAP are based on building hash tables for the reads, whereas the remaining tools are based on indexing the reference genome. The benchmarking tests reveal the strengths and weaknesses of each tool. The results show that no single tool outperforms all others in all metrics. However, Bowtie maintained the best throughput for most of the tests while BWA performed better for longer read lengths. The benchmarking tests are not restricted to the mentioned tools and can be further applied to others.
The mapping process is still a hard problem that is affected by many factors. In this work, we provided a benchmarking suite that reveals and evaluates the different factors affecting the mapping process. Still, there is no tool that outperforms all of the others in all the tests. Therefore, the end user should clearly specify his needs in order to choose the tool that provides the best results.
KeywordsShort sequence mapping Next-generation sequencing Benchmark Sequence analysis
Next-generation sequencing (NGS) technology has evolved rapidly in the last five years, leading to the generation of hundreds of millions of sequences (reads) in a single run. The number of generated reads varies between 1 million for long reads generated by Roche/454 sequencer (≈400 base pairs (bps)) and 2.4 billion for short reads generated by Illumina/Solexa and ABI/SOLIDTM sequencers (≈75 bps). The invention of the high-throughput sequencers has led to a significant cost reduction, e.g., a Megabase of DNA sequence costs only <DOLLAR/>0.1 .
Nevertheless, the large amount of generated data tells us almost nothing about the DNA, as stated by Flicek and Birney . This is due to the lack of proper analysis tools and algorithms. Therefore, bioinformatics researchers started to think about new ways to efficiently handle and analyze this large amount of data.
Neglecting base quality score.
Limiting the number of allowed mismatches.
Disabling gapped alignment or limiting the gap length.
Ignoring SNP information.
In most cases, it is unclear how such compromises affect the performance of newly developed tools in comparison to the state of the art ones. Therefore, many studies have been carried out to provide such comparisons. Some of the available studies were mainly focused on providing new tools (e.g., [10, 13]). The remaining studies tried to provide a thorough comparison while each covering a different aspect (e.g., [30-34]).
For instance, Li and Homer  classified the tools into groups according to the used indexing technique and the features the tools support such as gapped alignment, long read alignment, and bisulfite-treated reads alignment. In other words, in that work, the main focus was classifying the tools into groups rather than evaluating their performance on various settings.
Similar to Li and Homer, Fronseca et al.  provided another classification study. However, they included more tools in the study, around 60 mappers, while being more focused on providing a comprehensive overview of the characteristics of the tools.
Ruffalo et al.  presented a comparison between Bowtie, BWA, Novoalign, SHRiMP, mrFAST, mrsFAST, and SOAP2. Unlike the above mentioned studies, Ruffalo et al. evaluated the accuracy of the tools in different settings. They defined a read to be correctly mapped if it maps to the correct location in the genome and has a quality score higher than or equal to the threshold. Accordingly, they evaluated the behavior of the tools while varying the sequencing error rate, indel size, and indel frequency. However, they used the default options of the mapping tools in most of the experiments. In addition, they considered small simulated data sets of 500,000 reads of length 50 bps while using an artificial genome of length 500Mbp and the Human genome of length 3Gbp as the reference genomes.
Another study was done by Holtgrewe et al. , where the focus was the sensitivity of the tools. They enumerated the possible matching intervals with a maximum distance k for each read. Afterwards, they evaluated the sensitivity of the mappers according to the number of intervals they detected. Holtgrewe et al. used the suggested sensitivity evaluation criteria to evaluate the performance of SOAP2, Bowtie, BWA, and Shrimp2 on both simulated and real datasets. However, they used small reference genomes (the S. cerevisiae genome of length 12 Mbp and the D. melanogaster genome of length 169 Mbp). In addition, the experiments were performed on small real data sets of 10,000 reads. For evaluating the performance of the tools on real data sets, Holtgrewe et al. used RazerS to detect the possible matching intervals. RazerS is a full sensitive mapper, hence it is a very slow mapper . Therefore, scaling the suggested benchmark process for realistic whole genome mapping experiments with millions of reads is not practical. Nevertheless, after the initial submission of this work, RazerS3  was published, thus, making a significant improvement in the running time of the evaluation process.
Schbath et al.  also focused on evaluating the sensitivity of the sequencing tools. They evaluated if a tool correctly reports a read as a unique or not. In addition, for non-unique reads, they evaluated if a tool detects all of the mapping locations. However, in their work, like many previous studies, the tools were used with default options, and they tested the tools with a very small read length of 40 bps. Additionally, the error model they used did not include indels and allowed only 3 mismatches.
Even though many studies have been published for evaluating short sequence mapping tools, the problem is still open and further perspectives were not tackled in the current studies. For instance, the above studies did not consider the effect of changing the default options and using the same options across the tools. In addition, some of the studies used small data sets (e.g., 10,00 and 500,000 reads) while using small reference genomes (e.g., 169Mbps and 500Mbps) [31, 32]. Furthermore, they did not take the effect of input properties and algorithmic features into account. Here, input properties refer to the type of the reference genome and the properties of the reads including their length and source. Algorithmic features, on the other hand, pertain to the features provided by the mapping tool regarding its performance and utility. Therefore, there is still a need for a quantitative evaluation method to systematically compare mapping tools in multiple aspects. In this paper, we address this problem and present two different sets of experiments to evaluate and understand the strengths and weaknesses of each tool. The first set includes the benchmarking suite, consisting of tests that cover a variety of input properties and algorithmic features. These tests are applied on real RNA-Seq data and genomic resequencing synthetic data to verify the effectiveness of the benchmarking tests. The real data set consists of 1 million reads while the synthetic data sets consist of 1 million reads and 16 million reads. Additionally, we have used multiple genomes with sizes varying from 0.1 Gbps to 3.1 Gbps. The second set includes a use case experiment, namely, SNP calling, to understand the effects of mapping techniques on a real application.
Furthermore, we introduce a new, albeit simple, mathematical definition for the mapping correctness. We define a read to be correctly mapped if it is mapped while not violating the mapping criteria. This is in contrast to previous works where they define a read to be correctly mapped if it maps to its original genomic location. Clearly, if one knows “the original genomic location”, there is no need to map the reads. Hence, even though such a definition can be considered more biologically relevant, unfortunately this definition is neither sufficient nor computationally achievable. For instance, a read could be mapped to the original location with two mismatches (i.e., substitution error or SNP) while there might exist a mapping with an exact match to another location. If a tool does not have any a-priori information about the data, it would be impossible to choose the two mismatches location over the exact matching one. One can only hope that such tool can return “the original genomic location” when the user asks the tool to return all matching locations with two mismatches or less. Indeed, as later shown in the paper, our suggested definition is computationally more accurate than the naïve one. In addition, it complements other definitions such as the one suggested by Holtgrewe et al. .
To assess our work, we apply these tests on nine well known short sequence mapping tools, namely, Bowtie, Bowtie2, BWA, SOAP2, MAQ, RMAP, Novoalign, GSNAP, and mrFAST (mrsFAST). Unlike the other tools in this study, mrFAST (mrsFAST) is a full sensitive exact mapper that reports all the mapping locations. Therefore, comparing the mapping accuracy performance of mrFAST with the remaining tools is beneficial in further understanding the behavior of the different tools, even though comparing the execution time performance will not be fair. Moreover, we compare the performance of these tools with that of FANGS, a long read mapping tool, to show their effectiveness in handling long reads. The remaining tools were chosen according to the indexing techniques they use. Therefore, we can emphasize on the effect of the indexing technique on the performance. The experiments are carried out while using the same options for the tools, whenever possible.
The paper is organized as follows: in the next section, we briefly describe the sequence mapping problem, the mapping techniques used by the tools, and various evaluation criteria used to evaluate the performance of the tools including other definitions for mapping correctness. Then, we discuss how we designed the benchmarking suite and give a real application for the mapping problem. Finally, we present and explain the results for our benchmarking suite.
The exact matching of DNA sequences to a genome is a special case of the string matching problem. It requires incorporating the known properties or features of the DNA sequences and the sequencing technologies, thus, adding additional complexity to the mapping process. In this section, we first give a brief description of a set of features of DNA and sequencing technologies. Then, we explain how the tools used in this study work and support these features. Additionally, we describe the default options setup and show how divergent they are among the tools. Finally, we compare the evaluation criteria used in previous studies.
Seeding represents the first few tens of base pairs of a read. The seed part of a read is expected to contain less erroneous characters due to the specifics of the NGS technologies. Therefore, the seeding property is mostly used to maximize performance and accuracy.
Base quality scores provide a measure on correctness of each base in the read. The base quality score is assigned by a phred-like algorithm [35, 36]. The score Q is equal to -10 log10(e), where e is the probability that the base is wrong. Some tools use the quality scores to decide mismatch locations. Others accept or reject the read based on the sum of the quality scores at mismatch positions.
Existence of indels necessitates inserting or deleting nucleotides while mapping a sequence to a reference genome (gaps). The complexity of choosing a gap location increases with the read length. Therefore, some tools do not allow any gaps while others limit their locations and numbers.
Paired-end reads result from sequencing both ends of a DNA molecule. Mapping paired-end reads increases the confidence in the mapping locations due to having an estimation of the distance between the two ends.
Color space read is a read type generated by SOLiD sequencers. In this technology, overlapping pairs of letters are read and given a number (color) out of four numbers . The reads can be converted into bases, however, performing the mapping in the color space has advantages in terms of error detection.
Splicing refers to the process of cutting the RNA to remove the non-coding part (introns) and keeping only the coding part (exons) and joining them together. Therefore, when sequencing the RNA, a read might be located across exon-exon junctions. The process of mapping such reads back to the genome is hard due to the variability of the intron length. For instance, the intron length ranges between 250 and 65,130 nt in eukaryotic model organisms .
SNPs are variations of a single nucleotide between members of the same species. SNPs are not mismatches. Therefore, their locations should be identified before mapping reads in order to correctly identify actual mismatch positions.
Bisulphite treatment is a method used for the study of the methylation state of the DNA . In bisulphite treated reads, each unmethylated cytosine is converted to uracil. Therefore, they require special handling in order not to misalign the reads.
Hash Tables: The hash based methods are divided into two types: hashing the reads and hashing the genome. In general, the main idea for both types is to build a hash table for subsequences of the reads/genome. The key of each entry is a subsequence while the value is a list of positions where the subsequence can be found. Hashing based tools include the following tools:
GSNAP is a genome indexing tool. The hash table is built by dividing the reference genome into overlapping oligomers of length 12 sampled every 3 nucleotides. The mapping phase works by first dividing the read into smaller substrings, finding candidate regions for each substring, and finally combining the regions for all of the substrings to generate the final results. GSNAP was mainly designed to detect complex variants and splicing in individual reads. However, in this study, GSNAP is only used as a mapper to evaluate its efficiency.
Novoalign is a genome indexing tool. Similar to GSNAP, the hash table is built by dividing the reads into overlapping oligomers. The mapping phase uses the Needleman-Wunsch algorithm with affine gap penalties to find the global optimum alignment.
mrFAST and mrsFAST[6, 21] are genome indexing tools. They build a collision free hash table to index k-mers of the genome. mrFAST and mrsFAST are both developed with the same method, however, the former supports gaps and mismatches while the latter supports only mismatches to run faster. Therefore, in the following, we will use mrsFAST for experiments that do not allow gaps and mrFAST for experiments that allow gaps. Unlike the other tools, mrFAST and mrsFAST report all of the available mapping locations for a read. This is important in many applications such as structural variants detection.
FANGS is a genome indexing tool. In contrary to the other tools, it is designed to handle the long reads generated by the 454 sequencer.
MAQ is a read indexing tool. The algorithm works by first constructing multiple hash tables for the reads. Then, the reference genome is scanned against the tables to find the mapping locations.
RMAP is a read indexing tool. Similar to MAQ, RMAP pre-processes the reads to build the hash table, then the reference genome is scanned against the hash table to extract the mapping locations.
Most of the newly developed tools are based on indexing the genome. Nevertheless, MAQ and RMAP are included in this study to investigate the effectiveness of our benchmarking tests on evaluating read indexing based tools. In addition, we investigate if there is any potential for the read indexing technique to be used in new tools.
Burrows-Wheeler Transform (BWT):
BWT is an efficient data indexing technique that maintains a relatively small memory footprint when searching through a given data block. BWT was extended by Ferragina and Manzini  to a newer data structure, named FM-index, to support exact matching. By transforming the genome into an FM-index, the lookup performance of the algorithm improves for the cases where a single read matches multiple locations in the genome. However, the improved performance comes with a significantly large index build up time compared to hash tables.
BWT based tools include the following:
Bowtie starts by building an FM-index for the reference genome and then uses the modified Ferragina and Manzini  matching algorithm to find the mapping location. There are two main versions of Bowtie namely Bowtie and Bowtie 2. Bowtie 2 is mainly designed to handle reads longer than 50 bps. Additionally, Bowtie 2 supports features not handled by Bowtie. It was noticed that both versions had different performance in the experiments. Therefore, both versions are included in this study.
BWA is another BWT based tool. The BWA tool uses the Ferragina and Manzini  matching algorithm to find exact matches, similar to Bowtie. To find inexact matches, the authors provided a new backtracking algorithm that searches for matches between substring of the reference genome and the query within a certain defined distance.
SOAP2 works differently than the other BWT based tools. It uses the BWT and the hash table techniques to index the reference genome in order to speed up the exact matching process. On the other hand, it applies a “split-read strategy”, i.e., splits the read into fragments based on the number of mismatches, to find inexact matches.
Features supported by the tools
Up to 3
Up to 2
Var. seed len.
In some cases, the features are partially supported. For example, SOAP2 supports gapped alignment only for paired end reads, while BWA limits the gap size. Therefore, considering only one of the above features when comparing between the tools would lead to under- or over-estimation of the tools’ performance.
Default options of the tested tools
Maximum number of mismatches in the seed: the seed based tools use a default value of 2.
Maximum number of mismatches in the read: Bowtie2, BWA, and GSNAP determine the number of mismatches based on the read length. It is 10 for RMAP, 2 for mrsFAST, and 5 for SOAP2, FANGS, and mrFAST.
Seed length: It is 28 for MAQ, 32 for RMAP, and 28 for Bowtie. BWA disables seeding while SOAP2 considers the whole read as the seed.
Quality threshold: It is equal to 70 for MAQ and Bowtie while it depends on the read length and the genome size for Novoalign.
Splicing: This option is enabled for GSNAP.
Gapped alignment: It is enabled for Bowtie2, GSNAP, BWA, Novoalign and MAQ while it is disabled for SOAP2.
Minimum and maximum insert sizes for paired-end mapping: The insert size represents the distance between the two ends. The values used for the minimum and the maximum insert sizes are 0 and 250 for Bowtie and MAQ, 0 and 500 for BWA and Bowtie2, 400 and 500 for SOAP2, and 100 and 400 for RMAP. mrFAST and mrsFAST do not have default values for max and min insert sizes.
Indeed, as will be shown in the results’ section, having different default values lead to different results for the same data set. Hence, using the same values when comparing between the tools is important.
In general, the performance of the tools is evaluated by considering three aspects, namely, the throughput or the running time, the memory footprint, and the mapping percentage. The throughput is the number of base pairs mapped per second (bps/sec) while the memory footprint is the required memory by the tool to store/process the read/genome index. The mapping percentage is the percentage of reads each tool maps.
The naïve definition for the error was further modified by Ruffalo et al.  to develop a more concrete definition. The authors incorporated the mapping quality information such that a read is correctly mapped if it is mapped to the original genomic location while having a mapping quality greater than a certain threshold. They further categorized the incorrectly mapped reads into incorrectly mapped-strict and incorrectly mapped-relaxed. The incorrectly mapped-strict are the reads that were mapped with a quality higher than the threshold while not mapped to the original genomic location. On the other hand, the incorrectly mapped-relaxed are the reads that were mapped to an incorrect location with a quality higher than the threshold and there is no correct mapping for the read with a mapping quality higher than the threshold. As an example, in Figure 1, if the used threshold is 30, then the read would be considered correctly mapped if the tool returned alignment (1) while it would be considered as incorrectly mapped-strict if the tool returned either alignment (2) or (3). On the other hand, if the used threshold is 40, a read would be incorrectly mapped-relaxed if the tool returned alignment (3). Indeed, this is a valuable evaluation criterion, however, many tools, such as SOAP2, RMAP, and BWA, do not use quality scores in the mapping phase. In addition, not all of the tools report the mapping quality.
Another definition was introduced by Holtgrewe et al. . Unlike the previous works, the authors tried to find a gold standard for each read, where a gold standard refers to all of the possible matching intervals for each read with a maximum distance k from the read. To enumerate all of the possible matching intervals, the authors used RazerS to detect the initial seed location for each interval. Afterwards, they developed a method to find the boundary of the interval centered at the seed and with a maximum distance k from the read. They named the suggested evaluation method Rabema. As an example, a possible interval with k=3 would contain alignment (1) and (2) in Figure 1. Accordingly, Holtgrewe et al. defined the false negatives as the intervals missed by the mapper and the false positives as the intervals returned by the mapper and not included in the gold standard. However, consisting of seed detection phase and enumeration phase while depending on RazerS to return seed locations for the matching intervals makes Rabema impractical to apply on large genomes and long read lengths, e.g., RazerS took 25 hours to map 1 million reads of length 100 to the Human genome while doubling the running time when increasing the read length from 75 to 100 . Therefore, Holtgrewe et al. suggested another mode, an oracle mode, which makes use of the original location of simulated reads. The oracle mode uses the original location as the seed location instead of using RazerS to detect the initial seed locations. However, this method is only suitable in case of a-priori knowledge that the possible mapping locations for a read are around the simulated location (e.g., alignment (3) in Figure 1 would be missed in the oracle mode). Nevertheless, after the initial submission of this work, RazerS3  was published; making a significant improvement in Rabema running time and elevating the slowness problem. Even though the suggested definition for a gold standard quantitatively estimates the sensitivity for each mapper, it suffers from a couple of drawbacks. First, the definition does not take into consideration whether the alignments are violating the mapping criterion for the mapper or not. For instance, in Figure 1, the sensitivity of the mapper would increase if it detected alignments (1), (2), and (3). However, if the mapping criterion for the mapper is to allow a maximum of two mismatches, then alignment (1) should have not been detected by the mapper and should be considered as a wrong alignment or error. Second, quality aware based tools, such as Bowtie, MAQ, and Novoalign, would be incorrectly evaluated by Rabema since they use the quality threshold to accept or reject a read instead of calculating the edit or hamming distance. Therefore, they might map a read with more mismatches than the limit allowed by Rabema.
Quality threshold: MAQ, Bowtie, and Novoalign use the quality threshold to determine the number of allowed mismatches. Therefore, setting a quality threshold is similar to explicitly setting the number of mismatches. However, there is no hard limit on the actual number of mismatches. The impact of varying the quality threshold while finding a mapping between the quality threshold and the number of mismatches has not been studied before.
Number of mismatches: Changing the number of allowed mismatches affects the percentage of mapped reads. This effect was studied in , however, the mismatches were generated uniformly on the genome which does not mimic real mismatches distribution.
Seed length: Seeding-based tools impose limits on the number of mismatches in the seed part. As a result, increasing or decreasing the length of the seed part affects the percentage of mapped reads. The effect of the seed length has not been studied in details before.
Read length: The read length varies between 30bps for ABI’s SOLiD and Illumina’s Solexa sequencers up to 500 bps for Roche’s 454. Therefore, the impact of read length should be considered for throughput evaluation. Even though the effect of the read length was explored in several studies, the default options were usually used leading to incomparable trade-offs.
Paired-end reads: Mapping paired reads requires the mapping of both ends within a maximum distance between them. Hence, it adds a constraint when finding the corresponding genomic locations.
Genome type: The efficiency of most algorithms are tested by using the Human genome as the reference. However, each genome has its own properties such as the percentage of repeated regions and ambiguous characters. Therefore, using a single genome does not reveal the effect of these properties. To the best of our knowledge, BWA  was the only tool to test its performance on a large genome other than the Human.
Gapped alignment: is important for variant discovery due to the ability to detect indel polymorphism . Bowtie2, GSNAP, Novoalign, BWA, and mrFAST are the only tools to support it for single-end reads while the remaining tools support it for paired-end only. However, from the results provided by the previous studies, it is not obvious how gapped alignment affects the performance of the tools in comparison to allowing only mismatches.
SNP awareness: Incorporating SNP information into mapping allows considering minor alleles as matches rather than mismatches. Currently, this feature is provided only by GSNAP. It was shown in  that integrating SNP information affected around 8% of the reads and allowed mapping 0.4% of unmapped reads.
Splicing awareness: Reads located across exon-exon junctions would be wrongly aligned using standard alignment algorithms. Splicing awareness is only required for certain types of data such as RNA-Seq data. The only tool that currently supports splicing while performing the mapping phase is GSNAP. It was shown in  that the alignment yield increased by 8-9% when splicing detection based on known splice junctions was introduced. However, there was only 0.3-0.6% increase in case of detecting novel splice junctions.
The scalability of the mapping tools may be different under different parallel settings. Many tools support multithreading, which is expected to yield linearly increasing speedup with the increase in the number of CPU cores. On the other hand, using multiprocessing is more general and may improve the throughput even for tools that do not support multithreading (e.g., MAQ and RMAP), where multiprocessing refers to using more than one process in a distributed memory fashion while communicating through a message passing interface.
Each tool is expected to map a set of reads based on its mapping criteria. However, a subset of the reads might not be mapped (i.e., false negatives) due to using heuristics in the mapping algorithm or the default options limitations. Moreover, some of the tools map a subset of these reads while violating the mapping criteria.
Rabema benchmark enumerates all of the possible matching locations. Then, it evaluates whether the tool detected the possible matching locations with the specified error rate or not. Therefore, Rabema evaluation is a valuable one and helps in adding another perspective when comparing between the tools.
Usecase: SNP calling
SNP calling is the process of detecting genetic variations in a given genome. The genetic variations contribute to the generation of different phenotypes for the same gene, leading to increasing the risk of having complex diseases. Therefore, the discovery of SNPs is a very important process that needs to be done accurately. Many tools have been developed to detect SNPs including ssahaSNP  and SNPdetector . These tools were developed to analyze the DNA sequences generated using either the Sanger or the direct PCR amplification methods. However, with the development of the next generation sequencing technology, new tools are required to analyze the new data . The developed new tools work by first mapping sequences to a reference genome, then using statistical analysis methods to extract SNPs  after filtering out low-quality mismatches. Therefore, accurately mapping the reads to the reference genome is a very crucial task in the SNP calling pipeline.
Results and discussion
In this section, we present the results from our benchmarking tests. The experiments were performed on a cluster of quad-core AMD Opteron CPUs at 2.4 GHz with 32 GB of RAM. We used SOAP2 v2.20, Bowtie v0.12.6 and v2.0.0-beta5, BWA v0.5.0, MAQ v0.7.0, RMAP v2.05.0, FANGS v0.2.3, GSNAP v2010-07-27, Novoalign v2.07.0, and mrFAST and mrsFAST v188.8.131.52.
Performance evaluation: The performance is evaluated by considering two factors, namely, the mapping percentage and the throughput. The mapping percentage is the percentage of reads each tool maps while the throughput is the number of mapped base pairs per second (bps/sec). The throughput is calculated by dividing the number of reads mapped over the running time. For genome indexing based tools, the running time includes only the matching time while it includes the indexing and matching time for read indexing based tools. However, the running time for mrsFAST includes also the indexing time even though it is a genome indexing based tool. This is due to the dependence of the sensitivity of mrsFAST on the window size used in the indexing phase. Therefore, the index is rebuilt in most of the experiments to maintain a full sensitivity for mrsFAST.
Correctly mapped reads: The percentage of reads mapped within the mapping criteria.
Error: The percentage of reads mapped while violating the mapping criteria. As shown in the background section, this definition provides another evaluation perspective that was not covered by older definitions.
Amb: The percentage of reads mapped to more than one location with the same number of mismatches. Most of the tools can return more than one mapping location for Amb reads if desired. However, RMAP only reports the number of Amb reads while not providing any information regarding the mapping location and the number of mismatches. Therefore, we will not be able to report the mismatches distribution for the RMAP reported Amb reads.
Synthetic data: There is a number of tools available to extract synthetic, Fastq format, data sets from a reference genome including wgsim, dwgsim, Mason, and ART. wgsim generates reads with uniform error distribution while dwgsim provides a uniformly increasing/decreasing error rate. On the other hand, Mason and ART mimic the error rates for Illumina and 454 sequencers. In this study, we are using wgsim and ART to generate the synthetic data from the Human genome. wgsim helps in providing a fair comparison between the tools by using a uniform error distribution model resulting in the same quality score for each base. Therefore, all of the tools can be allowed exactly the same number of mismatches regardless of the technique used to set the maximum number. For wgsim, the reads were generated with 0.09% SNP mutation rate, 0.01% indel mutation rate, 2% uniform sequencing base error rate, and with a maximum insert size of 500, which are the same parameters used in . Additionally, Dohm et al.  showed that the sequencing error rate for Illumina changes between 0.3% for the beginning of the read and 3.8% at the end of the read. Therefore, a uniform sequencing error rate of 2% is acceptable. Moreover, according to the error rates and indels rate used by the Mason simulator , an indel rate of 0.01% is acceptable. We determined the number of reads to generate using wgsim based on the used tool and the experiment. On the other hand, ART does not explicitly allow the user to choose the number of generated reads. ART generates reads that cover the whole genome with a given coverage level. Therefore, to manage generating 1 million reads, we used ART to generate reads that cover the whole genome with 1x coverage. Then, we randomly selected 1 million reads from the output reads.
To make sure that the results are not affected by different wgsim runs, we generated 13 different wgsim data sets and ran a sample of the tools independently on each data set. The sample included BWA, GSNAP, Bowtie, Bowtie2, and SOAP2. We found that the maximum standard deviation from the average was 0.03 (results are not included). Since there is no significant change between the runs, we will only carry each experiment once on a single data set.
Real data: There are many types of real data sets such as RNA-Seq data, Chip-Seq data, and DNA sequences that are used in different applications. It is important in our evaluation process to choose the right data set type to better evaluate the applicability of the tools in the different applications. Therefore, we prefer to use RNA-Seq data sets as they are used in many applications including SNP and alternative splicing detection. The used data set consists of 1 million reads generated by Illumina sequencer after isolating mRNA from the Spretus mouse colon tissues. The mouse genome version mm9 was used as the reference genome. Indeed, as will be shown, the tools have similar behavior on both the mouse and the human genomes. Therefore, there is no contradiction in using the human genome for generating the synthetic data while the mouse genome is used for the real ones.
In the remaining experiments, unless otherwise stated, the number of mismatches in the seed and in the whole read are fixed to 2 and 5, respectively, while the quality threshold is kept at 100. The minimum and maximum insert sizes allowed are 0 and 500, respectively. In addition, the splicing, SNPs, and gapped alignment options are disabled, unless otherwise stated. For the number of reported hits, tools are only allowed to report one location except for mrsFAST that does not have this option and report all of the mapping locations. The default values are used for the remaining options.
Quality threshold is one of the two main metrics used for mismatch tolerance. The other main metric is the explicit specification of the number of mismatches. To compare fairly between the tools, a relationship between the two metrics should be found, which is the main target of this experiment. In this experiment, wgsim is used to generate the data set instead of using ART or a real one. The different base quality scores in real data cause quality threshold based tools to allow more mismatches than the other tools. For instance, when allowing a quality threshold of 70 and 5 mismatches for the remaining tools, Bowtie and MAQ map reads with up to 10 mismatches while the other tools are limited to 5 (results are not included). Therefore, MAQ and Bowtie had a mapping percentage larger than the other tools, hence, the comparison is not fair. Nevertheless, in the following, we show how the quality threshold can be used to mimic the behavior of the explicit specification of the number of mismatches.
Even though the maximum insert size was 500, tools such as BWA, SOAP, and GSNAP mapped paired-end reads while exceeding the maximum insert size, except for Novoalign that explicitly requires the user to set the standard deviation for the insert size.
In general, the throughput of the tools increases when using ART instead of wgsim data sets. However, the relative performance between the tools and the different genomes is still the same.
For the real data set, mrsFAST (mrFAST) is not included in the results since the minimum allowed window size in the indexing phase does not guarantee a full sensitivity for mrFAST.
In general, using multiprocessing provides more degrees of freedom by parallelizing tools that do not support multithreading and by making use of the available computational resources.
Another important observation is the effect of the indexing method on the total speedup. Read indexing based tools did not have any significant speedup in comparison to the genome indexing based ones which had more than 5x speedup. Therefore, genome indexing is more efficient in case of designing a read partitioning parallelism based tool.
Sensitivity evaluation of the different tools
In general, the tools were able to map a percentage of the unmappable reads, however, it was mapped with a large error percentage. For instance, even though GSNAP mapped around 3% of the unmappable reads, only 0.03% of them were correctly mapped. Therefore, even though GSNAP maps the largest percentage of reads, other tools such as BWA and Novoalign are more accurate and precise than GSNAP.
It is important to note that the percentage of reads mapped from the unmappable reads is similar to the percentage of incorrectly mapped reads-relaxed given in Ruffalo et al. work . However, they define a read to be unmappable if it has a mapping quality less than a certain threshold while we consider it as unmappable if it violates the mapping criteria for the tool.
As shown in the results, both Novoalign and Bowtie are evaluated as mapping invalid reads. This is because Rabema does not take the quality scores into consideration and just calculate the edit distance. Therefore, from the mismatches perspective, the reads have more than 5 mismatches. However, from the quality threshold perspective, they have a quality threshold less than the specified one. Therefore, at the end, they are valid mappings.
In general, BWA has been able to detect almost all of the reads with the correct error rate. This suggest that most of the mismatches exist at the end of the read. In addition, the seeding technique is a valid method specially if it can speed up the mapping process.On the other hand, even though SOAP2 is a seed based tool, similar to BWA, it could not detect as much correct reads as BWA. Bowtie2 missed some of the reads, however, it can detect them by changing its sensitivity at the cost of increasing the running time. On the other hand, mrsFAST mapped all of the reads with the correct error rate since it is a full sensitive mapper.
Use case: SNP calling
SNP calling results
There have been many studies carried out to analyze the performance of short sequence mapping tools and choose the best tool among them. However, the analysis of short sequence mapping tools is still an active problem with many aspects have not been addressed yet. In this work, we provided a benchmarking study for short sequence mapping tools while tackling different aspects that have not been covered by previous studies. We mainly focused on studying the effect of different input properties, algorithmic features, and changing the default options on the performance of the different tools. Additionally, we provided a set of benchmarking tests which extensively analyze the performance of the different tools. Each of the benchmarking tests stresses on a different aspect. The benchmarking tests were further applied on a variety of short sequence mapping tools, namely, Bowtie, Bowtie2, BWA, SOAP2, MAQ, RMAP, GSNAP, Novoalign, mrsFAST (mrFAST), and FANGS.
The experiments show that some tools report an error percentage (i.e., reads mapped while violating the mapping criteria). Among these tools are GSNAP and SOAP2. GSNAP reported the highest error percentage in the experiments. Additionally, the error increases with the read length and it decreases with the the number of mismatches. Nevertheless, GSNAP was one of the tools which reports the largest mapping percentage in most of the experiments even after filtering out the error reads. The main reason for mapping more reads is allowing any number of mismatches in the seed part. From a real application perspective, GSNAP’s filtered output helped in detecting the largest number of SNPs.
The evaluation of Bowtie, Bowtie2, BWA, mrsFAST, and Novoalign show their ability to correctly map the reads. Moreover, Novoalign mapped the largest percentage of reads, similar to GSNAP, specially for highly repeated genomes. However, it maintained the lowest throughput among the genome indexing tools in most of the experiments.
mrsFAST’s throughput is highly affected by the read length and the number of mismatches. Our experiments show that it is better to use mrsFAST for longer reads. It can also be used for short reads but only with a small number of mismatches.
In general, genome indexing based tools performed better than read indexing tools in all of the experiments. However, MAQ was faster than Novoalign for small genomes. Therefore there is a potential for read indexing tools to be used for small genomes. In addition to providing the worst performance, read indexing does not provide any significant speedup in case of using read partitioning based parallelism. Therefore, the read indexing method is not preferred when designing a new read partitioning mapping tools.
Interestingly, the genome type experiment revealed many strengths and weaknesses for the tools. For instance, the performance of SOAP2, GSNAP, and Novoalign is highly dependent on the genome type; the throughput decreased significantly for the Zebrafish genome. This is due to the large repetition rate on the Zebrafish genome. In addition, the tools behaved differently on the Human and the Chimpanzee genomes albeit having comparable genome sizes. The results of the genome type experiment suggest that the different properties of the genomes affect the performance of the tools. Therefore, further investigations are required to understand the different properties of the genomes and their effect on the different mapping techniques.
Even though there are differences between the results for the real data sets and the synthetic ones, both experiments are important as they give us a different perspective when comparing between the tools. The control on the number of mismatches for the wgsim synthetic data allows us to know exactly what the throughput of each tool is while looking for exactly the same number of mismatches. Therefore, it becomes easier to understand why a tool is faster than another one or why a tool seems to map more reads than the other ones. At the same time, it is important to look at the behavior of the tools in case of real data and real-like synthetic data (e.g., ART) to further understand how they behave in the real world. For instance, for the number of mismatches experiment, even though Bowtie looks like it maps a percentage of reads similar to the other tools in case of 7 t-mms, it actually maps the reads with a maximum of 4 t-mms. Therefore, the output mappings are more accurate than the other tools.
In general, there is no the-best tool among all of the tools; each tool was the-best in certain conditions. The short sequence mapping problem is still an active problem and new tools are needed to be developed. However, these new tools should be application-specific. By taking the target application into consideration, more accurate results can be obtained. For instance, for genome assembly, we can analyze the reference genome and estimate the number of reads that can be mapped for the different regions (e.g., repeated regions) based on the coverage information in the sequencing process. Another example for an application with very specific properties is the mapping of RNA-Seq data which contain short sequences for the exon regions rather than intron regions for the genome. Therefore, for well-studied genomes, if a small number of reads where mapped to different intron regions, we can expect them to be wrongly mapped and look for other mapping locations with more number of mismatches or less mapping quality.
Links to tools, datasets, parameters used in each individual tool and test, and the code used to verify the tools are available at http://bmi.osu.edu/hpc/software/benchmark/.
This work was supported in parts by the DOE grant DE-FC02-06ER2775; by the NSF grants CNS-0643969, OCI-0904809, and OCI-0904802, by the NIH grant R01 CA133461, and by grant NPRP 4-1454-1-233 from the Qatar National Research Fund (a member of The Qatar Foundation).
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