CLAST: CUDA implemented large-scale alignment search tool
© Yano et al.; licensee BioMed Central Ltd. 2014
Received: 5 June 2014
Accepted: 2 December 2014
Published: 11 December 2014
Metagenomics is a powerful methodology to study microbial communities, but it is highly dependent on nucleotide sequence similarity searching against sequence databases. Metagenomic analyses with next-generation sequencing technologies produce enormous numbers of reads from microbial communities, and many reads are derived from microbes whose genomes have not yet been sequenced, limiting the usefulness of existing sequence similarity search tools. Therefore, there is a clear need for a sequence similarity search tool that can rapidly detect weak similarity in large datasets.
We developed a tool, which we named CLAST (CUDA implemented large-scale alignment search tool), that enables analyses of millions of reads and thousands of reference genome sequences, and runs on NVIDIA Fermi architecture graphics processing units. CLAST has four main advantages over existing alignment tools. First, CLAST was capable of identifying sequence similarities ~80.8 times faster than BLAST and 9.6 times faster than BLAT. Second, CLAST executes global alignment as the default (local alignment is also an option), enabling CLAST to assign reads to taxonomic and functional groups based on evolutionarily distant nucleotide sequences with high accuracy. Third, CLAST does not need a preprocessed sequence database like Burrows–Wheeler Transform-based tools, and this enables CLAST to incorporate large, frequently updated sequence databases. Fourth, CLAST requires <2 GB of main memory, making it possible to run CLAST on a standard desktop computer or server node.
CLAST achieved very high speed (similar to the Burrows–Wheeler Transform-based Bowtie 2 for long reads) and sensitivity (equal to BLAST, BLAT, and FR-HIT) without the need for extensive database preprocessing or a specialized computing platform. Our results demonstrate that CLAST has the potential to be one of the most powerful and realistic approaches to analyze the massive amount of sequence data from next-generation sequencing technologies.
The rapid development of sequencing technologies has resulted in a flood of new data. For example, a single run of the latest version of the Illumina sequencing system (HiSeq 2500) can produce ~540–600 Gb of sequences with 100-bp read lengths, and can take >11 days . These technologies have made it easier to perform massive sequencing projects such as metagenomic analyses. For example, 3.3 million genes, representing the human gut metagenome, were derived from 124 human fecal samples using next generation sequence technologies . Similarly, the Human Microbiome Project (HMP) produced >8.8 Tb of sequences, representing the normal human metagenome, from 681 samples using the Illumina Genome Analyzer IIx system .
Most fundamental metagenomic analyses are highly dependent on sequence alignment tools, such as the Basic Local Alignment Search Tool (BLAST) , BLAST-like Alignment Tool (BLAT) , and Fragment Recruitment at High Identity with Tolerance (FR-HIT) algorithm , to search for nucleotide sequence similarity against sequence databases. The alignment sensitivity of the tool is a crucial factor for metagenomic studies because many of the reads are derived from microbes whose genomes have not yet been sequenced. On the other hand, the search speed is also an important issue because of the increasing amount of data produced from advances in sequencing platforms. For instance, SSEARCH , which is an alignment tool based on the Smith–Waterman local alignment algorithm, is too slow to use for massive metagenomic analyses. However, the sensitivity and search speed often have contradictory requirements, and thus most alignment tools used for metagenomic studies sacrifice one of these aspects.
An effective way to accelerate sequence similarity searching while maintaining sufficient sensitivity is to maximize the degree of parallelism. Use of graphics processing units (GPUs) is a suitable way to parallelize calculations with low financial and computational cost, because GPUs are relatively inexpensive, powerful, and widely used for high-performance computing. Many GPU-based sequence similarity search tools have been developed, such as CUDASW++2.0, GPU-BLAST, GHOSTM, G-BLASTN, MUMmerGPU, and SARUMAN -. However, none of these GPU-based tools is suitable for metagenomic analyses because they cannot detect weak similarity of numerous query nucleotide sequences against reference genomes at reasonably high speed (Additional file 1).
Here, we developed a nucleotide sequence similarity search tool CLAST (CUDA implemented large-scale alignment search tool) that can rapidly detect weak sequence similarity with both short and long query read lengths. CLAST uses GPUs and searches with both global and local alignment algorithms. Global alignment facilitates taxonomic and functional assignment of metagenomic reads, and local alignment is useful in motif searching. Furthermore, we implemented a novel algorithm to construct the q-gram index , which allows sequence similarity searching with reference data that has not been pre-indexed. This feature minimizes the memory requirements of CLAST, and allows the use of large and frequently updated reference databases. CLAST was optimized for both the NVIDIA Fermi and more recent Kepler GPU architectures.
Method of detecting similar regions
In the first phase, CLAST creates k-mers along the reference genome sequence with a sliding window of k bases with a step of p bases (p and k are user-adjusted parameters). Next, CLAST constructs a read-only q-gram index from the k-mers of the reference sequence that dramatically accelerates similarity searching (the algorithm to create the read-only q-gram index is described below). Finally, CLAST creates seeds by referring k-mers across the query sequences to the q-gram index with a sliding window of k bases and a step of 1 base.
General algorithm for creating the read-only q-gram index in a parallel architecture
Firstly, we describe the procedure for constructing the read-only q-gram index that consists of data and index arrays from the original data that consisted of keys and values (independent of each other) of each element in the original data (Figure 3A). The data array consists of a sorted key array and a value array sorted by keys. The index array consists of the sorted non-redundant key array (redundant key elements removed), a cellSize array (the redundancy number of each key element), and a gateway array (exclusive prefix summation of the cellSize array).
Secondly, we describe the way to obtain the corresponding values stored in the read-only q-gram index of a queryKey (Figure 3B). A binary search of the sorted non-redundant key array provides the corresponding cellSize and gateway arrays of the queryKey (referred to as gottenCellSize and gottenGateway). The queryKey corresponds to the elements located from the gottenGateway to [gottenGateway + gottenCellSize −1] in the value array (as writeValues).
Finally, we describe the way to write the queryValue array and its corresponding values stored in the read-only q-gram index (Figure 3C). Referencing the read-only q-gram index values by the queryKey (usually generated from queryValue) array creates the gottenCellSize and writeValue arrays. To assign each queryValue and corresponding writeValue to a result array, a writeIndex array was computed by an exclusive prefix sum operation of the gottenCellSize array. The queryValue and corresponding writeValues are written as the location from the writeIndex to the [writeIndex + gottenCellSize −1] in the result array.
In CLAST, the key and value arrays of the original data are the k-mer and k-mer position hash keys, respectively. In addition to the general algorithm for creating the q-gram index, CLAST overwrites elements of the cellSize array that are larger than the repeat threshold (user-adjustable parameter) with zero to minimize uninformative sequence search seeds. In CLAST, the queryKey and queryValue arrays are the hash key and position information of each k-mer of the query sequences, respectively. Therefore, in CLAST, the result array indicates the correspondence of the k-mer position in the reference and query sequences and thus is the seed array.
Algorithm to reduce the number of seeds
Splitting of long reference sequences for alignment
Because of the small working memory in GPUs, CLAST is limited in the length of sequences that it can manipulate. Therefore, reference genome sequences longer than the user-defined limit L (default value is 64 Mb) must be split into shorter overlapping sequence fragments with a CLAST accessory tool. Because all prokaryotic genomes obtained to date are shorter than the default value of L, these reference genome sequences do not need to be fragmented for alignment.
Each CLAST process uses one GPU, and users can specify the GPU on which CLAST runs. This design allows a GPU queuing system to control CLAST processes.
CLAST accuracy evaluation by comparison with the Smith–Waterman algorithm
To measure the search accuracy of CLAST, we compared the output results of BLAST 2.2.25 , BLAT 34 , and CLAST against that of SSEARCH version 36.3.6  (hereafter referred to as the accuracy test). We chose only BLAST and BLAT in the accuracy test because these two tools are widely used in metagenomic analyses (e.g. MEGAN, which is a commonly used taxonomic and functional assignment tool for metagenomics, uses BLAST results for their taxonomic and functional assignment ; MG-RAST, which is a commonly used metagenomic analyses web service, uses BLAT for their sequence similarity analyses ). This comparison consisted of six phases. First, we obtained the reference genome sequences of all bacteria and archaea in the National Center for Biotechnology Information (NCBI) RefSeq Genome database (October 2011, 4.3 GB, 2,314 sequences)  that were completely sequenced and had full taxonomic information. Second, we created two query sets (100-base test; 10,000 reads of 100 bases as simulated-Illumina reads, 800-base test; 10,000 reads of 800 bases as simulated-454 reads) by randomly retrieving 100-base and 800-base sequence fragments from the 2,314 reference genome sequences. Thirdly, these query sets were searched against the reference genome sequences using SSEARCH, BLAST, BLAT, and CLAST. Fourthly, we removed hits from the results for each alignment tool if the assigned regions and query were from the same reference genome sequence. This step makes the result equivalent to a search for the query sequence in the reference sequence database without the original query genome sequence. Fifthly, we selected the best non-self hits from the result of each tools with the scoring criteria dependent on the alignment tool. Then sixthly, BLAST, BLAT, and CLAST were considered to accurately find a hit when they reported the same hit and alignment position as SSEARCH. This accuracy test was performed on a desktop computer with an Intel Xeon X5670 6 core 2.93 GHz CPU, 48 GB main memory, and two NVIDIA Tesla C2050 GPUs.
Results of comparison of search accuracy
Evaluating speed, sensitivity, and accuracy of taxonomic assignments
Massive metagenomic analyses generally depend on the alignment for each read against reference genomes to assign taxonomy for the read. Therefore, we designed a simulated metagenomic analysis test to evaluate the sensitivity and accuracy of the taxonomic assignments as well as calculation time.
In the simulated metagenomic analysis test, we compared CLAST with other similar tools, namely BLAST 2.2.25, BLAT 34, FR-HIT v0.6, Burrows–Wheeler Aligner (BWA)/BWA-SW 0.5.9, Bowtie 2 2.0.4, and G-BLASTN 1.1, which depends on BLAST 2.2.28+ -,-. G-BLASTN was separately compared with CLAST because G-BLASTN was designed for the NVIDIA Kepler architecture GPU. The default command line options were used for each alignment tool tested (Additional file 5). BWA/BWA-SW 0.5.9, Bowtie 2 2.0.4, and BLAT 34 cannot handle databases larger than 4 GB -. Therefore, we separated the reference genome sequences into three sets for testing these programs. Similarity search results from the three sets were merged for comparison with the results from BLAST, CLAST, and FR-HIT. The best non-self hits were selected using CIGAR code and MD tag (BWA), E-value (FR-HIT), and alignment score (Bowtie 2, BWA-SW, BLAST, BLAT, and CLAST). The simulated metagenomic analysis test (except for G-BLASTN) was performed on the same desktop computer as the accuracy test.
Results of comparison of calculation time between CLAST and other tools
In the 100-base test, Bowtie 2 (global mode) was the fastest tool, followed by Bowtie 2 (local mode), BWA, CLAST (global mode), CLAST (local mode), BLAT, FR-HIT (both global and local modes), and BLAST. CLAST (global mode) was 72.6 times faster than BLAST. CLAST (local mode) speed was comparable to CLAST (global mode) and 2.35 times faster than BLAT.
Results of comparison of similarity search sensitivity and accuracy of taxonomic assignment
Calculation time using multiple GPUs
We ran CLAST on one, two, and eight GPUs with actual metagenomic reads to investigate the effect of GPU number on the calculation time. The reference genome sequences were the same as that used in the simulated metagenomic analysis test. The query sequences, which are the Illumina Genome Analyzer IIx reads from a human gut microbial community, were obtained from Qin et al.  (NCBI SRA accession number ERR011343; 75 bp, 21,739,219 reads). For this test, we used a 4-node GPU server. Each of the node had an Intel Xeon X5690 6 core 3.47 GHz CPU, 64 GB main memory, and two NVIDIA Tesla C2075 GPUs.
Results of calculation time using multiple GPUs
Comparison with G-BLASTN
In addition to comparison of CLAST with CPU-based tools, we compared the speed, sensitivity, and accuracy of CLAST taxonomic assignments to those of G-BLASTN (BLAST algorithm optimized for Kepler architecture GPU computing). The dataset and the analysis pipeline for comparison with G-BLASTN were the same as those of the simulated metagenomic analysis test. We used a workstation with two Intel Xeon E5-2687 W 8 core 3.10 GHz CPUs, 62.9 GB main memory, and two NVIDIA Tesla K20m GPUs (hereafter referred to as the two K20 machine). If the CLAST algorithm achieves the same speed as that of G-BLASTN, G-BLASTN (default settings) would be approximately two times as fast as CLAST (default settings) on the two K20 machine because G-BLASTN automatically uses all available GPUs, and one CLAST process uses only the one specified GPU. We compared CLAST to G-BLASTN run in the megablast mode (designed to identify only similar sequences) and blastn modes (command line parameters are -use_gpu true -outfmt 6 -task megablast and -use_gpu true -outfmt 6 -task blastn).
Results of comparison with G-BLASTN
In the simulated metagenomic analysis test, the G-BLASTN (blastn mode) analysis took 15,970 s when the query length was 100 bases, and 136,560 s when the query length was 800 bases, on the two K20 machines. On the other hand, CLAST took 210 s (global mode) and 215 s (local mode) for the 100-base query length, and 1,248 s (global mode) and 1,352 s (local mode) for the 800-base query length in the same GPU architecture. In other words, CLAST was 150–200 times faster than G-BLASTN (blastn mode). Furthermore, G-BLASTN (megablast mode) took 199 s when query length was 100 bases, and 724 s when query length was 800 bases. Thus, CLAST was 1.07–1.85 times faster than G-BLASTN (megablast mode). These results suggest that CLAST is much faster than G-BLASTN (blastn mode) and slightly faster than G-BLASTN (megablast mode).
The total reported hits and correct genus assignments of G-BLASTN (blastn mode) were 99,841 and 56,151, respectively (CGA ratio: 58%), when the query length was 100 bases. The total reported hits and correct genus assignments of G-BLASTN (blastn mode) were 100,000 and 62,728, respectively (CGA ratio: 63%), when the query length was 800 bases. Thus, G-BLASTN (blastn mode) performed similarly to BLAST in the simulated metagenomic analysis test. This result suggests that CLAST (local mode) can detect as much information as G-BLASTN (blastn mode) when the query length is 800 bases.
The total reported hits and correct genus assignments of G-BLASTN (megablast mode) were 46,720 and 42,664, respectively (CGA ratio: 91%), when the query length was 100 bases. The total reported hits and correct genus assignments of G-BLASTN (megablast mode) were 65,108 and 52,754, respectively (CGA ratio: 81%), when the query length was 800 bases. Thus, G-BLASTN (megablast mode) was similar to Bowtie 2 (local mode) in the simulated metagenomic analysis test. This result showed that the accuracy of taxonomic assignments of CLAST (global mode) is greater than that of G-BLASTN (megablast mode) and that the sensitivity of CLAST (local mode) is greater than that of G-BLASTN (megablast mode).
In addition to its speed and sensitivity, the dynamic programming of CLAST also allows both global and local alignments. Global alignment is useful for assigning taxonomic assignment of metagenomic reads because global alignments can evaluates similarity of large regions of the query and reference genome sequences (Figure 8). Our results demonstrate that CLAST (global mode) produces highly accurate taxonomic assignments, similar to several other global alignment tools. CLAST (global mode) also achieved relatively high sensitivity, which is higher than those of BWA and Bowtie 2 (global mode) (Figure 8). On the other hand, local alignment is useful for motif searching, which is often used in functional metagenomics , because it can identify partial sequence identity between reads and reference genome sequences. The unique ability of CLAST to perform both local and global alignment greatly enhances its usefulness in metagenomics analyses (see Additional file 8).
Large-scale metagenomic analyses often require use of very large and frequently updated reference databases. CLAST is exceptionally suited for these analyses because it requires minimal database preprocessing for larger genome sequences and does not have a database size limitation. In addition, the maximum memory usage of CLAST is independent of the size of the reference genome sequences in the database.
Although some other alignment tools are able to perform large-scale similarity searches, they tend to require more database preprocessing and memory usage than CLAST. For example, BLAST requires database preprocessing, and the calculation time of this preprocessing is highly related to the size of database. Although BLAT does not require preprocessing, it cannot utilize databases larger than 4 GB . FR-HIT does not require preprocessing, but its memory usage is typically two or three times larger than the size of the database . Burrows–Wheeler transform-based mapping tools usually also require database preprocessing, but because these mapping tools use block sorting, the preprocessing time is generally incidental. For instance, preprocessing of the human genome database (~3 GB) by Burrows–Wheeler transform-based tools usually takes only a few hours ,. However, the microbial genome sequence database now exceeds 5 GB, and the NCBI non-redundant nucleotide sequence database is now >40 GB, and these databases will continue to grow. Given its unique ability to use these extremely large and rapidly growing databases, CLAST shows great promise as an alignment tool for genomic and metagenomic analyses (see Additional file 8).
CLAST requires ~2 GB of main memory and ~2 GB of VRAM, under default settings, and usual metagenomic analyses. More VRAM may be consumed when numerous outputs are produced compared with the input query and reference sequences, such as in 16S rRNA gene amplicon sequencing analyses, but users can manipulate the VRAM usage by specifying specific parameters. The low memory usage of CLAST makes it a reasonable approach for large-scale metagenomic analyses by researchers who do not have access to specialized large-memory computers. This low memory usage is achieved by dividing both the reference genome sequences and query sequences into smaller units that are loaded stepwise to the main memory. Although CLAST depends on a q-gram index of reference sequences, creation of the read-only q-gram index is also performed by the GPUs and therefore does not add substantially to the running time. This feature is one of the most important and innovative advances of CLAST. In contrast, BLAT and FR-HIT load all reference genome sequence data to the main memory at the same time, thus requiring a larger main memory for larger databases. Indeed, FR-HIT used >13 GB of memory in our simulated metagenomic analysis test.
To further take advantage of parallel-computation power, CLAST can be run by multiple GPUs, dramatically accelerating the homology search. This feature, combined with its low memory usage, makes CLAST appropriate for GPU clusters and supercomputers, which are often equipped with nodes having more than one GPU and less than 100 GB of memory. The source code of the CLAST tool is attached to this paper (Additional file 9).
The novel high-speed and sensitive sequence similarity search tool CLAST was designed and validated for metagenomic analysis applications. CLAST was capable of identifying sequence similarities ~80.8 times faster than BLAST and 9.6 times faster than BLAT owing to a GPU-based parallelization technique using CUDA computing architecture. To improve the sensitivity of similarity searching for taxonomic assignment and motif searching, CLAST supports both global and local alignment. Furthermore, CLAST does not require extensive database preprocessing, and consequently can be run on a standard desktop computer with NVIDIA GPUs. Taken together, our results demonstrate that CLAST run on a GPU-oriented cluster or supercomputer has the potential to be one of the most powerful and realistic approaches to analyze the massive amount of sequence data from next-generation sequencing technologies.
Availability and requirements
Project name: CLAST
Project home page: https://github.com/masayano/CLAST
Operating system(s): Platform independent
Programming language: CUDA
Other requirements: NVIDIA Fermi architecture GPU, CUDA 4.0
License: GNU GPL
Any restrictions to use by non-academics: None
This study was supported through a Grant-in-Aid for Scientific Research on Innovative Areas “Genome Science” from the Ministry of Education, Culture, Sports, Science and Technology of Japan, and a grant from the Database Integration Coordination Program of the JST National Bioscience Database Center, and a grant from the “Advanced Core Technologies for Big Data Integration” of the JST Strategic Basic Research Program (CREST).
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