Figures
Abstract
The presence of duplicates introduced by PCR amplification is a major issue in paired short reads from next-generation sequencing platforms. These duplicates might have a serious impact on research applications, such as scaffolding in whole-genome sequencing and discovering large-scale genome variations, and are usually removed. We present FastUniq as a fast de novo tool for removal of duplicates in paired short reads. FastUniq identifies duplicates by comparing sequences between read pairs and does not require complete genome sequences as prerequisites. FastUniq is capable of simultaneously handling reads with different lengths and results in highly efficient running time, which increases linearly at an average speed of 87 million reads per 10 minutes. FastUniq is freely available at http://sourceforge.net/projects/fastuniq/.
Citation: Xu H, Luo X, Qian J, Pang X, Song J, Qian G, et al. (2012) FastUniq: A Fast De Novo Duplicates Removal Tool for Paired Short Reads. PLoS ONE 7(12): e52249. https://doi.org/10.1371/journal.pone.0052249
Editor: Daniel Doucet, Natural Resources Canada, Canada
Received: May 13, 2012; Accepted: November 16, 2012; Published: December 20, 2012
Copyright: © 2012 Xu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Key National Natural Science Foundation of China [Grant number 81130069]; the Program for Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China [Grant number IRT1150]; and the National Science Foundation of China [Grant numbers 30901156, 31170619]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Massively parallel sequencing technologies, also called next-generation sequencing (NGS) technologies, provide a major approach to obtaining millions of short reads from DNA/RNA samples. NGS has been used in a wide range of research areas over the past few years such as determining whole-genome sequences for new species [1], [2], addressing evolutionary processes at a genomic scale in natural populations [3], identifying mutant alleles in oncogenes in human cancers [4], and resolving whole-genome transcription profiles [5].
In general, the quality of NGS data is one of the major concerns with final study conclusions. Thus, quality control is generally considered the first step in data analyses and is a mandatory prerequisite to downstream analyses and further studies [6]. The presence of duplicates is a major issue in paired short reads from NGS platforms. Polymerase chain reaction (PCR) amplification is one of the major sources of duplicates, which are usually introduced during sequencing library amplification [7]. These duplicates might have a serious impact on research applications, such as scaffolding in whole-genome sequencing [2] and discovering large-scale genome variations [8], and are usually removed. For example, scaffolding is one of the key steps in whole-genome sequencing, in which paired read mappings are used to estimate the order and intervening distance between initial contiguous sequences (contigs) [9]. Because the number of read pairs spanning contigs plays critical roles in scaffolding results, two types of errors may be introduced by the existence of duplicates: false-positive results, in which contigs are incorrectly connected due to the increased numbers of connections; and false-negative results, in which contigs are incorrectly unconnected due to the increased numbers of conflicting connections.
In recent studies, pipelines using a mapping-based strategy have been used to remove duplicates in paired short reads [2], [10]–[14]. In this process, read pairs are first aligned to reference sequences using short read alignment tools such as Bowtie [15], Crossbow [16], and BWA [17], and those read pairs that are exactly mapped to the same position are considered duplicate candidates. Duplicates of this kind are finally removed using tools such as Rmdup in the SAMtools package [18], MarkDuplicates in the Picard toolkit [19], and SEAL [20].
In many studies, however, the performance of the mapping-based strategy is not always satisfactory. In most cases, mapping-based strategies require completed genome sequences as references, and thus, they are not suitable for the many species without genome sequences available. More importantly, the accuracy of paired reads alignment might be affected both by genomic variations that are widely distributed among individuals such as large scale structural variations [21], copy number variations [22], small insertion/deletion variations [23], and single-nucleotide polymorphisms (SNPs) [3], and by repetitive elements that are interspersed throughout the genome such as Alu elements in primate genomes [24] and Mu transposons in plant genomes [25]. Hence, a mapping-based strategy is not sufficient in many studies of model species and especially in studies focusing on genomic variations and genomes containing large numbers of repeat elements. Thus, a tool is required that implements a de novo strategy to remove duplicates only by making use of clues in paired short reads from NGS platforms, regardless of the availability of completed genome sequences. Recently, a de novo strategy was implemented in several tools such as fastx_collapser in the FASTX-Toolkit [26] and Fulcrum [27] was used successfully to remove duplicates. However, these tools are either not designed for the removal of duplicates in paired short reads, or inefficient with running time ranged from several hours to several days.
To accelerate duplicates removal in paired short reads using a de novo strategy, we developed FastUniq. FastUniq is a fast tool that can handle data at an average speed of 87 million reads per 10 minutes.
Program Design
FastUniq was engineered to accomplish duplicate read-pair removal in a three-step process (Figure 1). First, FastUniq imports all paired reads into memory. Then, FastUniq sorts these read pairs on the basis of their sequences. Finally, FastUniq marks duplicates in sorted read pairs and outputs the unique sequences.
Step 1: import all read pairs into memory; Step 2: sort read pairs based on nucleotide sequences; Step 3: identify duplicates in sorted read pairs and output the unique sequences.
FastUniq accepts a list of FASTQ sequence files as its input file, in which two adjacent files with reads in the same order belong to a pair. FastUniq can simultaneously handle reads with different lengths. FastUniq outputs unique read pairs into two sequence files in either FASTQ or FASTA format, with reads in the same order belonging to a pair. In addition, FastUniq provides an option to output unique read pairs into a single sequence file in FASTA format with adjacent sequences belonging to a pair.
FastUniq was written in C language using standard POSIX libraries and can be run at full speed on most UNIX/Linux-compatible systems.
Implementation
Importing Paired Short Reads
FastUniq imports all read pairs into memory. In this process, a three-tier architecture was built to store hundreds of millions or more of paired reads (Figure 2). In the basic tier, the object named ‘fastq’ is used to store data for one read, including description, sequence, and quality values. The ‘fastq_pair’ middle-tier object is composed of two ‘fastq’ objects to store data for a read pair, and the high-tier object is a list composed of large numbers of ‘fastq_pair’ objects. After all paired reads are correctly imported, the list of ‘fastq_pair’ is indexed for rapid access to any ‘fastq_pair’ objects in the list.
The high-tier objective was to store hundreds of millions or more of paired reads. Data for each read pair composed of two reads are stored in a middle-tier ‘fastq_pair’ object, and data for each read are stored in a basic-tier ‘fastq’ object.
Sorting
FastUniq makes use of the merge sort algorithm [28] to sort all ‘fastq_pair’ objects in the list. The order of ‘fastq_pair’ objects is determined by nucleotide sequences in paired reads. To compare two ‘fastq_pair’ objects, FastUniq first compares sequences of the first reads and then compares sequences of the second reads only if sequences of the first reads were the same. To determine the order of two sequences, FastUniq compares bases at the same position in a one-by-one manner using the sorting rule of ‘A’, ‘C’, ‘G’, and ‘T’ in order; the order of these two sequences is determined when the first different bases are detected. For two sequences with different lengths, the order is determined by the sequence length if the shorter sequence exactly matches to the 5′ end of the longer one.
Removing Duplicates and Outputting the Unique Sequences
FastUniq identifies duplicates in the sorted ‘fastq_pair’ list by comparing the adjacent read pairs in the list. Similarly, duplicates also are identified by sequence comparison. Two reads with different lengths are considered the same if the shorter sequence exactly matches to the 5′ end of the longer one, and two read pairs are identified as duplicate candidates if both reads are considered the same. For two duplicate pairs, FastUniq outputs the one in which the lengths of both reads are longer than or equal to another; otherwise, FastUniq outputs both reads.
Application
We evaluated FastUniq using Illumina sequencing libraries of the Acropora digitifera genome project [2], taken from the DDBJ Read Archive (DRA000447) [29] and including all paired-end libraries corresponding to short insert sizes of 200, 300, 500, and 700 base pair (bp) and all mate-pair libraries corresponding to large insert sizes of 1, 3, 5, 7, 15, and 20 kilobase (kb). Nucleotides were trimmed from the end of reads with the fastq_quality_trimmer in the FASTX-Toolkit [26], with a quality threshold of 20 and a length threshold of 20 bp, in a one-by-one manner. The clean read pairs then were extracted.
Duplicates were identified from these paired reads on a DELL PowerEdge R910 server with 256 gigabytes (GB) RAM. The maximum memory usage is 35.6 GB at the time of removing duplicates for mate-pair library corresponding to a 15-kb insert size (DDBJ:DRX000986), which was composed of 227 million reads or 16.6 billion bases. Figure 3A shows the levels of duplicates identified by FastUniq for each library, in which levels of duplicates are obviously different between paired-end libraries and mate-pair libraries (Table S1). Of these, all paired-end libraries had levels of duplicates lower than 4%, indicating the success of preparation steps for these paired-end libraries. In contrast, all mate-pair libraries had significantly higher levels of duplicates, in which the lowest ones were 25% in both the 1-kb and the 7-kb libraries. In particular, the highest levels exceeded 80% in both the 15-kb and the 20-kb libraries.
(A) The number of read pairs before and after duplicates removal using FastUniq or the mapping-based pipeline for each library. (B) The percentage of duplicates in the results of the mapping-based pipeline identified using FastUniq or fastx_collapser for each library.
Evaluation
Accuracy
The fastx_collapser in the FASTX-Toolkit is a widely accepted de novo tool for removing duplicates from unpaired reads [26]. For libraries composed of paired reads with identical read length, duplicates could also be removed accurately using fastx_collapser through merging reads belonging to a pair into a single sequence.
To evaluate the accuracy of FastUniq, duplicates were identified in the clean read pairs of Acropora digitifera mate-pair library corresponding to a 15-kb insert size with identical read length of 75 bp (DDBJ:DRX000986) using both FastUniq and fastx_collapser. By comparing the results, we found the unique read pairs identified by FastUniq was identical to that identified by fastx_collapser (data not shown). The result indicates that FastUniq has a good accuracy on paired short reads.
Comparison with the Mapping-based Strategy
To verify the conclusions from the application of FastUniq, a mapping-based pipeline consisting of BWA and the Picard toolkit was used to identify duplicates from the same data sets, using the completed genome sequences of Acropora digitifera [2], taken from Okinawa Institute of Science and Technology [30], as its references. The details of the procedures are as follows. First, read pairs for each library were aligned to references using a pipeline composed of the ‘index’, ‘aln’, and ‘sampe’ functions of BWA in sequence to generate an index for reference. Next, the suffix array (SA) coordinates of good hits of each individual read were found. Finally, the SA coordinates of paired reads were converted to chromosomal coordinates. The results were stored in sequence alignment/map (SAM) format [18]. Then, duplicates were identified based on their coordinate position relationships stored in SAM files, using a pipeline consisting of the ‘SortSam’, ‘MarkDuplicates’, and ‘SamToFastq’ functions of the Picard toolkit in the sequence to coordinately sort read pairs. Duplicates then were removed from the coordinated sorted pairs, and the unique pairs were exported in FASTQ format.
Because both the Illumina sequencing libraries and the completed genome were from a single clonal colony of the coral Acropora digitifera species [2], the negative effects of the Picard MarkDuplicates results were reduced to a minimum level. Thus, Picard MarkDuplicates performs well in removing duplicates from these sequencing libraries. Therefore, the fact that the level of duplicates identified by FastUniq for each library was close to or exceeding that identified by Picard MarkDuplicates (Figure 3A, Table S1) indicates that FastUniq performs well on these sequencing libraries.
To further evaluate the effect of FastUniq, we used this software to check the level of duplicates for each library after mapping-based duplicates were removed. We found that duplicates existed in all libraries after mapping-based duplicates were removed and especially in mate-pair libraries corresponding to large insert sizes of 15 kb and 20 kb, in which the level of duplicates remained 60% and 74%, respectively (Figure 3B, Table S2). Meanwhile, we merged reads belonging to a pair into a single sequence for each library after mapping-based duplicates were removed, and then used fastx_collapser in the FASTX-Toolkit to check the level of duplicates with exactly the same sequence in both reads in pairs for each library. The similar trends of duplicate percentages identified by FastUniq and fastx_collapser (Figure 3B, Table S2) confirmed the existence of duplicates in the results of the mapping-based strategy.
Several reasons may contribute to the existence of these duplicates. To determine the major ones, read pairs not mapped to references in the mate-pair library corresponding to a 15-kb insert size (DDBJ:DRX000986) were extracted after mapping-based duplicates were removed, accounting for 56% of the library. The level of duplicates for these unmapped pairs was identified using FastUniq. We found a 79% level of duplicates within these unmapped pairs, accounting for 74% of duplicates in the results of the mapping-based strategy. Thus, it can be concluded that majority of duplicates in the results of the mapping-based strategy was due to the mapping-based strategy lacks the capability to remove duplicates in read pairs not mapped to references.
Running Time
The running time for FastUniq was evaluated by removing duplicates in a series of libraries, with the number of reads gradually increasing from 100 million to one billion. These libraries were simulated by combining multiple copies of the Acropora digitifera mate-pair library corresponding to a 1-kb insert size with read length of 35 bp (DDBJ:DRX000983). The results showed that FastUniq has a highly efficient running time, with the removal of duplicates in a library composed of 100 million reads completed 10 minutes (Figure 4). More importantly, the running time increased linearly with an increasing amount of data, with an average speed of 87 million reads per 10 minutes.
The running time is measured by the ‘time’ command in the Linux operating system.
Discussion
In this paper, we describe a new method that uses a de novo strategy to remove duplicates in paired short reads. In recent studies, duplicate removal was generally achieved through pipelines using a mapping-based strategy. Different from the mapping-based strategy in which duplicate candidates were identified based on coordinate relationships between read pairs, the de novo strategy determines duplicate candidates directly based on sequences in paired short reads. Thus, the advantage of the de novo strategy is that it does not require completed genome sequences as a prerequisite, and it is not influenced by the completion level of genome sequences, widespread genome variation among individuals, or repeat elements in genomes. Therefore, the de novo strategy could provide a universal approach to remove duplicates in paired short reads, with a wide adaptability to nearly all species.
We described the implementation of the de novo strategy in FastUniq, a tool that can be used with flexibility in almost all NGS-based studies. FastUniq is capable of simultaneously handling reads with different lengths, and thus, it provides an opportunity to remove duplicates in multiple sequencing results from one library and to be integrated into the mainstream NGS processing pipelines. FastUniq can output the unique pairs in multiple sequence formats to meet diverse demands in various types of analyses.
An efficient in-memory architecture was used to store read pairs in FasUniq. In practice, a computing server equipped with 64 GB memory is sufficient for FastUniq to handle reads produced from a whole Illumina Hiseq2000 lane. In general, a 64 GB memory is a minimum requirement for subsequent large-scale data analysis using popular tools such as Velvet [31], SOAPdenovo [32], ALLPATHS-LG [33] and so on. In comparison to the running time of mapping-based pipelines and other de novo tools that ranges from several hours to several weeks in our practice, FastUniq is a fast tool that removes duplicates at an average speed of 87 million reads per 10 minutes (Figure 4).
The results from the evaluation demonstrated that FastUniq identified percentages of duplicates close to or exceeding that identified by Picard MarkDuplicates in libraries corresponding to multiple insert sizes from 200 bp to 20 kb (Figure 3A, Table S1). Picard MarkDuplicates performs among the best in this process because of the use of completed genome sequences and minimized genome variations. Theoretically, however, the performance of Picard MarkDuplicates may be significantly reduced in many studies. Because FastUniq only examines bases in paired reads, it can be inferred that FastUniq will show a better performance than Picard MarkDuplicates.
There were some differences in levels of duplicates identified by FastUniq and Picard Markduplicates that were caused by the different criteria in read pair comparisons (Figure 3A, Table 1). Of them, FastUniq compares read pairs on the basis of sequences only, and it is sensitive to SNPs caused by heterozygous or sequencing errors. Picard MarkDuplicates compares read pairs on the basis of coordinate relationships, but in practice, it is not sensitive to a few SNPs between read pairs. In addition, FastUniq identified up to 74% of the levels of duplicates in mate-pair library corresponding to a 20 kb insert size in the Picard MarkDuplicates results (Figure 3B, Table S2), a result that is mainly caused by the inability of the mapping-based strategy to identify duplicates in read pairs that are not mapped to references. Therefore, we conclude that FastUniq is an unbiased tool for removal of duplicates in all input read pairs that maximally retain polymorphisms in the sequencing data.
Availability
FastUniq is open source software that is freely available at http://sourceforge.net/projects/fastuniq/.
Supporting Information
Table S1.
The number and percentage of unique read pairs after duplicates removal using FastUniq or the mapping-based pipeline for each library.
https://doi.org/10.1371/journal.pone.0052249.s001
(DOC)
Table S2.
The number and percentage of duplicates in the results of the mapping-based pipeline identified using FastUniq or fastx_collapser for each library.
https://doi.org/10.1371/journal.pone.0052249.s002
(DOC)
Author Contributions
Conceived and designed the experiments: HBX JHC. Performed the experiments: HBX JHC. Analyzed the data: HBX XL JQ XHP. Contributed reagents/materials/analysis tools: GRQ. Wrote the paper: HBX XL JQ XHP JYS GRQ JHC SLC.
References
- 1. Li R, Fan W, Tian G, Zhu H, He L, et al. (2010) The sequence and de novo assembly of the giant panda genome. Nature 463: 311–317.
- 2. Shinzato C, Shoguchi E, Kawashima T, Hamada M, Hisata K, et al. (2011) Using the Acropora digitifera genome to understand coral responses to environmental change. Nature 476: 320–323.
- 3. Hohenlohe PA, Bassham S, Etter PD, Stiffler N, Johnson EA, et al. (2010) Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet 6: e1000862.
- 4. Thomas RK, Baker AC, Debiasi RM, Winckler W, Laframboise T, et al. (2007) High-throughput oncogene mutation profiling in human cancer. Nat Genet 39: 347–351.
- 5. Lu T, Lu G, Fan D, Zhu C, Li W, et al. (2010) Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. Genome Res 20: 1238–1249.
- 6.
Turner S, Armstrong LL, Bradford Y, Carlson CS, Crawford DC, et al.. (2011) Quality control procedures for genome-wide association studies. Curr Protoc Hum Genet Chapter 1: Unit1 19.
- 7. Kozarewa I, Ning Z, Quail MA, Sanders MJ, Berriman M, et al. (2009) Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+C)-biased genomes. Nat Methods 6: 291–295.
- 8. Handsaker RE, Korn JM, Nemesh J, McCarroll SA (2011) Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nat Genet 43: 269–276.
- 9. Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W (2011) Scaffolding pre-assembled contigs using SSPACE. Bioinformatics 27: 578–579.
- 10. Bowne SJ, Humphries MM, Sullivan LS, Kenna PF, Tam LC, et al. (2011) A dominant mutation in RPE65 identified by whole-exome sequencing causes retinitis pigmentosa with choroidal involvement. Eur J Hum Genet 19: 1074–1081.
- 11.
Bowen ME, Henke K, Siegfried KR, Warman ML, Harris MP (2011) Efficient mapping and cloning of mutations in zebrafish by low coverage whole genome sequencing. Genetics.
- 12.
Van Nieuwerburgh F, Thompson RC, Ledesma J, Deforce D, Gaasterland T, et al.. (2011) Illumina mate-paired DNA sequencing-library preparation using Cre-Lox recombination. Nucleic Acids Res.
- 13. Clark MJ, Chen R, Lam HY, Karczewski KJ, Euskirchen G, et al. (2011) Performance comparison of exome DNA sequencing technologies. Nat Biotechnol 29: 908–914.
- 14. Skelly DA, Johansson M, Madeoy J, Wakefield J, Akey JM (2011) A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. Genome Res 21: 1728–1737.
- 15. Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10: R25.
- 16. Langmead B, Schatz MC, Lin J, Pop M, Salzberg SL (2009) Searching for SNPs with cloud computing. Genome Biol 10: R134.
- 17. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760.
- 18. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079.
- 19.
The Picard toolkit. Available: http://picard.sourceforge.net/. Accessed 2012 Nov 19.
- 20. Pireddu L, Leo S, Zanetti G (2011) SEAL: a distributed short read mapping and duplicate removal tool. Bioinformatics 27: 2159–2160.
- 21. Korbel JO, Urban AE, Affourtit JP, Godwin B, Grubert F, et al. (2007) Paired-end mapping reveals extensive structural variation in the human genome. Science 318: 420–426.
- 22. Park H, Kim JI, Ju YS, Gokcumen O, Mills RE, et al. (2010) Discovery of common Asian copy number variants using integrated high-resolution array CGH and massively parallel DNA sequencing. Nat Genet 42: 400–405.
- 23. Mills RE, Luttig CT, Larkins CE, Beauchamp A, Tsui C, et al. (2006) An initial map of insertion and deletion (INDEL) variation in the human genome. Genome Res 16: 1182–1190.
- 24. Liu GE, Alkan C, Jiang L, Zhao S, Eichler EE (2009) Comparative analysis of Alu repeats in primate genomes. Genome Res 19: 876–885.
- 25. Liu S, Yeh CT, Ji T, Ying K, Wu H, et al. (2009) Mu transposon insertion sites and meiotic recombination events co-localize with epigenetic marks for open chromatin across the maize genome. PLoS Genet 5: e1000733.
- 26. Pearson WR, Wood T, Zhang Z, Miller W (1997) Comparison of DNA sequences with protein sequences. Genomics 46: 24–36.
- 27. Burriesci MS, Lehnert EM, Pringle JR (2012) Fulcrum: condensing redundant reads from high-throughput sequencing studies. Bioinformatics 28: 1324–1327.
- 28.
Merge sort algorithm. Available: http://en.wikipedia.org/wiki/Merge_sort. Accessed 2012 Nov 19.
- 29.
The DDBJ sequence read archive database. Available : http://trace.ddbj.nig.ac.jp/. Accessed 2012 Nov 19.
- 30.
The Acropora digitifera genome database. Available: http://marinegenomics.oist.jp/acropora_digitifera/. Accessed 2012 Nov 19.
- 31. Zerbino DR, Birney E (2008) Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Research 18: 821–829.
- 32. Li R, Zhu H, Ruan J, Qian W, Fang X, et al. (2010) De novo assembly of human genomes with massively parallel short read sequencing. Genome Res 20: 265–272.
- 33. Gnerre S, Maccallum I, Przybylski D, Ribeiro FJ, Burton JN, et al. (2011) High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc Natl Acad Sci U S A 108: 1513–1518.