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LW-FQZip 2: a parallelized reference-based compression of FASTQ files



The rapid progress of high-throughput DNA sequencing techniques has dramatically reduced the costs of whole genome sequencing, which leads to revolutionary advances in gene industry. The explosively increasing volume of raw data outpaces the decreasing disk cost and the storage of huge sequencing data has become a bottleneck of downstream analyses. Data compression is considered as a solution to reduce the dependency on storage. Efficient sequencing data compression methods are highly demanded.


In this article, we present a lossless reference-based compression method namely LW-FQZip 2 targeted at FASTQ files. LW-FQZip 2 is improved from LW-FQZip 1 by introducing more efficient coding scheme and parallelism. Particularly, LW-FQZip 2 is equipped with a light-weight mapping model, bitwise prediction by partial matching model, arithmetic coding, and multi-threading parallelism. LW-FQZip 2 is evaluated on both short-read and long-read data generated from various sequencing platforms. The experimental results show that LW-FQZip 2 is able to obtain promising compression ratios at reasonable time and memory space costs.


The competence enables LW-FQZip 2 to serve as a candidate tool for archival or space-sensitive applications of high-throughput DNA sequencing data. LW-FQZip 2 is freely available at and


The rapid progress of high-throughput DNA sequencing techniques has dramatically reduced the costs of whole genome sequencing, which leads to revolutionary advances in gene industry [1, 2]. Genome studies have produced tremendous volume of data that poses great challenges to storage and transfer. Data compression becomes necessary as a silver-bullet solution to ease the dilemma [36]. Nevertheless, the popular generic compression tools, such as gzip ( and bzip2 (, cannot obtain satisfactory performance on high-throughput DNA sequencing data, because they do not utilize the biological characteristics of the data like repeat fragments and palindromes. Efficient compression methods oriented to high-throughput DNA sequencing data are highly demanded.

Many specialized compression methods have been proposed for sequencing data in raw FASTQ format [711], sequencing reads (DNA nucleotides only) [1215] and/or aligned SAM/BAM format [16]. Depending on whether extra reference genomes are required, these compression methods normally can be classified into reference-based and reference-free methods.

Reference-based methods align the targeted sequences to some external reference sequence(s) for identifying the similarity between them. The variances of the alignment are encoded instead of the original targeted sequences. Reference-based methods generally obtain better compression ratios with more time consumption by involving a sequence alignment pre-processing. Representative state-of-the-art reference-based methods include CRAM [17], Quip [11], and DeeZ [18]. CRAM [17], working with BAM-based input, records the variances of reads and a reference genome with Huffman coding. Quip [11] with ‘-r’ option compresses SAM/BAM data in a standard reference-based scheme and employs highly optimized statistical models for various SAM fields, thus leading to compromising compression rate. It is also applicable to reference-based FASTQ compression in ‘-a’ mode where a de novo assembly procedure is introduced to construct references in the target data rather than using existing references. DeeZ [18] lowers the cost of representing common differences among the reads’ mapping results by implicitly assembling the underlying donor genome in order to encode these variants only once.

Reference-free methods compress the raw sequencing data, mainly in FASTQ format, directly based on their natural statistics. For example, FQZcomp [8] uses an order-k context model to predict the nucleotide sequences in FASTQ format followed by arithmetic coding based compression. DSRC [10] partitions input FASTQ data into blocks enabling independent compression of them using LZ77 and Huffman encoding schemes. DSRC 2 [19] is an improvement of DSRC by introducing threaded parallelism and more efficient coding scheme. LFQC [20] preforms data transformation to the four fields of the sequencing records in an FASTQ file separately, followed by regular data compressor namely zpaq and lpaq8. LEON [21] first builds a reference as a probabilistic de Bruijn graph based on bloom filter, and then records the reads and quality scores as mapped paths in the graph using arithmetic encoding. SCALCE [22] reorganizes reads in an FASTQ file that share common ‘core’ substrings into groups, and then compacts the groups using gzip or LZ77-like tools. SeqDB [23] coordinates sequences bases and their corresponding quality scores into 2D byte arrays and compresses them with an existing multithreaded compressor Blosc. Quip [11], in addition to the reference-based compression mode, also provides reference-free compression using arithmetic coding based on high order Markov chains. Instead of exploiting the redundancy of homologous sequences, reference-free methods put more effort into predictive model and coding scheme, which tends to improve the time efficiency by sacrificing compression ratio to some degree [24].

This work focus on long-term archiving and space-sensitive scenarios, where superior compression ratio is pursued and reference-based methods are more favourable. In our previous work [25], we have proposed a self-contained reference-based method, namely LW-FQZip 1, to compress high-throughput DNA sequencing data in raw FASTQ format. LW-FQZip 1 introduces a light-weight mapping model to efficiently align short reads against the reference sequence based on a k-mer indexing strategy. The light-weight mapping model distinguishes LW-FQZip 1 from other reference-based methods for not relying on any external alignment software. Nevertheless, LW-FQZip 1 is far from satisfactory in terms of compression efficiency.

LW-FQZip 2 is an improved version of LW-FQZip 1 by introducing parallelism and more efficient coding schemes. Especially, LW-FQZip 2 is equipped with light-weight mapping model, bitwise prediction by partial matching (PPM), arithmetic coding, and multi-threading parallelism. It can support various FASTQ files generated from the most well-known high-throughput sequencing platforms and obtain superior compression ratios at reasonable time and memory space costs.


The general framework of LW-FQZip 2 is shown in Fig. 1. Firstly, LW-FQZip 2 splits an input FASTQ file into three data streams (i.e., metadata, nucleotide sequences, and quality scores) and then the nucleotide sequences (also known as reads) are divided into equal-sized sub-blocks which are simultaneously fed to the light-weight mapping model implemented with multi-threading. After the sequence mapping, the matching results (i.e., the mapped position, palindrome flag, match length, and match type) and mismatch values are recorded in different intermediate files. Secondly, the metadata and quality scores are simultaneously proceeded by abridging the consecutive repeats with incremental encoding and run-length-limited encoding, respectively. Finally, the intermediate files generated from the metadata, nucleotide sequences, and quality scores streams are compacted with a combination of bitwise order-32 PPM model and arithmetic coder in parallel, except that the intermediate file storing mismatch values is compressed by a distinct bitwise order-28 arithmetic coder. In best compression ratio mode, i.e., LW-FQZip 2 with a ‘–g’ option selected, the quality scores and mismatch values are compacted using the zpaq tool ( and the other intermediate files encoded with lpaq9m ( LW-FQZip 2 is available at and The pseudo-code of LW-FQZip 2 is provided in Algorithm 1, Additional file 1. The key components of LW-FQZip 2 are described as follows.

Fig. 1

The general framework of LW-FQZip 2. Firstly, the input FASTQ file is split into three data streams of metadata, bases, and quality scores. Secondly, the quality scores and metadata are compacted with run-length-limited encoding and incremental encoding, respectively. The nucleotide bases are partitioned and mapped to an external reference sequence based on the light-weight mapping model. Finally, the processed intermediate files from the three streams are compressed with arithmetic coder and/or other specific coding schemes

Compression of metadata and quality scores

In LW-FQZip 2, the metadata are pre-processed with incremental encoding, with which the variances of one metadata to its previous neighbour is stored rather than the original data. The quality scores are pre-processed with run-length-limited encoding. More details of the incremental encoding and run-length-limited encoding are available in [25].

After pre-processing, the processed data can be compressed with lpaq9m (if ‘-g’ option is selected) or a combination of PPM model and arithmetic coder. In the latter case, a binary tree is established to store the predictive context information. The pre-processed metadata and quality scores are transformed into binary streams to train an order-32 PPM model, i.e., prediction based on a 4-byte context (higher order can improve the compression ratios by promoting the predictive accuracy but it consumes more memory space and running time. A trade-off order 32 is adopted in this study). The binary quality scores are matched against the predicted results per bit by producing ‘0’ or ‘1’, and then the context prediction model is updated accordingly. Finally, the prediction results are recorded using arithmetic coder or zpaq (if ‘-g’ option is selected). The pseudo-code of the compression with PPM prediction model and arithmetic coding/zpaq is provided in Algorithm 2, Additional file 1.

Reference-based compression of nucleotide sequences using light-weight mapping model

The target nucleotide sequences are mapped to an external reference sequence based on the light-weight mapping model [25] and the mapping results are recorded instead of the original sequences.

To make this article self-contained, the light-weight mapping model is briefly introduced in this subsection. The mapping model is designed to implement fast alignment by indexing the k-mer substrings within the reference. A hash table I R is firstly established to save all positions of k-mer substrings in the reference with some predefined prefix, e.g., ‘CG’. On mapping a read of nucleotide bases X to the reference, the model identifies all k-mer substring included in I R and selects the valuable k-mer substrings served as seeds, where some restrictions are predefined to eliminate the low-quality k-mer substrings (e.g., minimum seed length L, mismatch tolerance rate e). Based on the selected seeds, multiple local alignments are performed to identify the maximum matches. The mapping results of X including the mapped position, palindrome flag, match length, match type and mismatch values are recorded in some intermediate files. If no k-mer substring in X is identified in I R , the palindrome of X undergoes the same mapping procedure. In the case neither X nor its palindrome is mapped to the reference, the plain X is output for encoding directly.

To improve the matching rate, the unmapped parts of reads are further partitioned into shorter segments and realigned against the reference where palindrome match is considered. The mismatched nucleotide bases are composed exclusively of four characters (i.e., {‘A’, ‘C’, ‘G’, ‘T’}), which are much easier to encode than quality scores. Therefore, a simpler yet efficient model based on the bitwise order-28 arithmetic coder ( or zpaq is adopted. If no proper reference is available, LW-FQZip 2 also provides an option ‘-a’ to generate a reference by assembling a portion of reads that contain the predefined prefix.

Blocking and multithreading

Parallelism is introduced to LW-FQZip 2 to improve the computational efficiency using the Pthreads library. In the mapping procedure, the input FASTQ file is partitioned into b (empirically set to 10 in this study) equal-sized blocks. Accordingly, b threads are simultaneously executed with each running a light-weight mapping model for a corresponding block. Afterward, a new single thread is created to collect the mapping results of the previous b threads and dispatch the results into different intermediate files. After the three data streams, i.e., metadata, quality scores, and nucleotide sequences are properly processed, multiple threads are created to compress the six intermediate files with the corresponding encoding schemes as shown in Fig. 1.

In summary, LW-FQZip 2 improves LW-FQZip 1 by introducing multi-threading for the time-consuming read mapping and using more efficient encoding schemes based on PPM model, arithmetic coders, lpaq9m and/or zpaq. The implementation details of LW-FQZip 2 are provided in the Additional file 1.

Results and discussion

LW-FQZip 2 is verified using ten representative real-world FASTQ files (five short-read data and five long-read data) on a platform running 64-bit Red Hat 4.4.7-16 with four 8-core Intel(R) Xeon(R) E7-8837 CPUs (@2.67GHz with Hyper-Threading Technology). These data sets, generated from various well-known high-throughput DNA sequencing platforms, were downloaded from the Sequence Read Archive of the National Centre for Biotechnology Information (NCBI) [26]. Details of these data sets are provided in Table 1.

Table 1 The ten real-world FASTQ data sets used for performance evaluation

LW-FQZip 2 is compared with the other state-of-the-art lossless FASTQ compressors namely Quip [11], DSRC 2 [19], FQZcomp [8], CRAM [17], LFQC [20], LEON [21] and SCALCE [22]. The general-purpose compression tools, i.e., gzip and bzip2, as well as the original LW-FQZip 1 are also included in the comparison as baselines. All compared methods are configured to obtain the best compression ratio (the parameter settings of all methods and the software version information are provided in the Additional file 1). LW-FQZip 2 is evaluated on two modes, i.e., the normal mode (‘LW-FQZip 2’, using arithmetic coders to compress the intermediate files) and the best compression ratio mode (‘LW-FQZip 2 (−g)’, using lpaq9m and zpaq to compress the intermediate files). Quip is also executed in two modes, i.e., the reference-based compression (‘quip -r’) and the assembly-based compression (‘quip -a’, a reference is assembled with a portion of reads, and then a reference-based compression is conducted using the generated reference). Quip, CRAM, and LW-FQZip 1 fall within reference-based scheme. The other methods are reference-free methods.

The performance of the methods is evaluated in terms of compression ratio, speed, and memory consumption. The compression ratios of all compared methods on the ten FASTQ data sets are tabulated in Table 2. The average compression and decompression speeds of all methods are plotted in Fig. 2. The memory sizes consumed by the compared methods on each data set are reported in Table 3. More details of the data sets and experimental studies are provided in Additional file 1: Tables S1-S13. The average CPU utilization and version information of all compared methods are provided in Additional file 1: Tables S14 and S17, respectively.

Table 2 The compression ratios of the compared methods on ten test data sets
Fig. 2

The average compression and decompression speeds of the compared methods on ten test data sets. The compression speed is calculated as the original file size divided by the compression time. The decompression speed is calculated as the original file size divided by the decompression time

Table 3 The memory usage (MB) of the compared methods on ten test data sets

From Table 2, it is shown that LW-FQZip 2 and LW-FQZip 2 (−g) successfully work on all test data sets. LW-FQZip 2 (−g) tends to obtain superior compression ratios to the other methods especially on long-read data. DSRC 2, Quip, FQZcomp, LEON, SCALCE and LW-FQZip 1 suffer from some issues like incompatibility and fidelity-loss on the long-read data generated from Pacbio RS and MinION platforms. LW-FQZip 1, CRAM, FQZcomp and LFQC also fail on some short-read data sets of large size. LFQC obtains comparable compression ratios to LW-FQZip 2 and LW-FQZip 2 (−g) in the data sets it works out. We made an extra comparison analysis between LW-FQZip 2, LW-FQZip (−g) and LFQC in terms of compression ratio, memory usage, and time consumption in a radar chart in Fig. 3. The results show that LW-FQZip (−g) and LFQC attain slightly better compression ratios than LW-FQZip 2 at the cost of memory usage and time consumption, respectively. Nevertheless, LW-FQZip 2 achieves better compromise over all metrics than the other two methods.

Fig. 3

Comparison between LW-FQZip 2, LW-FQZip 2 (−g) and LFQC in a radar chart in terms of average compression ratio, compression time, decompression time, compression memory usage, and decompression usage. In each criterion, the results of the three methods are normalized to the range of [0, 1] and a smaller value, i.e., closer to the centroid, indicates a better performance

In terms of compression and decompression speeds, LW-FQZip 2 outperforms other reference-based methods as shown in Fig. 2. It is worth highlighting that LW-FQZip 2 outperforms LW-FQZip 1 in terms of compatibility, compression ratio, and speed, which suggests a substantial improvement of LW-FQZip 2 to LW-FQZip 1. As expected, reference-free methods tend to be faster than reference-based methods. Among the reference-free methods, DSRC 2 compresses the fastest with compromising compression ratio by taking full advantage of multithreading. Quip and LEON manage to obtain some trade-offs between the three evaluation criteria. SCALCE is a very efficient tool by introducing a boosting scheme based on locally consistent parsing (LCP) technique to sort the reads, which enables SCALCE to compress similar reads together and attain competitive compression ratios at high speed.

We also try to adopt the LCP technique in our method as the framework shown in Additional file 1: Figure S2. In this attempt, the successfully mapped reads are still compressed with the original LW-FQZip 2, whereas the unmapped reads undergo the LCP boosting and gzip compression. LCP is applied to only a small portion of the reads, yet the results shown in Additional file 1: Table S21, suggest that LCP can improve the compression ratio. Indeed, re-sorting the reads according to their similarity is really an efficient option to improve the compression ratio, especially for archiving-oriented applications. However, since the order of the reads is changed, it inevitably imposes extra cost if random access of the archive is the concern in the downstream analysis. LW-FQZip 2 is designed to preserve the original read order to facilitate the implementation of random access in the future extension of this tool.

The compared methods are also tested on the benchmark data sets suggested by the MPEG working group on genomic compression ( The results are presented in Additional file 1: Tables S18-S20, where the proposed method shows consistent efficiency.

The experimental results suggest that all specialized methods outperform the general-purpose tools in terms of compression ratio but use more memory space. The reference-based methods tend to be slower than the reference-free methods, due to the extra running time involved in the sequence alignment preprocessing. Different methods are designed with different strengths and can be used for different purposes. There is no single method dominates other methods in all criteria.

The effect of palindrome handling is investigated in the Additional file 1: Tables S15 and S16. With palindrome handling more reads can be mapped to the reference at a higher speed (the mapping is stopped once the first match is identified), thus the compression ratio and speed are improved slightly, while the extra memory consumption is eligible.

The effect of thread number is studied in Additional file 1: Figure S1. The compression speed is affected not only by the number of threads but also the disk I/O speed. As a result, the compression might not be speeded up proportionally as the number of threads increases (as shown in Additional file 1: Figure S1). Using faster disk system like solid state disk (SSD) can help to speed up both compression and decompression (see in the Additional file 1: Table S22).


This article presents a specialized compression tool LW-FQZip 2 for FASTQ files. LW-FQZip 2 shows superior compression ratios and compatibility with reasonable (de) compression speed and memory space consumption. It could serve as a candidate tool for archival or storage space-sensitive applications of sequencing data.

The emerging long-read technologies, e.g., Single Molecule Real Time (SMRT) sequencing [27] and Nanopore sequencing [28], produce much longer DNA sequences, reportedly providing a more complete picture of genome structure. They are deemed to be a complementary solution to overcome the shortages of short-read sequencing. The exponentially increasing long-read sequencing data poses new great challenges to the existing specialized compression methods. LW-FQZip 2 shows good compatibility to long-read sequencing data. The current work is hoped to provide insights into the storage problems of new sequencing data. More efficient alignment models for long-read data will be developed in the future work.



Prediction by partial matching


Locally consistent parsing


National Centre for Biotechnology Information


Single molecule real time


Solid state disk


  1. 1.

    van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet. 2014;30(9):418–26.

    Article  PubMed  Google Scholar 

  2. 2.

    Kozanitis C, Heiberg A, Varghese G, Bafna V. Using genome query language to uncover genetic variation. Bioinformatics. 2014;30(1):1–8.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Zhu Z, Zhang Y, Ji Z, He S, Yang X. High-throughput DNA sequence data compression. Brief Bioinform. 2015;16(1):1–15.

    Article  PubMed  Google Scholar 

  4. 4.

    Numanagic I, Bonfield JK, Hach F. Comparison of high-throughput sequencing data compression tools. Nat Methods. 2016;13(12):1005–8.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Hosseini M, Pratas D, Pinho AJ. A survey on data compression methods for biological sequences. Information. 2016;7(4):56.

    Article  Google Scholar 

  6. 6.

    Zhu Z, Li L, Zhang Y, Yang Y, Yang X. CompMap: a reference-based compression program to speed up read mapping to related reference sequences. Bioinformatics. 2015;31(3):426–8.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Zhang, Y, Patel K, Endrawis T, Bowers A, Sun Y. A FASTQ compressor based on integer-mapped k-mer indexing for biologist. Gene. 2016;579(1):75-81.

  8. 8.

    Bonfield JK, Mahoney MV. Compression of FASTQ and SAM format sequencing data. PLoS ONE. 2013;8(3):e59190.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Tembe W, Lowey J, Suh E. G-SQZ: compact encoding of genomic sequence and quality data. Bioinformatics. 2010;26(17):2192–4.

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Deorowicz S, Grabowski S. Compression of DNA sequence reads in FASTQ format. Bioinformatics. 2011;27(6):860–2.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Jones DC, Ruzzo WL, Peng X, Katze MG. Compression of next-generation sequencing reads aided by highly efficient de novo assembly. Nucleic Acids Res. 2012;40(22):e171.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Grabowski S, Deorowicz S, Roguski L. Disk-based compression of data from genome sequencing. Bioinformatics. 2015;31(9):1389–95.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Janin L, Schulz-Trieglaff O, Cox AJ. BEETL-fastq: a searchable compressed archive for DNA reads. Bioinformatics. 2014;30(19):2796–801.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Patro R, Kingsford C. Data-dependent bucketing improves reference-free compression of sequencing reads. Bioinformatics. 2015;31(17):2770–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Rozov R, Shamir R, Halperin E. Fast lossless compression via cascading Bloom filters. BMC Bioinformatics. 2014;15 Suppl 9:S7.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Fritz MH-Y, Leinonen R, Cochrane G, Birney E. Efficient storage of high throughput DNA sequencing data using reference-based compression. Genome Res. 2011;21(5):734–40.

    CAS  Article  Google Scholar 

  18. 18.

    Hach F, Numanagic I, Sahinalp SC. DeeZ: reference-based compression by local assembly. Nat Methods. 2014;11(11):1082–4.

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Roguski L, Deorowicz S. DSRC 2--Industry-oriented compression of FASTQ files. Bioinformatics. 2014;30(15):2213–5.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Nicolae M, Pathak S, Rajasekaran S. LFQC: a lossless compression algorithm for FASTQ files. Bioinformatics. 2015;31(20):3276–81.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Benoit G, Lemaitre C, Lavenier D, Drezen E, Dayris T, Uricaru R, Rizk G. Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph. BMC Bioinformatics. 2015;16:288.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Hach F, Numanagic I, Alkan C, Sahinalp SC. SCALCE: boosting sequence compression algorithms using locally consistent encoding. Bioinformatics. 2012;28(23):3051–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Howison M. High-throughput compression of FASTQ data with SeqDB. IEEE/ACM Trans Comput Biol Bioinform. 2013;10(1):213–8.

    Article  PubMed  Google Scholar 

  24. 24.

    Kahn SD. On the future of genomic data. Science. 2011;331(6018):728–9.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Zhang Y, Li L, Yang Y, Yang X, He S, Zhu Z. Light-weight reference-based compression of FASTQ data. BMC Bioinformatics. 2015;16:188.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. 2010;39(suppl_1):D19-D21.

  27. 27.

    Flusberg BA, Webster DR, Lee JH, Travers KJ, Olivares EC, Clark TA, Korlach J, Turner SW. Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat Methods. 2010;7(6):461–5.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Branton D, Deamer DW, Marziali A, Bayley H, Benner SA, Butler T, Di Ventra M, Garaj S, Hibbs A, Huang X. The potential and challenges of nanopore sequencing. Nat Biotechnol. 2008;26(10):1146–53.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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We would like to thank the Editor and the reviewers for their precious comments on this work which helped improve the quality of this paper.


This work was supported in part by the National Natural Science Foundation of China (61471246, 61575125, and 61572328), Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions (Yq2013141 and Yq2015141), Guangdong Special Support Program of Top-notch Young Professionals (2014TQ01X273 and 2015TQ01R453), Guangdong Province Ordinary University Characteristic Innovation Project (2015KTSCX122), Shenzhen Scientific Research and Development Funding Program (ZYC201105170243A, JCYJ20150324141711587, CXZZ20140902102350474, JCYJ20150630105452814, CXZZ20140902160818443, CXZZ20150813151056544, and JCYJ20160331114551175), and China-UK Visual Information Processing Lab Foundation. The funding body played no role in the design or conclusions of the study.

Availability of data and materials

Project name: LW-FQZip 2

Project website: and

Operating systems: Linux or MacOS

Programming language: C/C++

Other requirements: GCC compiler and the archiving tool ‘tar’

License: The MIT License

Any restrictions to use by non-academics: For commercial use, please contact the authors. All datasets are downloaded from SRA of NCBI. All data supporting the conclusions of this article are included within the article and its additional files.

Author’s contributions

ZH & ZZ conceived the algorithm, developed the program, and wrote the manuscript. ZW and YS helped with manuscript editing, designed and performed experiments. QD&YC prepared the data sets, carried out analyses and helped with program design. All authors read and approved the final manuscript.

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Additional file

Additional file 1:

This document provides the implementation details of LW-FQZip 2 and the detailed experimental results of each compared algorithm in the related comparison study. Algorithm 1. The main procedure of LW-FQZip 2. Algorithm 2. Compression with PPM prediction model and arithmetic coding. Table S1. The performance of LW-FQZip 2. Table S2. The performance of LW-FQZip 2 (−g). Table S3. The performance of LW-FQZip 1. Table S4. The performance of Quip (−a). Table S5. The performance of Quip (−r). Table S6. The performance of DSRC 2. Table S7. The performance of CRAM. Table S8. The performance of FQZcomp. Table S9. The performance of LFQC. Table S10. The performance of LEON. Table S11. The performance of SCALCE. Table S12. The performance of bzip 2. Table S13. The performance of gzip. Table S14. The average number of CPU cores used by the compared methods. Table S15. The compression ratios and time consumptions of LW-FQZip 2 w/o complementary palindrome mapping. Table S16. The memory usage of the LW-FQZip 2 w/o complementary palindrome mapping. Table S17. The version information of all compared methods. Table S18. The compression ratios of the compared methods on benchmark data provided by MPEG working group on genomic compression. Table S19. The performance of LW-FQZip 2 on benchmark data provided by MPEG working group on genomic compression. Table S20. The performance of LW-FQZip 2 (−g) on benchmark data provided by MPEG working group on genomic compression. Table S21. The compression ratios of LW-FQZip2 + LCP, LW-FQZip2 -g + LCP, SCALCE, and LW-FQZip 2 on seven representative data sets. Table S22. The comparison of compression speed of LW-FQZip 2 using SSD and HDD disk systems. Figure S1. The compression speeds of LW-FQZip 2 using different number of threads on five representative data sets. Figure S2. The framework of LW-FQZip 2 with LCP technique. (DOCX 229 kb)

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Huang, Z., Wen, Z., Deng, Q. et al. LW-FQZip 2: a parallelized reference-based compression of FASTQ files. BMC Bioinformatics 18, 179 (2017).

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  • High-throughput sequencing
  • Sequencing data compression
  • Reference- based compression
  • Sequence alignment