AQUa: an adaptive framework for compression of sequencing quality scores with random access functionality

PQSDC: a parallel lossless compressor for quality scores data via sequences partition and run-length prediction mapping

MOTIVATION

The quality scores data (QSD) account for 70% in compressed FastQ files obtained from the short and long reads sequencing technologies. Designing effective compressors for QSD that counterbalance compression ratio, time cost, and memory consumption is essential in scenarios such as large-scale genomics data sharing and long-term data backup. This study presents a novel parallel lossless QSD-dedicated compression algorithm named PQSDC, which fulfills the above requirements well. PQSDC is based on two core components: a parallel sequences-partition model designed to reduce peak memory consumption and time cost during compression and decompression processes, as well as a parallel four-level run-length prediction mapping model to enhance compression ratio. Besides, the PQSDC algorithm is also designed to be highly concurrent using multicore CPU clusters.

RESULTS

We evaluate PQSDC and four state-of-the-art compression algorithms on 27 real-world datasets, including 61.857 billion QSD characters and 632.908 million QSD sequences. (1) For short reads, compared to baselines, the maximum improvement of PQSDC reaches 7.06% in average compression ratio, and 8.01% in weighted average compression ratio. During compression and decompression, the maximum total time savings of PQSDC are 79.96% and 84.56%, respectively; the maximum average memory savings are 68.34% and 77.63%, respectively. (2) For long reads, the maximum improvement of PQSDC reaches 12.51% and 13.42% in average and weighted average compression ratio, respectively. The maximum total time savings during compression and decompression are 53.51% and 72.53%, respectively; the maximum average memory savings are 19.44% and 17.42%, respectively. (3) Furthermore, PQSDC ranks second in compression robustness among the tested algorithms, indicating that it is less affected by the probability distribution of the QSD collections. Overall, our work provides a promising solution for QSD parallel compression, which balances storage cost, time consumption, and memory occupation primely.

AVAILABILITY AND IMPLEMENTATION

The proposed PQSDC compressor can be downloaded from https://github.com/fahaihi/PQSDC.

CMIC: an efficient quality score compressor with random access functionality

Background

Over the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access.

Results

In this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores.

Conclusions

CMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04837-1.

FCLQC: fast and concurrent lossless quality scores compressor

BACKGROUND

Advances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality.

RESULTS

This paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads.

CONCLUSION

The superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage.

LCQS: an efficient lossless compression tool of quality scores with random access functionality

Background

Advanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio. However, the main drawbacks of these compressors are the problem of weak robustness which means unstable or even unavailable results of sequencing files and the problem of slow compression speed. Meanwhile, some compressors attempt to construct a fine-grained index structure to solve the problem of slow random access decompression speed. However, they solve the problem at the sacrifice of compression speed and at the expense of large index files, which makes them inefficient and impractical. Therefore, an efficient lossless compressor of quality scores with strong robustness, high compression ratio, fast compression and random access decompression speed is urgently needed and of great significance.

Results

In this paper, based on the idea of maximizing the use of hardware resources, LCQS, a lossless compression tool specialized for quality scores, was proposed. It consists of four sequential processing steps: partitioning, indexing, packing and parallelizing. Experimental results reveal that LCQS outperforms all the other state-of-the-art compressors on all criteria except for the compression speed on the dataset SRR1284073. Furthermore, LCQS presents strong robustness on all the test datasets, with its acceleration ratios of compression speed increasing by up to 29.1x, its file size reducing by up to 28.78%, and its random access decompression speed increasing by up to 2.1x. Additionally, LCQS also exhibits strong scalability. That is, the compression speed increases almost linearly as the size of input dataset increases.

Conclusion

The ability to handle all different kinds of quality scores and superiority in compression ratio and compression speed make LCQS a high-efficient and advanced lossless quality score compressor, along with its strength of fast random access decompression. Our tool LCQS can be downloaded from https://github.com/SCUT-CCNL/LCQSand freely available for non-commercial usage.

Genomic Data Compression

Recently, there has been growing interest in genome sequencing, driven by advances in sequencing technology, in terms of both efficiency and affordability. These developments have allowed many to envision whole-genome sequencing as an invaluable tool for both personalized medical care and public health. As a result, increasingly large and ubiquitous genomic data sets are being generated. This poses a significant challenge for the storage and transmission of these data. Already, it is more expensive to store genomic data for a decade than it is to obtain the data in the first place. This situation calls for efficient representations of genomic information. In this review, we emphasize the need for designing specialized compressors tailored to genomic data and describe the main solutions already proposed. We also give general guidelines for storing these data and conclude with our thoughts on the future of genomic formats and compressors.