PgRC: pseudogenome-based read compressor

PgRC2: engineering the compression of sequencing reads

SUMMARY

The FASTQ format remains at the heart of high-throughput sequencing. Despite advances in specialized FASTQ compressors, they are still imperfect in terms of practical performance tradeoffs. We present a multi-threaded version of Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of approximating the shortest common superstring over high-quality reads. Redundancy in the obtained string is efficiently removed by using a compact temporary representation. The current version, v2.0, preserves the compression ratio of the previous one, reducing the compression (resp. decompression) time by a factor of 8-9 (resp. 2-2.5) on a 14-core/28-thread machine.

AVAILABILITY AND IMPLEMENTATION

PgRC 2.0 can be downloaded from https://github.com/kowallus/PgRC and https://zenodo.org/records/14882486 (10.5281/zenodo.14882486).

Genie: the first open-source ISO/IEC encoder for genomic data

For the last two decades, the amount of genomic data produced by scientific and medical applications has been growing at a rapid pace. To enable software solutions that analyze, process, and transmit these data in an efficient and interoperable way, ISO and IEC released the first version of the compression standard MPEG-G in 2019. However, non-proprietary implementations of the standard are not openly available so far, limiting fair scientific assessment of the standard and, therefore, hindering its broad adoption. In this paper, we present Genie, to the best of our knowledge the first open-source encoder that compresses genomic data according to the MPEG-G standard. We demonstrate that Genie reaches state-of-the-art compression ratios while offering interoperability with any other standard-compliant decoder independent from its manufacturer. Finally, the ISO/IEC ecosystem ensures the long-term sustainability and decodability of the compressed data through the ISO/IEC-supported reference decoder.

A compressive seeding algorithm in conjunction with reordering-based compression

MOTIVATION

Seeding is a rate-limiting stage in sequence alignment for next-generation sequencing reads. The existing optimization algorithms typically utilize hardware and machine-learning techniques to accelerate seeding. However, an efficient solution provided by professional next-generation sequencing compressors has been largely overlooked by far. In addition to achieving remarkable compression ratios by reordering reads, these compressors provide valuable insights for downstream alignment that reveal the repetitive computations accounting for more than 50% of seeding procedure in commonly used short read aligner BWA-MEM at typical sequencing coverage. Nevertheless, the exploited redundancy information is not fully realized or utilized.

RESULTS

In this study, we present a compressive seeding algorithm, named CompSeed, to fill the gap. CompSeed, in collaboration with the existing reordering-based compression tools, finishes the BWA-MEM seeding process in about half the time by caching all intermediate seeding results in compact trie structures to directly answer repetitive inquiries that frequently cause random memory accesses. Furthermore, CompSeed demonstrates better performance as sequencing coverage increases, as it focuses solely on the small informative portion of sequencing reads after compression. The innovative strategy highlights the promising potential of integrating sequence compression and alignment to tackle the ever-growing volume of sequencing data.

AVAILABILITY AND IMPLEMENTATION

CompSeed is available at https://github.com/i-xiaohu/CompSeed.

PMFFRC: a large-scale genomic short reads compression optimizer via memory modeling and redundant clustering

BACKGROUND

Genomic sequencing reads compressors are essential for balancing high-throughput sequencing short reads generation speed, large-scale genomic data sharing, and infrastructure storage expenditure. However, most existing short reads compressors rarely utilize big-memory systems and duplicative information between diverse sequencing files to achieve a higher compression ratio for conserving reads data storage space.

RESULTS

We employ compression ratio as the optimization objective and propose a large-scale genomic sequencing short reads data compression optimizer, named PMFFRC, through novelty memory modeling and redundant reads clustering technologies. By cascading PMFFRC, in 982 GB fastq format sequencing data, with 274 GB and 3.3 billion short reads, the state-of-the-art and reference-free compressors HARC, SPRING, Mstcom, and FastqCLS achieve 77.89%, 77.56%, 73.51%, and 29.36% average maximum compression ratio gains, respectively. PMFFRC saves 39.41%, 41.62%, 40.99%, and 20.19% of storage space sizes compared with the four unoptimized compressors.

CONCLUSIONS

PMFFRC rational usage big-memory of compression server, effectively saving the sequencing reads data storage space sizes, which relieves the basic storage facilities costs and community sharing transmitting overhead. Our work furnishes a novel solution for improving sequencing reads compression and saving storage space. The proposed PMFFRC algorithm is packaged in a same-name Linux toolkit, available un-limited at https://github.com/fahaihi/PMFFRC .

A new efficient referential genome compression technique for FastQ files

Hospitals and medical laboratories create a tremendous amount of genome sequence data every day for use in research, surgery, and illness diagnosis. To make storage comprehensible, compression is therefore essential for the storage, monitoring, and distribution of all these data. A novel data compression technique is required to reduce the time as well as the cost of storage, transmission, and data processing. General-purpose compression techniques do not perform so well for these data due to their special features: a large number of repeats (tandem and palindrome), small alphabets, and highly similar, and specific file formats. In this study, we provide a method for compressing FastQ files that uses a reference genome as a backup without sacrificing data quality. FastQ files are initially split into three streams (identifier, sequence, and quality score), each of which receives its own compression technique. A novel quick and lightweight mapping mechanism is also presented to effectively compress the sequence stream. As shown by experiments, the suggested methods, both the compression ratio and the compression/decompression duration of NGS data compressed using RBFQC, are superior to those achieved by other state-of-the-art genome compression methods. In comparison to GZIP, RBFQC may achieve a compression ratio of 80-140% for fixed-length datasets and 80-125% for variable-length datasets. Compared to domain-specific FastQ file referential genome compression techniques, RBFQC has a compression and decompression speed (total) improvement of 10-25%.

Reference-free lossless compression of nanopore sequencing reads using an approximate assembly approach

The amount of data produced by genome sequencing experiments has been growing rapidly over the past several years, making compression important for efficient storage, transfer and analysis of the data. In recent years, nanopore sequencing technologies have seen increasing adoption since they are portable, real-time and provide long reads. However, there has been limited progress on compression of nanopore sequencing reads obtained in FASTQ files since most existing tools are either general-purpose or specialized for short read data. We present NanoSpring, a reference-free compressor for nanopore sequencing reads, relying on an approximate assembly approach. We evaluate NanoSpring on a variety of datasets including bacterial, metagenomic, plant, animal, and human whole genome data. For recently basecalled high quality nanopore datasets, NanoSpring, which focuses only on the base sequences in the FASTQ file, uses just 0.35-0.65 bits per base which is 3-6[Formula: see text] lower than general purpose compressors like gzip. NanoSpring is competitive in compression ratio and compression resource usage with the state-of-the-art tool CoLoRd while being significantly faster at decompression when using multiple threads (> 4[Formula: see text] faster decompression with 20 threads). NanoSpring is available on GitHub at https://github.com/qm2/NanoSpring .

Simultaneous compression of multiple error-corrected short-read sets for faster data transmission and better de novo assemblies

Next-Generation Sequencing has produced incredible amounts of short-reads sequence data for de novo genome assembly over the last decades. For efficient transmission of these huge datasets, high-performance compression algorithms have been intensively studied. As both the de novo assembly and error correction methods utilize the overlaps between reads data, a concern is that the will the sequencing errors bring up negative effects on genome assemblies also affect the compression of the NGS data. This work addresses two problems: how current error correction algorithms can enable the compression algorithms to make the sequence data much more compact, and whether the sequence-modified reads by the error-correction algorithms will lead to quality improvement for de novo contig assembly. As multiple sets of short reads are often produced by a single biomedical project in practice, we propose a graph-based method to reorder the files in the collection of multiple sets and then compress them simultaneously for a further compression improvement after error correction. We use examples to illustrate that accurate error correction algorithms can significantly reduce the number of mismatched nucleotides in the reference-free compression, hence can greatly improve the compression performance. Extensive test on practical collections of multiple short-read sets does confirm that the compression performance on the error-corrected data (with unchanged size) significantly outperforms that on the original data, and that the file reordering idea contributes furthermore. The error correction on the original reads has also resulted in quality improvements of the genome assemblies, sometimes remarkably. However, it is still an open question that how to combine appropriate error correction methods with an assembly algorithm so that the assembly performance can be always significantly improved.

SFQ: Constructing and Querying a Succinct Representation of FASTQ Files

A large and ever increasing quantity of high throughput sequencing (HTS) data is stored in FASTQ files. Various methods for data compression are used to mitigate the storage and transmission costs, from the still prevalent general purpose Gzip to state-of-the-art specialized methods. However, all of the existing methods for FASTQ file compression require the decompression stage before the HTS data can be used. This is particularly costly with the random access to specific records in FASTQ files. We propose the sFASTQ format, a succinct representation of FASTQ files that can be used without decompression (i.e., the records can be retrieved and listed online), and that supports random access to individual records. The sFASTQ format can be searched on the disk, which eliminates the need for any additional memory resources. The searchable sFASTQ archive is of comparable size to the corresponding Gzip file. sFASTQ format outputs (interleaved) FASTQ records to the STDOUT stream. We provide SFQ, a software for the construction and usage of the sFASTQ format that supports variable length reads, pairing of records, and both lossless and lossy compression of quality scores.

CURC: A CUDA-based reference-free read compressor

MOTIVATION

The data deluge of high-throughput sequencing has posed great challenges to data storage and transfer. Many specific compression tools have been developed to solve this problem. However, most of the existing compressors are based on CPU platform, which might be inefficient and expensive to handle large-scale HTS data. With the popularization of GPUs, GPU-compatible sequencing data compressors become desirable to exploit the computing power of GPUs.

RESULTS

We present a GPU-accelerated reference-free read compressor, namely CURC, for FASTQ files. Under a GPU-CPU heterogeneous parallel scheme, CURC implements highly efficient lossless compression of DNA stream based on the pseudogenome approach and CUDA library. CURC achieves 2∼6-fold speedup of the compression with competitive compression rate, compared with other state-of-the-art reference-free read compressors.

AVAILABILITY

CURC can be downloaded from https://github.com/BioinfoSZU/CURC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hamming-shifting graph of genomic short reads: Efficient construction and its application for compression

Graphs such as de Bruijn graphs and OLC (overlap-layout-consensus) graphs have been widely adopted for the de novo assembly of genomic short reads. This work studies another important problem in the field: how graphs can be used for high-performance compression of the large-scale sequencing data. We present a novel graph definition named Hamming-Shifting graph to address this problem. The definition originates from the technological characteristics of next-generation sequencing machines, aiming to link all pairs of distinct reads that have a small Hamming distance or a small shifting offset or both. We compute multiple lexicographically minimal k-mers to index the reads for an efficient search of the weight-lightest edges, and we prove a very high probability of successfully detecting these edges. The resulted graph creates a full mutual reference of the reads to cascade a code-minimized transfer of every child-read for an optimal compression. We conducted compression experiments on the minimum spanning forest of this extremely sparse graph, and achieved a 10 - 30% more file size reduction compared to the best compression results using existing algorithms. As future work, the separation and connectivity degrees of these giant graphs can be used as economical measurements or protocols for quick quality assessment of wet-lab machines, for sufficiency control of genomic library preparation, and for accurate de novo genome assembly.

SCA-NGS: Secure compression algorithm for next generation sequencing data using genetic operators and block sorting

Recent advancements in sequencing methods have led to significant increase in sequencing data. Increase in sequencing data leads to research challenges such as storage, transfer, processing, etc. data compression techniques have been opted to cope with the storage of these data. There have been good achievements in compression ratio and execution time. This fast-paced advancement has raised major concerns about the security of data. Confidentiality, integrity, authenticity of data needs to be ensured. This paper presents a novel lossless reference-free algorithm that focuses on data compression along with encryption to achieve security in addition to other parameters. The proposed algorithm uses preprocessing of data before applying general-purpose compression library. Genetic algorithm is used to encrypt the data. The technique is validated with experimental results on benchmark datasets. Comparative analysis with state-of-the-art techniques is presented. The results show that the proposed method achieves better results in comparison to existing methods.

Transformation of FASTA files into feature vectors for unsupervised compression of short reads databases

FASTA data sets of short reads are usually generated in tens or hundreds for a biomedical study. However, current compression of these data sets is carried out one-by-one without consideration of the inter-similarity between the data sets which can be otherwise exploited to enhance compression performance of de novo compression. We show that clustering these data sets into similar sub-groups for a group-by-group compression can greatly improve the compression performance. Our novel idea is to detect the lexicographically smallest k-mer (k-minimizer) for every read in each data set, and uses these k-mers as features and their frequencies in every data set as feature values to transform these huge data sets each into a characteristic feature vector. Unsupervised clustering algorithms are then applied to these vectors to find similar data sets and merge them. As the amount of common k-mers of similar feature values between two data sets implies an excessive proportion of overlapping reads shared between the two data sets, merging similar data sets creates immense sequence redundancy to boost the compression performance. Experiments confirm that our clustering approach can gain up to 12% improvement over several state-of-the-art algorithms in compressing reads databases consisting of 17-100 data sets (48.57-197.97[Formula: see text]GB).

Allowing mutations in maximal matches boosts genome compression performance

MOTIVATION

A maximal match between two genomes is a contiguous non-extendable sub-sequence common in the two genomes. DNA bases mutate very often from the genome of one individual to another. When a mutation occurs in a maximal match, it breaks the maximal match into shorter match segments. The coding cost using these broken segments for reference-based genome compression is much higher than that of using the maximal match which is allowed to contain mutations.

RESULTS

We present memRGC, a novel reference-based genome compression algorithm that leverages mutation-containing matches (MCMs) for genome encoding. MemRGC detects maximal matches between two genomes using a coprime double-window k-mer sampling search scheme, the method then extends these matches to cover mismatches (mutations) and their neighbouring maximal matches to form long and MCMs. Experiments reveal that memRGC boosts the compression performance by an average of 27% in reference-based genome compression. MemRGC is also better than the best state-of-the-art methods on all of the benchmark datasets, sometimes better by 50%. Moreover, memRGC uses much less memory and de-compression resources, while providing comparable compression speed. These advantages are of significant benefits to genome data storage and transmission.

AVAILABILITY AND IMPLEMENTATION

https://github.com/yuansliu/memRGC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.