Tomasz Marek Kowalski

Summary: FASTA is a widely used text-based format for storing nucleotide and protein sequences. The existing FASTA compressors usually focus on (slightly) improving the compression ratio, not on practical performance. We present FFC, a scalable FASTA compressor that achieves average compression speeds 4.7× and 11.4× higher than two high-performance compressors, zstd and NAF, respectively, across a benchmark set of seven single genomes. It also delivers average decompression speeds 3.5× and 2.7× higher than zstd and NAF, respectively. Although a chunk-based zstd variant with parallel decompression, pzstd, almost matches FFC speed, its compression ratio is on average by 23% worse than FFC’s. For the experiment, a 14-core workstation and a RAM disk (to reduce the impact of I/O) were used. Availability and implementation: FFC is freely available at github.com/kowallus/ffc and also as a Zenodo repository at 10.5281/zenodo.18892353, and the used datasets at 10.5281/zenodo.18873744.

Background: FASTA is the primary format for representing DNA, RNA, and protein sequences. While progress has been made, specialized FASTA collection compressors still struggle with practical limitations and inconsistent performance across different datasets, hindering effective storage and transfer of large genomic datasets. Results: We present an enhanced version of the Multiple Bacteria Genome Compressor (MBGC), a high-throughput, in-memory algorithm for compressing genome collections. It relies on information about maximum exact matches in the compressed set to identify possibly long approximate matches. It encodes them even when they partially overlap, boosting the compression ratio by an average of 14% across bacterial datasets, while the reengineered multithreaded decoding speeds up decompression compared to its predecessor by around 40%. The compression ratio improvement is even more pronounced on other collections, reaching 18% for Homo sapiens and up to 55% for Saccharomyces paradoxus. MBGC2 performs consistently across diverse datasets and introduces practical features to ease data management, such as archive appending, repacking, fast content listing, and flexible decompression options. Benchmark tests covering nucleotide-based bacterial, viral, and human genome collections show that MBGC2 combines compression efficiency and processing speed. The tool supports working with single genomes or amino acid collections but does not guarantee such high performance in these cases. Conclusions: MBGC2 addresses critical limitations in genome collection compression by delivering reliable performance, improved compression ratios, and enhanced usability features. The consistent efficiency across diverse genomic datasets makes it a versatile tool for managing the growing volume of genomic data in research and clinical settings. The balance between compression ratio and speed positions MBGC2 as a practical solution for the storage and transfer of large genomic collections.

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).

Sample-based methods are a useful tool in analyzing the global behavior of multi-stable systems originating from various branches of science. Classical methods, such as bifurcation diagrams, Lyapunov exponents, and basins of attraction, often fail to analyze complex systems with many coexisting attractors. Thus, we have to apply a different strategy to understand the dynamics of such systems. We can distinguish basin stability, extended basin stability, constrained basin stability, basin entropy, time dependent stability margin, and survivability among sample-based methods. Each method has specific properties and gives us important data about the behavior of the analyzed system. However, none of the methods provides complete information. Hence, to have a full overview of the dynamics, one has to collect data from two or more approaches. This study describes the sample-based methods and presents their advantages and disadvantages for the archetypal nonlinear oscillator with multiple coexisting attractors. Hence, we give helpful information in selecting the best method or methods for analyzing the dynamical system.

Background: Genomes within the same species reveal large similarity, exploited by specialized multiple genome compressors. The existing algorithms and tools are however targeted at large, e.g., mammalian, genomes, and their performance on bacteria strains is rather moderate. Results: In this work, we propose MBGC, a specialized genome compressor making use of specific redundancy of bacterial genomes. Its characteristic features are finding both direct and reverse-complemented LZ-matches, as well as a careful management of a reference buffer in a multi-threaded implementation. Our tool is not only compression efficient but also fast. On a collection of 168,311 bacterial genomes, totalling 587 GB, we achieve a compression ratio of approximately a factor of 1,265 and compression (respectively decompression) speed of ∼1,580 MB/s (respectively 780 MB/s) using 8 hardware threads, on a computer with a 14-core/28-thread CPU and a fast SSD, being almost 3 times more succinct and >6 times faster in the compression than the next best competitor.

Summary Genomes within the same species reveal large similarity, exploited by specialized multiple genome compressors. The existing algorithms and tools are however targeted at large, e.g., mammalian, genomes, and their performance on bacteria strains is mediocre. In this work, we propose MBGC, a specialized genome compressor making use of specific redundancy of bacterial genomes. Our tool is not only compression efficient, but also fast. On a collection of 168,311 bacterial genomes, totalling 587 GB, we achieve the compression ratio around the factor of 730, and the compression (resp. decompression) speed around 1070 MB/s (resp. 740 MB/s) using 8 hardware threads, on a computer with a 6-core / 12-thread CPU and a fast SSD, being about 4 times more succinct and more than an order of magnitude faster in the compression than our main competitors. Availability and implementation MBGC is freely available at github.com/kowallus/mbgc.

Motivation: FASTQ remains among the widely used formats for high-throughput sequencing data. Despite advances in specialized FASTQ compressors, they are still imperfect in terms of practical performance tradeoffs. Results: 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 building an approximation of the shortest common superstring over high-quality reads. The current version, v1.2, practically preserves the compression ratio and decompression speed of the previous one, reducing the compression time by a factor of about 4–5 on a 6-core/12-thread machine. Availability: PgRC 1.2 can be downloaded from https://github.com/kowallus/PgRC . Contact: sgrabow@kis.p.lodz.pl

Motivation: The amount of sequencing data from high-throughput sequencing technologies grows at a pace exceeding the one predicted by Moore’s law. One of the basic requirements is to efficiently store and transmit such huge collections of data. Despite significant interest in designing FASTQ compressors, they are still imperfect in terms of compression ratio or decompression resources. Results: We present Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. Experiments show that PgRC wins in compression ratio over its main competitors, SPRING and Minicom, by up to 15 and 20% on average, respectively, while being comparably fast in decompression. Availability and implementation: PgRC can be downloaded from https://github.com/kowallus/PgRC. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: The amount of sequencing data from High-Throughput Sequencing technologies grows at a pace exceeding the one predicted by Moore’s law. One of the basic requirements is to efficiently store and transmit such huge collections of data. Despite significant interest in designing FASTQ compressors, they are still imperfect in terms of compression ratio or decompression resources. Results: We present Pseudogenome-based Read Compressor (PgRC), an in-memory algorithm for compressing the DNA stream, based on the idea of building an approximation of the shortest common superstring over high-quality reads. Experiments show that PgRC wins in compression ratio over its main competitors, SPRING and Minicom, by up to 18 and 21 percent on average, respectively, while being at least comparably fast in decompression. Availability: PgRC can be downloaded from https://github.com/kowallus/PgRC . Contact: tomasz.kowalski@p.lodz.pl