Szymon Grabowski

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.

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).

Finding Maximum Exact Matches, i.e., matches between two strings that cannot be further extended to the left or right, is a classic string problem with applications in genome-to-genome comparisons. The existing tools rarely explicitly address the problem of MEM finding for a pair of very similar genomes, which may be computationally challenging. We present copMEM2, a multi-threaded implementation of its predecessor. Together with a few optimizations, including a carefully built predecessor query data structure and sort procedure selection, and taking care for highly similar data, copMEM2 allows to compute all MEMs of minimum length 50 between the human and mouse genomes in 59 s, using 10.40 GB of RAM and 12 threads, being at least a few times faster than its main contenders. On a pair of human genomes, hg18 and hg19, the results are 324 s and 16.57 GB, respectively. copMEM2 is available at https://github.com/wbieniec/copmem2. Supplementary data are available at Bioinformatics online.

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