MOTIVATION: High-quality sequence assembly is the ultimate representation of complete genetic information of an individual. Several ongoing pangenome projects are producing collections of high-quality assemblies of various species. Each project has already generated assemblies of hundreds of gigabytes on disk, greatly impeding the distribution of and access to such rich datasets. RESULTS: Here, we show how to reduce the size of the sequenced genomes by 2-3 orders of magnitude. Our tool compresses the genomes significantly better than the existing programs and is much faster. Moreover, its unique feature is the ability to access any contig (or its part) in a fraction of a second and easily append new samples to the compressed collections. Thanks to this, AGC could be useful not only for backup or transfer purposes but also for routine analysis of pangenome sequences in common pipelines. With the rapidly reduced cost and improved accuracy of sequencing technologies, we anticipate more comprehensive pangenome projects with much larger sample sizes. AGC is likely to become a foundation tool to store, distribute and access pangenome data. AVAILABILITY AND IMPLEMENTATION: The source code of AGC is available at https://github.com/refresh-bio/agc. The package can be installed via Bioconda at https://anaconda.org/bioconda/agc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Variant Call Format (VCF) files with results of sequencing projects take a lot of space. We propose the VCFShark, which is able to compress VCF files up to an order of magnitude better than the de facto standards (gzipped VCF and BCF). The advantage over competitors is the greatest when compressing VCF files containing large amounts of genotype data. The processing speeds up to 100 MB/s and main memory requirements lower than 30 GB allow to use our tool at typical workstations even for large datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/refresh-bio/vcfshark. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Nowadays large sequencing projects handle tens of thousands of individuals. The huge files summarizing the findings definitely require compression. We propose a tool able to compress large collections of genotypes almost 30% better than the best tool to date, i.e. squeezing human genotype to less than 62 KB. Moreover, it can also compress single samples in reference to the existing database achieving comparable results. AVAILABILITY AND IMPLEMENTATION: https://github.com/refresh-bio/GTShark. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: Kmer-db is a new tool for estimating evolutionary relationship on the basis of k-mers extracted from genomes or sequencing reads. Thanks to an efficient data structure and parallel implementation, our software estimates distances between 40 715 pathogens in <7 min (on a modern workstation), 26 times faster than Mash, its main competitor. Availability and implementation: https://github.com/refresh-bio/kmer-db and http://sun.aei.polsl.pl/REFRESH/kmer-db. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Nowadays, genome sequencing is frequently used in many research centers. In projects, such as the Haplotype Reference Consortium or the Exome Aggregation Consortium, huge databases of genotypes in large populations are determined. Together with the increasing size of these collections, the need for fast and memory frugal ways of representation and searching in them becomes crucial. Results: We present GTC (GenoType Compressor), a novel compressed data structure for representation of huge collections of genetic variation data. It significantly outperforms existing solutions in terms of compression ratio and time of answering various types of queries. We show that the largest of publicly available database of about 60 000 haplotypes at about 40 million SNPs can be stored in <4 GB, while the queries related to variants are answered in a fraction of a second. Availability and implementation: GTC can be downloaded from https://github.com/refresh-bio/GTC or http://sun.aei.polsl.pl/REFRESH/gtc. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Genomic repositories are rapidly growing, as witnessed by the 1000 Genomes or the UK10K projects. Hence, compression of multiple genomes of the same species has become an active research area in the past years. The well-known large redundancy in human sequences is not easy to exploit because of huge memory requirements from traditional compression algorithms. RESULTS: We show how to obtain several times higher compression ratio than of the best reported results, on two large genome collections (1092 human and 775 plant genomes). Our inputs are variant call format files restricted to their essential fields. More precisely, our novel Ziv-Lempel-style compression algorithm squeezes a single human genome to ∼400 KB. The key to high compression is to look for similarities across the whole collection, not just against one reference sequence, what is typical for existing solutions. AVAILABILITY: http://sun.aei.polsl.pl/tgc (also as Supplementary Material) under a free license. Supplementary data: Supplementary data are available at Bioinformatics online.