Biology subjects

Deorowicz, S.

Publications and source records attributed to Deorowicz, S..

5 recordsLinked to original sources

bioRxiv2018

Kmer-db: instant evolutionary distance estimation

SummaryKmer-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 less than 4 minutes (on a modern workstation), 44 times faster than Mash, its main competitor.\n\nAvailability and Implementationhttps://github.com/refresh-bio/kmer-db\n\nContactsebastian.deorowicz@polsl.pl\n\nSupplementary informationSupplementary data are available at publishers Web site

bioinformatics↗

bioRxiv2017

MSAC: Compression of multiple sequence alignment files

MotivationBioinformatics databases grow rapidly and achieve values hardly to imagine a decade ago. Among numerous bioinformatics processes generating hundreds of GB is multiple sequence alignments of protein families. Its largest database, i.e., Pfam, consumes 40-230 GB, depending of the variant. Storage and transfer of such massive data has become a challenge.\n\nResultsWe propose a novel compression algorithm, MSAC (Multiple Sequence Alignment Compressor), designed especially for aligned data. It is based on a generalisation of the positional Burrows-Wheeler transform for non-binary alphabets. MSAC handles FASTA, as well as Stockholm files. It offers up to six times better compression ratio than other commonly used compressors, i.e., gzip. Performed experiments resulted in an analysis of the influence of a protein family size on the compression ratio.\n\nAvailabilityMSAC is available for free at https://github.com/refresh-bio/msac and http://sun.aei.polsl.pl/REFRESH/msac.\n\nContactsebastian.deorowicz@polsl.pl\n\nSupplementary materialSupplementary data are available at the publisher Web site.

bioinformatics↗

bioRxiv2017

Whisper: Read sorting allows robust mapping of sequencing data

MotivationMapping reads to a reference genome is often the first step in a sequencing data analysis pipeline. Mistakes made at this computationally challenging stage cannot be recovered easily.\n\nResultsWe present Whisper, an accurate and high-performant mapping tool, based on the idea of sorting reads and then mapping them against suffix arrays for the reference genome and its reverse complement. Employing task and data parallelism as well as storing temporary data on disk result in superior time efficiency at reasonable memory requirements. Whisper excels at large NGS read collections, in particular Illumina reads with typical WGS coverage. The experiments with real data indicate that our solution works in about 15% of the time needed by the well-known Bowtie2 and BWA-MEM tools at a comparable accuracy (validated in variant calling pipeline).\n\nAvailabilityWhisper is available for free from https://github.com/refresh-bio/Whisper or http://sun.aei.polsl.pl/REFRESH/Whisper/\n\nContactsebastian.deorowicz@polsl.pl\n\nSupplementary informationSupplementary data are available at publisher Web site.

bioinformatics↗

bioRxiv2017

FaStore - a space-saving solution for raw sequencing data

The affordability of DNA sequencing has led to the generation of unprecedented volumes of raw sequencing data. These data must be stored, processed, and transmitted, which poses significant challenges. To facilitate this effort, we introduce FaStore, a specialized compressor for FASTQ files. The proposed algorithm does not use any reference sequences for compression, and permits the user to choose from several lossy modes to improve the overall compression ratio, depending on the specific needs. We demonstrate through extensive simulations that FaStore achieves a significant improvement in compression ratio with respect to previously proposed algorithms for this task. In addition, we perform an analysis on the effect that the different lossy modes have on variant calling, the most widely used application for clinical decision making, especially important in the era of precision medicine. We show that lossy compression can offer significant compression gains, while preserving the essential genomic information and without affecting the variant calling performance.

bioinformatics↗

bioRxiv2017

GTC: An Attempt To Maintenance Of Huge Genome Collections Compressed

MotivationO_ST_ABSResultsC_ST_ABSWe present GTC, a novel compressed data structure for representation of huge collections of genetic variation data. GTC 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 thousand haplotypes at about 40 million SNPs can be stored in less than 4 Gbytes, while the queries related to variants are answered in a fraction of a second.\n\nAvailabilityGTC can be downloaded from https://github.com/refresh-bio/GTC or http://sun.aei.polsl.pl/REFRESH/GTC.\n\nContactsebastian.deorowicz@polsl.pl

bioinformatics↗