ArticleBioinformatics (Oxford, England)2018
Compression of genomic sequencing reads via hash-based reordering: algorithm and analysis.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- FastqCA: an effective FASTQ compressor through 2D spatial redundancy reduction.Frontiers in genetics · 2026Article
- Efficient and robust search of microbial genomes via phylogenetic compression.Nature methods · 2025Article
- OReO: optimizing read order for practical compression.Bioinformatics advances · 2025Article
- PMFFRC: a large-scale genomic short reads compression optimizer via memory modeling and redundant clustering.BMC bioinformatics · 2023Article
- Efficient sequencing data compression and FPGA acceleration based on a two-step framework.Frontiers in genetics · 2023Article
- Efficient compression of SARS-CoV-2 genome data using Nucleotide Archival Format.Patterns (New York, N.Y.) · 2022Review
- Hamming-shifting graph of genomic short reads: Efficient construction and its application for compression.PLoS computational biology · 2021Article
- Variable-order reference-free variant discovery with the Burrows-Wheeler Transform.BMC bioinformatics · 2020Article
- Sequence Compression Benchmark (SCB) database-A comprehensive evaluation of reference-free compressors for FASTA-formatted sequences.GigaScience · 2020Article
- SPRING: a next-generation compressor for FASTQ data.Bioinformatics (Oxford, England) · 2019Article
- Tackling the Challenges of FASTQ Referential Compression.Bioinformatics and biology insights · 2019Article
- BdBG: a bucket-based method for compressing genome sequencing data with dynamic de Bruijn graphs.PeerJ · 2018Article
- SCA-NGS: Secure compression algorithm for next generation sequencing data using genetic operators and block sorting.Science progressArticle
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Authors and funding
3 authors.
Funding
Abstract
Motivation: New Generation Sequencing (NGS) technologies for genome sequencing produce large amounts of short genomic reads per experiment, which are highly redundant and compressible. However, general-purpose compressors are unable to exploit this redundancy due to the special structure present in the data. Results: We present a new algorithm for compressing reads both with and without preserving the read order. In both cases, it achieves 1.4×-2× compression gain over state-of-the-art read compression tools for datasets containing as many as 3 billion Illumina reads. Our tool is based on the idea of approximately reordering the reads according to their position in the genome using hashed substring indices. We also present a systematic analysis of the read compression problem and compute bounds on fundamental limits of read compression. This analysis sheds light on the dynamics of the proposed algorithm (and read compression algorithms in general) and helps understand its performance in practice. The algorithm compresses only the read sequence, works with unaligned FASTQ files, and does not require a reference. Contact: schandak@stanford.edu. Supplementary information: Supplementary material are available at Bioinformatics online. The proposed algorithm is available for download at https://github.com/shubhamchandak94/HARC.
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