ArticleBioinformatics (Oxford, England)2015
LFQC: a lossless compression algorithm for FASTQ files.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A lossless reference-free sequence compression algorithm leveraging grammatical, statistical, and substitution rules.Briefings in functional genomics · 2025Article
- OReO: optimizing read order for practical compression.Bioinformatics advances · 2025Article
- PQSDC: a parallel lossless compressor for quality scores data via sequences partition and run-length prediction mapping.Bioinformatics (Oxford, England) · 2024Article
- 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
- CMIC: an efficient quality score compressor with random access functionality.BMC bioinformatics · 2022Article
- CoLoRd: compressing long reads.Nature methods · 2022Article
- FCLQC: fast and concurrent lossless quality scores compressor.BMC bioinformatics · 2021Article
- Sequence Compression Benchmark (SCB) database-A comprehensive evaluation of reference-free compressors for FASTA-formatted sequences.GigaScience · 2020Article
- LCQS: an efficient lossless compression tool of quality scores with random access functionality.BMC bioinformatics · 2020Article
- Tackling the Challenges of FASTQ Referential Compression.Bioinformatics and biology insights · 2019Article
- LFastqC: A lossless non-reference-based FASTQ compressor.PloS one · 2019Article
- MZPAQ: a FASTQ data compression tool.Source code for biology and medicine · 2019Article
- The Biological Object Notation (BON): a structured file format for biological data.Scientific reports · 2018Article
- GTZ: a fast compression and cloud transmission tool optimized for FASTQ files.BMC bioinformatics · 2017Article
- ARSDA: A New Approach for Storing, Transmitting and Analyzing Transcriptomic Data.G3 (Bethesda, Md.) · 2017Article
- LW-FQZip 2: a parallelized reference-based compression of FASTQ files.BMC bioinformatics · 2017Article
- Comparison of high-throughput sequencing data compression tools.Nature methods · 2016Article
- Single-cell Transcriptome Study as Big Data.Genomics, proteomics & bioinformatics · 2016Review
- 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
motivationNext Generation Sequencing (NGS) technologies have revolutionized genomic research by reducing the cost of whole genome sequencing. One of the biggest challenges posed by modern sequencing technology is economic storage of NGS data. Storing raw data is infeasible because of its enormous size and high redundancy. In this article, we address the problem of storage and transmission of large FASTQ files using innovative compression techniques.
resultsWe introduce a new lossless non-reference based FASTQ compression algorithm named Lossless FASTQ Compressor. We have compared our algorithm with other state of the art big data compression algorithms namely gzip, bzip2, fastqz (Bonfield and Mahoney, 2013), fqzcomp (Bonfield and Mahoney, 2013), Quip (Jones et al., 2012), DSRC2 (Roguski and Deorowicz, 2014). This comparison reveals that our algorithm achieves better compression ratios on LS454 and SOLiD datasets. AVAILABILITY AND IMPLEMENTATION: The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/rajasek/lfqc-v1.1.zip. CONTACT: rajasek@engr.uconn.edu.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.