ArticleBriefings in bioinformatics2017
Effect of lossy compression of quality scores on variant calling.
Article in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- A Framework for Compressive On-Chip Action Potential Recording.IEEE transactions on bio-medical engineering · 2026Article
- Tutorial for variant interrogation in tumor samples.PLoS computational biology · 2026Article
- FastqCA: an effective FASTQ compressor through 2D spatial redundancy reduction.Frontiers in genetics · 2026Article
- Quest for Orthologs in the era of Data Deluge and AI: Challenges and Innovations in Orthology Prediction and Data Integration.Journal of molecular evolution · 2025Review
- Navigating bottlenecks and trade-offs in genomic data analysis.Nature reviews. Genetics · 2023Review
- Reference-free lossless compression of nanopore sequencing reads using an approximate assembly approach.Scientific reports · 2023Article
- Efficient sequencing data compression and FPGA acceleration based on a two-step framework.Frontiers in genetics · 2023Article
- ACO:lossless quality score compression based on adaptive coding order.BMC bioinformatics · 2022Article
- Nanopore quality score resolution can be reduced with little effect on downstream analysis.Bioinformatics advances · 2022Article
- Performance evaluation of lossy quality compression algorithms for RNA-seq data.BMC bioinformatics · 2020Article
- LCQS: an efficient lossless compression tool of quality scores with random access functionality.BMC bioinformatics · 2020Article
- Better quality score compression through sequence-based quality smoothing.BMC bioinformatics · 2019Article
- Denoising of Aligned Genomic Data.Scientific reports · 2019Article
- SPRING: a next-generation compressor for FASTQ data.Bioinformatics (Oxford, England) · 2019Article
- MassComp, a lossless compressor for mass spectrometry data.BMC bioinformatics · 2019Article
- CALQ: compression of quality values of aligned sequencing data.Bioinformatics (Oxford, England) · 2018Article
- GeneComp, a new reference-based compressor for SAM files.Proceedings. Data Compression Conference · 2017Article
- Using reference-free compressed data structures to analyze sequencing reads from thousands of human genomes.Genome research · 2017Article
- Article
- CROMqs: an infinitesimal successive refinement lossy compressor for the quality scores.Proceedings. Information Theory Workshop · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Recent advancements in sequencing technology have led to a drastic reduction in genome sequencing costs. This development has generated an unprecedented amount of data that must be stored, processed, and communicated. To facilitate this effort, compression of genomic files has been proposed. Specifically, lossy compression of quality scores is emerging as a natural candidate for reducing the growing costs of storage. A main goal of performing DNA sequencing in population studies and clinical settings is to identify genetic variation. Though the field agrees that smaller files are advantageous, the cost of lossy compression, in terms of variant discovery, is unclear.Bioinformatic algorithms to identify SNPs and INDELs use base quality score information; here, we evaluate the effect of lossy compression of quality scores on SNP and INDEL detection. Specifically, we investigate how the output of the variant caller when using the original data differs from that obtained when quality scores are replaced by those generated by a lossy compressor. Using gold standard genomic datasets and simulated data, we are able to analyze how accurate the output of the variant calling is, both for the original data and that previously lossily compressed. We show that lossy compression can significantly alleviate the storage while maintaining variant calling performance comparable to that with the original data. Further, in some cases lossy compression can lead to variant calling performance that is superior to that using the original file. We envisage our findings and framework serving as a benchmark in future development and analyses of lossy genomic data compressors.
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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.