ArticleBMC bioinformatics2015
Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- BINSEQ: A family of high-performance binary formats for nucleotide sequences.PLoS computational biology · 2026Article
- zDUR: reference-free FASTQ compressor with high compression ratio and speed.BMC bioinformatics · 2026Article
- MBGC2: Boosting compression via efficient encoding of approximate matches in genome collections.GigaScience · 2026Article
- K2R: Tinted de Bruijn graphs implementation for efficient read extraction from sequencing datasets.Bioinformatics advances · 2025Article
- OReO: optimizing read order for practical compression.Bioinformatics advances · 2025Article
- JARVIS3: an efficient encoder for genomic data.Bioinformatics (Oxford, England) · 2024Article
- Genie: the first open-source ISO/IEC encoder for genomic data.Communications biology · 2024Article
- PMFFRC: a large-scale genomic short reads compression optimizer via memory modeling and redundant clustering.BMC bioinformatics · 2023Article
- Extremely fast construction and querying of compacted and colored de Bruijn graphs with GGCAT.Genome research · 2023Article
- Efficient compression of SARS-CoV-2 genome data using Nucleotide Archival Format.Patterns (New York, N.Y.) · 2022Review
- Scalable, ultra-fast, and low-memory construction of compacted de Bruijn graphs with Cuttlefish 2.Genome biology · 2022Article
- Topology-based sparsification of graph annotations.Bioinformatics (Oxford, England) · 2021Article
- Empirical evaluation of methods forPeerJ. Computer science · 2021Article
- Analyzing genomic data using tensor-based orthogonal polynomials with application to synthetic RNAs.NAR genomics and bioinformatics · 2020Article
- Bifrost: highly parallel construction and indexing of colored and compacted de Bruijn graphs.Genome biology · 2020Article
- EXFI: Exon and splice graph prediction without a reference genome.Ecology and evolution · 2020Article
- Performance evaluation of lossy quality compression algorithms for RNA-seq data.BMC bioinformatics · 2020Article
- Sequence Compression Benchmark (SCB) database-A comprehensive evaluation of reference-free compressors for FASTA-formatted sequences.GigaScience · 2020Article
- Better quality score compression through sequence-based quality smoothing.BMC bioinformatics · 2019Article
- Nucleotide Archival Format (NAF) enables efficient lossless reference-free compression of DNA sequences.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundData volumes generated by next-generation sequencing (NGS) technologies is now a major concern for both data storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip method.
resultsWe present a novel reference-free method meant to compress data issued from high throughput sequencing technologies. Our approach, implemented in the software LEON, employs techniques derived from existing assembly principles. The method is based on a reference probabilistic de Bruijn Graph, built de novo from the set of reads and stored in a Bloom filter. Each read is encoded as a path in this graph, by memorizing an anchoring kmer and a list of bifurcations. The same probabilistic de Bruijn Graph is used to perform a lossy transformation of the quality scores, which allows to obtain higher compression rates without losing pertinent information for downstream analyses.
conclusionsLEON was run on various real sequencing datasets (whole genome, exome, RNA-seq or metagenomics). In all cases, LEON showed higher overall compression ratios than state-of-the-art compression software. On a C. elegans whole genome sequencing dataset, LEON divided the original file size by more than 20. LEON is an open source software, distributed under GNU affero GPL License, available for download at http://gatb.inria.fr/software/leon/.
Indexed as
Identifiers
What OpenQuestion holds
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.