ArticleJournal of computational biology : a journal of computational molecular cell biology2020
An Efficient, Scalable, and Exact Representation of High-Dimensional Color Information Enabled Using de Bruijn Graph Search.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- LOCALE: Local-Alignment Embeddings for Noise-Robust DNA Search at SRA Scale.bioRxiv : the preprint server for biology · 2026Article
- Article
- Fast and Scalable Parallel External-Memory Construction of Colored Compacted de Bruijn Graphs with Cuttlefish 3.bioRxiv : the preprint server for biology · 2025Article
- Movi Color: fast and accurate taxonomic classification with the move structure.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Where the Patterns Are: Repetition-Aware Compression for Colored de Bruijn GraphsJournal of computational biology : a journal of computational molecular cell biology · 2024Article
- Compression algorithm for colored de Bruijn graphs.Algorithms for molecular biology : AMB · 2024Article
- Fulgor: a fast and compact k-mer index for large-scale matching and color queries.Algorithms for molecular biology : AMB · 2024Article
- Compression Algorithm for Colored de Bruijn Graphs.Algorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop) · 2023Article
- Hierarchical Interleaved Bloom Filter: enabling ultrafast, approximate sequence queries.Genome biology · 2023Article
- Fulgor: A fast and compactbioRxiv : the preprint server for biology · 2023Article
- An incrementally updatable and scalable system for large-scale sequence search using the Bentley-Saxe transformation.Bioinformatics (Oxford, England) · 2022Article
- VariantStore: an index for large-scale genomic variant search.Genome biology · 2021Article
- Topology-based sparsification of graph annotations.Bioinformatics (Oxford, England) · 2021Article
- Constructing small genome graphs via string compression.Bioinformatics (Oxford, England) · 2021Article
- Cuttlefish: fast, parallel and low-memory compaction of de Bruijn graphs from large-scale genome collections.Bioinformatics (Oxford, England) · 2021Article
- A tri-tuple coordinate system derived for fast and accurate analysis of the colored de Bruijn graph-based pangenomes.BMC bioinformatics · 2021Article
- Shark: fishing relevant reads in an RNA-Seq sample.Bioinformatics (Oxford, England) · 2021Article
- Data structures based onGenome research · 2021Review
- Bifrost: highly parallel construction and indexing of colored and compacted de Bruijn graphs.Genome biology · 2020Article
- Improved representation of sequence bloom trees.Bioinformatics (Oxford, England) · 2020Article
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5 authors.
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Abstract
The colored de Bruijn graph (cdbg) and its variants have become an important combinatorial structure used in numerous areas in genomics, such as population-level variation detection in metagenomic samples, large-scale sequence search, and cdbg-based reference sequence indices. As samples or genomes are added to the cdbg, the color information comes to dominate the space required to represent this data structure. In this article, we show how to represent the color information efficiently by adopting a hierarchical encoding that exploits correlations among color classes-patterns of color occurrence-present in the de Bruijn graph (dbg). A major challenge in deriving an efficient encoding of the color information that takes advantage of such correlations is determining which color classes are close to each other in the high-dimensional space of possible color patterns. We demonstrate that the dbg itself can be used as an efficient mechanism to search for approximate nearest neighbors in this space. While our approach reduces the encoding size of the color information even for relatively small cdbgs (hundreds of experiments), the gains are particularly consequential as the number of potential colors (i.e., samples or references) grows into thousands. We apply this encoding in the context of two different applications; the implicit cdbg used for a large-scale sequence search index, Mantis, as well as the encoding of color information used in population-level variation detection by tools such as Vari and Rainbowfish. Our results show significant improvements in the overall size and scalability of representation of the color information. In our experiment on 10,000 samples, we achieved >11 × better compression compared to Ramen, Ramen, Rao (RRR).
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