ArticleBioinformatics (Oxford, England)2019
Metagenomic binning through low-density hashing.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- CONSULT-II: accurate taxonomic identification and profiling using locality-sensitive hashing.Bioinformatics (Oxford, England) · 2024Article
- dna2bit: high performance genomic distance estimation software for microbial genome analysis.Frontiers in microbiology · 2024Article
- Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing.Genome biology · 2023Article
- Metagenome analysis using the Kraken software suite.Nature protocols · 2022Review
- CONSULT: accurate contamination removal using locality-sensitive hashing.NAR genomics and bioinformatics · 2021Article
- Improving metagenomic binning results with overlapped bins using assembly graphs.Algorithms for molecular biology : AMB · 2021Article
- MetaBCC-LR: metagenomics binning by coverage and composition for long reads.Bioinformatics (Oxford, England) · 2020Article
- To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics.Nucleic acids research · 2020Article
- Carnelian uncovers hidden functional patterns across diverse study populations from whole metagenome sequencing reads.Genome biology · 2020Article
- Dashing: fast and accurate genomic distances with HyperLogLog.Genome biology · 2019Article
- Streaming histogram sketching for rapid microbiome analytics.Microbiome · 2019Article
- Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines.IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum : [proceedings]. IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd ForumArticle
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Authors and funding
5 authors.
Funding
Abstract
Motivation: Vastly greater quantities of microbial genome data are being generated where environmental samples mix together the DNA from many different species. Here, we present Opal for metagenomic binning, the task of identifying the origin species of DNA sequencing reads. We introduce 'low-density' locality sensitive hashing to bioinformatics, with the addition of Gallager codes for even coverage, enabling quick and accurate metagenomic binning. Results: On public benchmarks, Opal halves the error on precision/recall (F1-score) as compared with both alignment-based and alignment-free methods for species classification. We demonstrate even more marked improvement at higher taxonomic levels, allowing for the discovery of novel lineages. Furthermore, the innovation of low-density, even-coverage hashing should itself prove an essential methodological advance as it enables the application of machine learning to other bioinformatic challenges. Availability and implementation: Full source code and datasets are available at http://opal.csail.mit.edu and https://github.com/yunwilliamyu/opal. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.