ArticleBMC bioinformatics2012
Compareads: comparing huge metagenomic experiments.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Trial
- Fecal microbial transplant effect on clinical outcomes and fecal microbiome in active Crohn's disease.Inflammatory bowel diseases · 2015Trial
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- Reference-free k-mer based dissimilarity measures for metagenomes comparison.Frontiers in bioinformatics · 2026Article
- A review of engraftment assessments following fecal microbiota transplant.Gut microbes · 2025Review
- Comparison of k-mer-basedMicrobiome research reports · 2023Article
- A convenient correspondence between k-mer-based metagenomic distances and phylogenetically-informed β-diversity measures.PLoS computational biology · 2023Article
- Article
- Metagenome SNP calling via read-colored de Bruijn graphs.Bioinformatics (Oxford, England) · 2021Article
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- Differences in the fungal communities nursed by two genetic groups of the alpine cushion plant,Ecology and evolution · 2018Article
- A Content-Based Retrieval Framework for Whole Metagenome Sequencing Samples.Journal of integrative bioinformatics · 2018Article
- GePMI: A statistical model for personal intestinal microbiome identification.NPJ biofilms and microbiomes · 2018Article
- A framework for space-efficient read clustering in metagenomic samples.BMC bioinformatics · 2017Article
- Mash: fast genome and metagenome distance estimation using MinHash.Genome biology · 2016Article
- Article
- Subtractive assembly for comparative metagenomics, and its application to type 2 diabetes metagenomes.Genome biology · 2015Article
- Article
- Dynamics of Viral Abundance and Diversity in a Sphagnum-Dominated Peatland: Temporal Fluctuations Prevail Over Habitat.Frontiers in microbiology · 2015Article
- GenomeTester4: a toolkit for performing basic set operations - union, intersection and complement on k-mer lists.GigaScience · 2015Article
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Authors and funding
5 authors.
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Abstract
backgroundNowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria.
methodsThis work introduces Compareads, a de novo comparative metagenomic approach that returns the reads that are similar between two possibly metagenomic datasets generated by High Throughput Sequencers. One originality of this work consists in its ability to deal with huge datasets. The second main contribution presented in this paper is the design of a probabilistic data structure based on Bloom filters enabling to index millions of reads with a limited memory footprint and a controlled error rate.
resultsWe show that Compareads enables to retrieve biological information while being able to scale to huge datasets. Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers.
conclusionUsing a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples. Compareads is released under the CeCILL license and can be freely downloaded from http://alcovna.genouest.org/compareads/.
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