ArticleGigaScience2019
Libra: scalable k-mer-based tool for massive all-vs-all metagenome comparisons.
Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- LLM-Assessed Relatedness of Microbiome Study Descriptions Aligns more Strongly with Functional than with Taxonomic Profile Similarity.Microbial ecology · 2026Article
- Reference-free k-mer based dissimilarity measures for metagenomes comparison.Frontiers in bioinformatics · 2026Article
- Elucidating Transitions ofJournal of computational biology : a journal of computational molecular cell biology · 2026Article
- kMermaid: Ultrafast metagenomic read assignment to protein clusters by hashing of amino acid k-mer frequencies.PLoS computational biology · 2025Article
- Estimating similarity and distance using FracMinHash.Algorithms for molecular biology : AMB · 2025Article
- Integrating sequence composition information into microbial diversity analyses with k-mer frequency counting.mSystems · 2025Article
- K-mer-based Approaches to Bridging Pangenomics and Population Genetics.Molecular biology and evolution · 2025Review
- Microbial communities associated with marine sponges from diverse geographic locations harbor biosynthetic novelty.Applied and environmental microbiology · 2024Article
- A Tale of Two Seasons: Distinct Seasonal Viral Communities in a Thermokarst Lake.Microorganisms · 2023Article
- A convenient correspondence between k-mer-based metagenomic distances and phylogenetically-informed β-diversity measures.PLoS computational biology · 2023Article
- Comparison of k-mer-basedMicrobiome research reports · 2023Article
- Practical selection of representative sets of RNA-seq samples using a hierarchical approach.Bioinformatics (Oxford, England) · 2021Article
- Biases in Viral Metagenomics-Based Detection, Cataloguing and Quantification of Bacteriophage Genomes in Human Faeces, a Review.Microorganisms · 2021Review
- Planet Microbe: a platform for marine microbiology to discover and analyze interconnected 'omics and environmental data.Nucleic acids research · 2021Article
- Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data.Biology direct · 2020Article
- Metagenomic analysis through the extended Burrows-Wheeler transform.BMC bioinformatics · 2020Article
- Article
- A New Alignment-Free Whole Metagenome Comparison Tool and Its Application on Gut Microbiomes of Wild Giant Pandas.Frontiers in microbiology · 2020Article
- Gut Microbiota in Dholes During Estrus.Frontiers in microbiology · 2020Article
- Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity.PLoS computational biology · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Shotgun metagenomics provides powerful insights into microbial community biodiversity and function. Yet, inferences from metagenomic studies are often limited by dataset size and complexity and are restricted by the availability and completeness of existing databases. De novo comparative metagenomics enables the comparison of metagenomes based on their total genetic content. Results: We developed a tool called Libra that performs an all-vs-all comparison of metagenomes for precise clustering based on their k-mer content. Libra uses a scalable Hadoop framework for massive metagenome comparisons, Cosine Similarity for calculating the distance using sequence composition and abundance while normalizing for sequencing depth, and a web-based implementation in iMicrobe (http://imicrobe.us) that uses the CyVerse advanced cyberinfrastructure to promote broad use of the tool by the scientific community. Conclusions: A comparison of Libra to equivalent tools using both simulated and real metagenomic datasets, ranging from 80 million to 4.2 billion reads, reveals that methods commonly implemented to reduce compute time for large datasets, such as data reduction, read count normalization, and presence/absence distance metrics, greatly diminish the resolution of large-scale comparative analyses. In contrast, Libra uses all of the reads to calculate k-mer abundance in a Hadoop architecture that can scale to any size dataset to enable global-scale analyses and link microbial signatures to biological processes.
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