ArticleComputational and structural biotechnology journal2022
KOMB: K-core based de novo characterization of copy number variation in microbiomes.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Leveraging spectrum of graph sheaf Laplacian as a genome-architecture-aware measure of microbiome diversity.bioRxiv : the preprint server for biology · 2026Article
- Comparative metagenomics using pan-metagenomic graphs.bioRxiv : the preprint server for biology · 2025Article
- Leveraging human microbiomes for disease prediction and treatment.Trends in pharmacological sciences · 2025Article
- Graph-based self-supervised learning for repeat detection in metagenomic assembly.Genome research · 2024Article
- KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Capturing variation in metagenomic assembly graphs with MetaCortex.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.
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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.