ArticleGenome biology2015
Subtractive assembly for comparative metagenomics, and its application to type 2 diabetes metagenomes.
Article in Genome biology, 2015. 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.
- Locality-Sensitive Hashing-Based k-Mer Clustering for Identification of Differential Microbial Markers Related to Host Phenotype.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Information Theoretic Metagenome Assembly Allows the Discovery of Disease Biomarkers in Human Microbiome.Entropy (Basel, Switzerland) · 2021Article
- MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.Methods (San Diego, Calif.) · 2019Article
- A repository of microbial marker genes related to human health and diseases for host phenotype prediction using microbiome data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Graft-Derived Reconstitution of Mucosal-Associated Invariant T Cells after Allogeneic Hematopoietic Cell Transplantation.Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation · 2018Observational
- A concurrent subtractive assembly approach for identification of disease associated sub-metagenomes.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2017Article
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Authors and funding
3 authors.
Funding
Abstract
Comparative metagenomics remains challenging due to the size and complexity of metagenomic datasets. Here we introduce subtractive assembly, a de novo assembly approach for comparative metagenomics that directly assembles only the differential reads that distinguish between two groups of metagenomes. Using simulated datasets, we show it improves both the efficiency of the assembly and the assembly quality of the differential genomes and genes. Further, its application to type 2 diabetes (T2D) metagenomic datasets reveals clear signatures of the T2D gut microbiome, revealing new phylogenetic and functional features of the gut microbial communities associated with T2D.
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