Year: 2020
Type: article
Abstract: Metagenomic profiling, predicting the presence and relative abundances of microbes in a sample, is a critical first step in microbiome analysis. Alignment-based approaches are often considered accurate yet computationally infeasible. Here, we present a novel method, Metalign, that performs efficient and accurate alignment-based metagenomic profiling. We use a novel containment min hash approach to pre-filter the reference database prior to alignment and then process both uniquely aligned and multi-aligned reads to produce accurate abundance estimates. In performance evaluations on both real and simulated datasets, Metalign is the only method evaluated that maintained high performance and competitive running time across all datasets.
Source: Genome biology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,990
Funders: National Science Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of General Medical Sciences, National Institute of Environmental Health Sciences, National Institute of Biomedical Imaging and Bioengineering
Awards: G1104670545, Substance Abuse & Behavioral Disinhibition: Integrating Genes & Environment, III: Small: Inference of Causal Regulatory Relationships from Genetic Studies, G1740316563, G1779979025, BSF:2012304:Methods for Preprocessing Population Sequence Data, G241196917, G2568082621, III: Small: Causal and Statistical Inference in the Presence of Confounding Factors, G343680451, QuBBD: Fast, Efficient Mathematical Approach to the Analysis of the Human Microbiome through Biodiversity Optimization, Genetic Regulation of Gene Expression and its Impact on Phenotypes, III: Medium: Private Identification of Relatives and Private GWAS: First Steps in the New Field of CryptoGenomics, III: Medium: Meta-analysis reinterpreted using causal graphs, Collaborative Research: SEIII: Estimating Haplotype Frequencies, G4847429650, Collaborative Research: SEIII: Estimating Haplotype Frequencies, An integrated molecular approach to understand variation in iron metabolism, INTEGRATIVE GENETIC APPROACHES TO GENE-AIR POLLUTION INTERACTIONS IN ASTHMA, Collaborative Research: Design and Analysis of Compressed Sensing DNA Microarrays, NRT-HDR: Modeling and Understanding Human Behavior: Harnessing Data from Genes to Social Networks, G9000015156, G9000025444, G9000032695, G9000061537, G9000065387, G9000097946, G9000100823, G9000102098, G9000140233, G9000151782, G9000222054, Medical Imaging Informatics Training Grant
OpenAlex ID: W3083852019