title: AMP: Assembly Matching Pursuit.
abstract: Metagenomics, the study of the total genetic material isolated from a biological host, promises to reveal host-microbe or microbe-microbe interactions that may help to personalize medicine or improve agronomic practice. We introduce a method that discovers metagenomic units (MGUs) relevant for phenotype prediction through sequence-based dictionary learning. The method aggregates patient-specific dictionaries and estimates MGU abundances in order to summarize a whole population and yield universally predictive biomarkers. We analyze the impact of Gaussian, Poisson, and Negative Binomial read count models in guiding dictionary construction by examining classification efficiency on a number of synthetic datasets and a real dataset from Ref. 1. Each outperforms standard methods of dictionary composition, such as random projection and orthogonal matching pursuit. Additionally, the predictive MGUs they recover are biologically relevant.
authors: Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. sbiswas@live.unc.edu
authors: S
authors: S
authors: Biswas
authors: V
authors: V
authors: Jojic
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D005243:Feces / Q000382:microbiology; D006801:Humans; D015212:Inflammatory Bowel Diseases / Q000235:genetics / Q000382:microbiology; D016013:Likelihood Functions; D054892:Metagenome; D056186:Metagenomics / Q000706:statistics & numerical data*; D015233:Models, Statistical; D016011:Normal Distribution; D016012:Poisson Distribution; D020641:Polymorphism, Single Nucleotide; D057285:Precision Medicine