Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing.

ABSTRACT:

Motivation

The number of microbial and metagenomic studies has increased drastically due to advancements in next-generation sequencing-based measurement techniques. Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data (sampling zeros). Accounting uncertainty in microbiome data is often challenging due to the difficulty of obtaining biological replicates. Additionally, the compositional nature of current amplicon and metagenomic data differs from many other biological data types adding another challenge to the data analysis.

Results

To address these challenges in human microbiome research, we introduce a novel probabilistic approa

SUBMITTER: Aijo T 

PROVIDER: S-EPMC5860357 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature