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PICRUSt / Phylogenetic investigation of communities by reconstruction of unobserved states

A computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty.

PAPRICA / PAthway PRediction by phylogenetIC plAcement

Conducts metabolic inference on (preferably, but not exclusively, NGS) 16S rRNA gene libraries. Instead of using an OTU based approach, however, PAPRICA uses a phylogenetic placement approach. This provides a more intuitive connection between its “hidden state prediction” and library analysis components, and allows resolution at the strain and species level for some spots on the prokaryotic phylogenetic tree. PAPRICA uses pathways shared between the members of all clades on a reference tree to determine what pathways are likely to be associated with a phylogenetically placed read. In addition to predicting pathways PAPRICA provides community structure data (i.e. taxonomy) and aggregates results into the "metabolic structure" of a sample. Both of these metrics are normalized by the 16S rRNA gene copy number. Additional parameters including genome length and GC content are estimated for the genome associated with each read.


Provides a topic model framework for microbiome abundance data, as well as prediction for 16S rRNA survey data. Themetagenomics is a package that offers an R implementation of PICRUSt and wraps Tax4fun, giving users a choice for their functional prediction strategy. Both GreeneGenes and Silva taxonomic annotations are also acceptable. The user provides an abundance table, sample metadata, and taxonomy information, and this method infers the association between topics and sample features, as well as topics and predicted functional content.


Processes large-scale datasets consisting of more than one hundred environmental samples and containing more than one million reads collectively. Phoenix 2 is a generic ribosomal RNA gene sequence analysis pipeline, which was developed with the goal of providing a complete package with all the required programs and integrated Web interface that is capable of dealing with large data sets. The set of analysis results produced by Phoenix 2 is comprehensive, including taxonomic annotations using multiple methods, alpha diversity indices, beta diversity measurements, and a number of visualizations.


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A multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. Vikodak is based upon the assumption that the overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.