Table 2.

Tools used to annotate functional potential profiles from metagenomic reads or to infer them from 16S taxonomic annotation.

Tool	Approach	Synopsis	Features	Reference
BLASTx	Read annotation	Uses alignment approach to annotate nucleotide reads into potential proteins	+ great sensitivity - it can be very slow for high-throughput data	Altschul et al. (1990)
MetaGeneAnnotator	Read annotation	Identify putative proteins by estimating di-codon frequencies through the GC content of a nucleotide read	- not precisely estimate de Domain of a given sequence	Noguchi et al. (2006)
DIAMOND	Read annotation	Uses double indexing alignment to annotate nucleotide reads into potential proteins	+ 2000 to 20000 times faster than BLASTx	Buchfink et al. (2015)
SUPER-FOCUS	Read annotation	Functional profiling of metagenomes	+ output consists in a three hierarchical level functional profile, useful to choose your level of functional resolution	Silva et al. (2016b)
MGS-Fast	Read annotation	Preprocess and analyses WGS reads into functional profiles by using stringent DNA-DNA matching to the IGC database.	+ includes preprocessing steps (read trimming and removal of low-quality sequences) and taxonomic profiling	Brown et al. (2019)
MetaCLADE	Read annotation	Uses a multi-source domain annotation strategy to profile reads into protein domains.	+ designed to also annotate metatranscriptomic reads	Ugarte et al. (2018)
PICRUSt	16S inference	Uses evolutionary modelling to predict community putative functional profiles from 16S marker gene using a genome reference database	+ online interface to users unfamiliar with programming	Langille et al. (2013)
PAPRICA	16S inference	Places reads into a 16S phylogenetic tree of consensus genomes to predict the functional profile	+ very accurate to infer functional profile of well-known organisms that have plenty of genomes in the database	Bowman and Ducklow (2015)
FAPROTAX	16S inference	Extrapolates community taxonomy into putative functional profiles	- database used from cultivated organisms only	Louca et al. (2016)
QIIME	Functional pipeline	Provides a wide range of microbial assembly analysis and visualizations from raw nucleotide sequences	+ network and phylogenetic analysis and core assessment	Caporaso et al. (2010)
MOCAT2	Functional pipeline	Assemble and quality-filter reads to comprehensively predict them functionally and quantify them	+ also annotate metagenomes taxonomically	Kultima et al. (2016)