Analysis of sequencing strategies and tools for taxonomic annotation: Defining standards for progressive metagenomics

Abstract

Metagenomics research has recently thrived due to DNA sequencing technologies improvement, driving the emergence of new analysis tools and the growth of taxonomic databases. However, there is no all-purpose strategy that can guarantee the best result for a given project and there are several combinations of software, parameters and databases that can be tested. Therefore, we performed an impartial comparison, using statistical measures of classification for eight bioinformatic tools and four taxonomic databases, defining a benchmark framework to evaluate each tool in a standardized context. Using in silico simulated data for 16S rRNA amplicons and whole metagenome shotgun data, we compared the results from different software and database combinations to detect biases related to algorithms or database annotation. Using our benchmark framework, researchers can define cut-off values to evaluate the expected error rate and coverage for their results, regardless the score used by each software. A quick guide to select the best tool, all datasets and scripts to reproduce our results and benchmark any new method are available at https://github.com/Ales-ibt/Metagenomic-benchmark. Finally, we stress out the importance of gold standards, database curation and manual inspection of taxonomic profiling results, for a better and more accurate microbial diversity description.

Tendentious effects of automated and manual metagenomic DNA purification protocols on broiler gut microbiome taxonomic profiling

“…This can be explained by the disadvantage that the V3-V4 variable regions of the 16S rRNA phylogenetic marker genes used as phylogenetic markers are very similar in sequence between non-closely related species 44,45 . This means that the lack of resolution of the different taxonomy datasets at species level is a biological issue which either classify the 16S rRNA genes correctly (true positive), do not classify them (false negative) or classify them wrongly (false positive) [46][47][48] .…”

Bacterial Diversity and the Geochemical Landscape in the Southwestern Gulf of Mexico

“…Taxonomic annotation was performed using Parallel-meta pipeline v2.4.1 ( Su et al, 2014 ) against the Metaxa2 database v2.1.1 ( Bengtsson-Palme et al, 2015 ) as described in ( Escobar-Zepeda et al, 2018 ). The annotation tables were formatted for MEGAN v5 ( Huson et al, 2007 ) to generate stacked bar plots at different taxonomic levels.…”

MetaGenomic analysis of short and long reads

“…Where possible, we wanted to analyze the differences between the RDP and MTX database for different algorithms (see Methods section). In a recent study of 16S rRNA data, Parallel-META v2.4.1 and QIIME v1.9.1 outperformed their competitors at the genus level (Escobar-Zepeda et al 2018). For this reason, we chose to compare the performance of MetaG to these two programs.…”

“…We then chose to compare the results to those obtained with Parallel-META 3. It had previously shown very high performance (Escobar-Zepeda et al 2018) and was applicable to 16S rRNA data (see previous section). MetaG was run with its standard settings for MTX and Nanopore reads.…”