FIGARO: An efficient and objective tool for optimizing microbiome rRNA gene trimming parameters

Abstract

Microbiome studies continue to provide tremendous insight into the importance of microorganism populations to the macroscopic world. High-throughput DNA sequencing technology (i.e., Next-generation Sequencing) has enabled the costeffective, rapid assessment of microbial populations when combined with bioinformatic tools capable of identifying microbial taxa and calculating the diversity and composition of biological and environmental samples. Ribosomal RNA gene sequencing, where 16S and 18S rRNA gene sequences are used to identify prokaryotic and eukaryotic species, respectively, is one of the most widely-used techniques currently employed in microbiome analysis. Prior to bioinformatic analysis of these sequences, trimming parameters must be set so that post-trimming sequence information is maximized while expected errors in the sequences themselves are minimized. In this application note, we present FIGARO: a Python-based application designed to maximize read retention after trimming and filtering for quality. FIGARO was designed specifically to increase reproducibility and minimize trial-and-error in trimming parameter selection for a DADA2-based pipeline and will likely be useful for optimizing trimming parameters and minimizing sequence errors in other pipelines as well where paired-end overlap is required. Availability and implementation:The FIGARO application is freely available as source code at https://github.com/Zymo-Research/figaro.

A Two-Time Point Analysis of Gut Microbiota in the General Population of Buenos Aires and Its Variation Due to Preventive and Compulsory Social Isolation During the COVID-19 Pandemic

“…Raw fastq reads were quality filtered (denoised, merged, and assessed for chimeras) to produce amplicon sequence variants (ASV) using the DADA2 (Plugin version 2021.2.0) pipeline ( Callahan et al, 2016 ). Figaro software was used to determine optimal trimming parameters for each group (trunc-len for PPG and PG samples was f271 r213 and f265 r219, respectively) ( Weinstein et al, 2020 ). After rare amplicon sequence variant filtering [0.1% minimum abundance filter was chosen based on the known 0.1% bleed through between Illumina MiSeq runs ( Laurence et al, 2014 ; Salter et al, 2014 )], tables were merged.…”

Gut Steroids and Microbiota: Effect of Gonadectomy and Sex

“…Sequence quality was supervised via FastQC and MultiQC [ 49 , 50 ]. FIGARO [ 51 ] determined per-sample optimal trimming and filtering parameters (sequence truncation lengths and maximum expected errors) which were then decreased by 1 to increase stringency. These values were then supplied to DADA2 (parameters: 3 × 10 8 bases for error profiling, no variant pooling, a minimum read-pair overlap of 20 bp and mismatch of 0) [ 52 ] with consensus removal of bimeras, and taxonomic identity was assigned via DADA2’s assignSpeciesTaxonomy function, using the SILVA taxonomic reference database [ 53 , 54 ], in order to determine the presence and abundance of unique amplicon sequencing variants (ASVs) representative of the different microbial populations.…”

Phyllosphere exudates select for distinct microbiome members in sorghum epicuticular wax and aerial root mucilage

“…Sequence quality control, denoising, and generation of feature tables containing counts for the Amplicon Sequencing Variants (ASVs) were performed with the q2-dada2 plugin version 2021.8.0 (Callahan et al 2016). Trimming parameters for the DADA2 plugin were selected with FIGARO version 1.1.2 (Weinstein et al 2019). ASVs tables and representative sequences from each sequencing experiment were merged with the q2-feature-table plugin.…”