Sponsler, D. | bioRxiv Science

bioRxiv2019

MetaCurator: A hidden Markov model-based toolkit for extracting and curating sequences from taxonomically-informative genetic markers

The community-level analysis of samples containing diverse genetic material, via metabarcoding and metagenomic approaches, is increasingly popular. While the production of sequence data for such studies has become straightforward, questions remain about how best to analyze and taxonomically characterize sequence data. For many sequence classification approaches, an important component of the workflow involves the curation of reference sequences. Ideally, this involves trimming away extraneous sequence at the 3 prime and 5 prime ends of the target marker of interest, as well as the removal of reference sequence duplicates. Here, we present MetaCurator, a software package written in Python, designed for automated reference sequence curation and highly generalizable across markers and study systems. MetaCurator is organized in a modular fashion, so users can implement tools individually in addition to utilizing the automated and flexible MetaCurator parental code. Aside from modules used to organize and format taxonomic lineage data, MetaCurator contains two signature tools. IterRazor utilizes profile hidden Markov models and an iterative search framework to exhaustively identify and extract the precise amplicon marker of interest from available reference sequence data. DerepByTaxonomy then facilitates sequence dereplication using a taxonomically aware approach, removing duplicates only when they belong to the same taxon. This is important for cases of incomplete lineage sorting between species and for highly conserved markers, such as plant rbcL and trnL, which often display no sequence divergence across taxa, even at the genus level.\n\nAvailability and implementationMetaCurator is supported on OSX and Linux (RedHat/CentOS) and is freely available under a GPL v3.0 license at https://github.com/RTRichar/MetaCurator.\n\nContactrtr87@yorku.ca\n\nSupplemental informationCode associated with this work is available at https://github.com/RTRichar/MetabarcodeDBsV2 and additional analysis is presented in supplementary files.

bioinformatics↗

bioRxiv2018

Quantitative multi-locus metabarcoding and waggle dance interpretation reveal honey bee spring foraging patterns in Midwest agroecosystems

We explored the pollen foraging behavior of honey bee colonies situated in the corn and soybean dominated agroecosystems of central Ohio over a month-long period using both pollen metabarcoding and waggle dance inference of spatial foraging patterns. For molecular pollen analysis we developed simple and cost-effective laboratory and bioinformatics methods. Targeting four plant barcode loci (ITS2, rbcL, trnL and trnH), we implemented metabarcoding library preparation and dual-indexing protocols designed to minimize amplification biases and index mis-tagging events. We constructed comprehensive, curated reference databases for hierarchical taxonomic classification of metabarcoding data and used these databases to train the Metaxa2 DNA sequence classifier. Comparisons between morphological and molecular palynology provide strong support for the quantitative potential of multi-locus metabarcoding. Results revealed consistent foraging habits between locations and show clear trends in the phenological progression of honey bee spring foraging in these agricultural areas. Our data suggest that three key taxa, woody Rosaceae such as pome fruits and hawthorns, Salix, and Trifolium provided the majority of pollen nutrition during the study. Spatially, these foraging patterns were associated with a significant preference for forests and tree lines relative to crop fields and herbaceous land cover.

ecology↗