jMOTU and Taxonerator: Turning DNA Barcode Sequences into Annotated Operational Taxonomic Units

Abstract

BackgroundDNA barcoding and other DNA sequence-based techniques for investigating and estimating biodiversity require explicit methods for associating individual sequences with taxa, as it is at the taxon level that biodiversity is assessed. For many projects, the bioinformatic analyses required pose problems for laboratories whose prime expertise is not in bioinformatics. User-friendly tools are required for both clustering sequences into molecular operational taxonomic units (MOTU) and for associating these MOTU with known organismal taxonomies.ResultsHere we present jMOTU, a Java program for the analysis of DNA barcode datasets that uses an explicit, determinate algorithm to define MOTU. We demonstrate its usefulness for both individual specimen-based Sanger sequencing surveys and bulk-environment metagenetic surveys using long-read next-generation sequencing data. jMOTU is driven through a graphical user interface, and can analyse tens of thousands of sequences in a short time on a desktop computer. A companion program, Taxonerator, that adds traditional taxonomic annotation to MOTU, is also presented. Clustering and taxonomic annotation data are stored in a relational database, and are thus amenable to subsequent data mining and web presentation.ConclusionsjMOTU efficiently and robustly identifies the molecular taxa present in survey datasets, and Taxonerator decorates the MOTU with putative identifications. jMOTU and Taxonerator are freely available from http://www.nematodes.org/.

A critique of Rossberg et al. : noise obscures the genetic signal of meiobiotal ecospecies in ecogenomic datasets

“…We see a similar relationship for raw reads and error-cleaned sequences to that observed for the mock community sequences (figure 2), and the cleaned sequences now display the plateau that was absent from RRM's results [2]. Here, the initial steep gradient representing intra-specific variation is absent, likely because this 18S region is highly conserved even between species of the same genus and intra-specific variation is expected to be below the range plotted by RRM [6,[11][12][13]. The models developed by RRM suggest that ecospecies are unlikely to form under high values of mK, and RRM hypothesized that the higher carrying capacity K of small organisms could be responsible for their observed differences in species formation [2].…”

DNA barcoding confirms the occurrence of Rhamdia branneri and Rhamdia voulezi (Siluriformes: Heptapteridae) in the Iguaçu River Basin

“…For details about the software and download of package see Brown et al, 2012 and SIPER web site (http:// spider.r-forge.r-project.org/SpiderWebSite/spider.html).The advantage of this method is that it does not require a priori knowledge about the identity of the species and minimizes error (barcoding gaps) (Meyer, Paulay, 2005;Brown et al, 2012). The optimum threshold value was used to define the molecular operational taxonomic units (MOTUs) using JMOTU (Jones et al, 2011). The graphical representation of the MOTUs was performed by neighbor-joining analysis (NJ) using the K2P model, implemented in the software Mega 6.6 (Tamura et al, 2013).…”

The effects of parameter choice on defining molecular operational taxonomic units and resulting ecological analyses of metabarcoding data

“…Identifications of MOTU tend to be biased towards larger, more charismatic species that are better known and appear in reference collections, but unknowns are equally important in most ecological investigations and should dominate in relatively unknown fauna (Trontelj and Fišer 2009). A number of analytical programmes are used to define MOTU (e.g., Caporaso et al 2010;Jones et al 2011;Ratnasingham and Hebert 2013), and most rely on some sort of clustering or threshold approach. As a standard, 3% sequence divergence is often applied and may function well in simple communities , and it is particularly popular in bacterial research where metabarcoding techniques have been used for some time and a default 3% is generally accepted, though this represents a somewhat arbitrary choice (Yang et al 2013).…”

“…While insertions and deletions are thought to be rarer than substitutions in most high-throughput sequencing profiles (though it is platform dependent), their distribution appears to be non-random with reports of insertions more likely than deletions and concentrations of errors around specific sequence locations (Schirmer et al 2015). This length variation through sequencing error may artificially increase MOTU estimates depending on how gaps are treated in alignments and clustering methods, and indeed some clustering approaches have opted to ignore any position with a gap or indeterminate base (Jones et al 2011).…”