Metabarcoding versus mapping unassembled shotgun reads for identification of prey consumed by arthropod epigeal predators

Abstract

Background A central challenge of DNA gut content analysis is to identify prey in a highly degraded DNA community. In this study, we evaluated prey detection using metabarcoding and a method of mapping unassembled shotgun reads (Lazaro). Results In a mock prey community, metabarcoding did not detect any prey, probably owing to primer choice and/or preferential predator DNA amplification, while Lazaro detected prey with accuracy 43–71%. Gut content analysis of field-collected arthropod epigeal predators (3 ants, 1 dermapteran, and 1 carabid) from agricultural habitats in Brazil (27 samples, 46–273 individuals per sample) revealed that 64% of the prey species detections by either method were not confirmed by melting curve analysis and 87% of the true prey were detected in common. We hypothesized that Lazaro would detect fewer true- and false-positive and more false-negative prey with greater taxonomic resolution than metabarcoding but found that the methods were similar in sensitivity, specificity, false discovery rate, false omission rate, and accuracy. There was a positive correlation between the relative prey DNA concentration in the samples and the number of prey reads detected by Lazaro, while this was inconsistent for metabarcoding. Conclusions Metabarcoding and Lazaro had similar, but partially complementary, detection of prey in arthropod predator guts. However, while Lazaro was almost 2× more expensive, the number of reads was related to the amount of prey DNA, suggesting that Lazaro may provide quantitative prey information while metabarcoding did not.

Quantitative prey species detection in predator guts across multiple trophic levels by mapping unassembled shotgun reads

“…The blastn xml output was converted to a tabular format and, to reduce the output file size, filtered for identity above 90% and overlap length above 100 bp (taxonomic resolution is poor at overlap lengths <100 bp, Meusnier et al, 2008). The reduced blastn files and associated metadata are given in Paula et al (2022b). In the reduced blastn output file, each read could match multiple reference sequences (each match is called a hit).…”

“…The complete bioinformatic pipelines using Java and R scripts are available in the GitHub repository: https://github.com/molec ularecolo gy/DDSS (snapshot available at Paula et al, 2022b). Raw sequence reads (Fastq) were deposited at GenBank in BioProject…”

“…We showed in previous work (Paula et al, 2015(Paula et al, , 2016(Paula et al, , 2022a that direct DNA shotgun sequencing can be applied to characterize trophic interactions through gut content analysis and that a priori barcode selection, sample DNA enrichment and assembly of reads were not required. The independence from barcode(s) selection before sample preparation gives freedom to use any piece of genetic material (any kind of gene(s) or partial or full genomes) and multiple reference databases for data analysis.…”

Towards exhaustive community ecology via DNA metabarcoding

“…Unfortunately, so far genomic information outside the barcode regions is mostly limited to vertebrates, some plants (Alsos et al, 2020; Garcés‐Pastor et al, 2022), and commercially important species. As a consequence, evidences of the advantage of shotgun sequencing over PCR‐based metabarcoding for broadscale community analyses remain mixed, so far (Bell et al, 2021; Murchie et al, 2020; Parducci et al, 2019; Paula et al, 2022). Despite these issues, the continuing advances of sequencing and bioinformatics technologies suggest that shotgun metagenomics will play an increasingly important role for whole‐community analyses, particularly for topical study systems such as ancient eDNA (Pedersen et al, 2016; Wang et al, 2021).…”

On the relationship between protist metabarcoding and protist metagenome-assembled genomes

“…As an alternative, trying to find correspondence between the MAGs and V9 OTUs obtained from the sequencing of the same samples may help us to understand the way in which MAGs and V9 OTUs are related to each other, and based on this, to hypothesise what may be the underlying biological entity for identified MAG-V9 OTU pairs. Investigating the relationship between MAGs and V9 OTUs is of further interest as there is an ongoing debate regarding which of the two approaches is more accurate in representing underlying biological entities (Becker and Pushkareva, 2023;Frioux et al, 2020;Bell et al, 2021;Paula et al, 2022). Previous comparisons of metagenomics and metabarcoding data (Armbrecht et al, 2021;Sternes et al, 2017;Vannier et al, 2016) have focused on selected groups for which high-quality reference sequences are available, whereas others compared only the diversity of higher-rank taxa, with no attempt to match individual MAGs to metabarcoding OTUs (Tedersoo et al, 2015), or focused on prokaryotes (Grützke et al, 2019).…”