AUTHOR=Rossel Sven , Khodami Sahar , Martínez Arbizu Pedro TITLE=Comparison of Rapid Biodiversity Assessment of Meiobenthos Using MALDI-TOF MS and Metabarcoding JOURNAL=Frontiers in Marine Science VOLUME=6 YEAR=2019 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2019.00659 DOI=10.3389/fmars.2019.00659 ISSN=2296-7745 ABSTRACT=

Nowadays, most biodiversity assessments involving meiofauna are mainly carried out using very time-consuming, specimen-wise morphological identifications, which demands comprehensive taxonomic knowledge. Animals have to be examined for minor differences of setae compositions, mouthpart morphology or number of segments for various extremities. DNA-based methods such as metabarcoding as well as recently emerged rapid analyses using MALDI-TOF mass spectrometry to identify specimens based on a proteome fingerprint could vastly accelerate the process of specimen identification in biodiversity assessments. However, these techniques depend on reference libraries to connect collected data to morphologically described species. In this study the success rate of both approaches have been tested based on reference libraries constructed using part of the samples from a new study area to identify unknown samples. Using MALDI-TOF MS we found, that species which do not exist in an incomplete mass spectra reference library only have minor impact on the results, when employing a post hoc test for Random Forest classifications. This test reveals specimens that demand morphological re-examination for the final species assignment. Metabarcoding however strongly demands a rich reference library to provide correct MOTU assessments in congruence with morphological determination. Nevertheless, with a complete library and a suitable data transformation [herein log(x + 1)], the number of reads per MOTU reflects relative species abundances in metabarcoding inference. The results of this study facilitate specimen identification by using MALDI-TOF MS, which is incomparably cheap for specimen-by-specimen identification, but when it comes to sample-wise analyses, metabarcoding outperforms other techniques by far.