DOI: 10.5061/dryad.rxwdbrv46
title: Data from: Comparison of rapid biodiversity assessment of meiobenthos using MALDI-TOF MS and metabarcoding

description: 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.
description: Samples
 were taken by hand with a syringe (Ø 3.1cm, 5cm depth) during low tide at
 a tidal flat (53°38'40.2"N 8°04'57.6"E) in front of
 the village Hooksiel in the littoral zone of the German North Sea coast on
 19<sup>th</sup> April 2017. Twelve sandy sediment samples were
 fixed in absolute ethanol and stored overnight at -25°C. Samples were
 sieved through a 40 µm sieve and density-gravity centrifuged according to
 McIntyre and Warwick (1984) employing Kaolin and Levasil® (Kurt Obermeier
 GmbH & Co. KG, Bad
 Berleburg, Germany). Until further processing, samples were
 stored at -25°C in absolute ethanol.
 Animals were incubated for 5 minutes in 2µl of a matrix solution
 containing α-Cyano-4-hydroxycinnamic acid (HCCA) as a saturated solution
 in 50% acetonitrile, 47.5% molecular grade water and 2.5% trifluoroacetic
 acid. Entire solution was applied to target plates for measurements in
 a Microflex LT/SH System (Bruker Daltonics). Masses were measured from 2k
 to 20k Dalton. For peak evaluation, mass peak range from 2k to 10k Dalton
 was analyzed using a centroid peak detection algorithm, a signal to noise
 threshold of 2 and a minimum intensity threshold of 600, with a peak
 resolution higher than 400. Proteins/Oligonucleotide method was employed
 for fuzzy control with a maximal resolution ten times above the threshold.
 For a sum spectrum, 240 satisfactory shots were summed up. One mass
 spectrum was measured for each specimen. Mass
 spectrometry data was processed together in R (version 3.2.3, R Core Team
 (2018) using packages ‘MALDIquant’ (Gibb and Strimmer, 2012) and
 ‘MALDIquantForeign’ (Gibb, 2015). Protein mass spectra were trimmed to an
 identical range from 2,000 to 20,000 m/z and smoothed with the
 Savitzky-Golay method (Savitzky and Golay, 1964). The baseline was removed
 based on SNIP baseline estimation method (Ryan et al., 1988) and spectra
 were normalized using the TIC method implemented in MALDIquant. Noise
 estimation was carried out with a signal to noise ratio (SNR) of 7. Peaks
 were repeatedly binned using command ‘binpeaks’ with a tolerance of 0.002
 in a strict approach to the number of peaks for the whole data set was
 reduced from 9344 to 652 peaks. The resulting intensity matrix was
 Hellinger transformed (Legendre and Gallagher, 2001).
 Both processed data in a Hellinger transformed matrix and raw
 measurements are stored here at Dryad.