DOI: 10.5061/dryad.7pvmcvdzf
title: Data from: A universal tool for marine metazoan species identification – Towards best practices in proteomic fingerprinting

description: Proteomic fingerprinting using MALDI-TOF mass spectrometry is a
 well-established tool for identifying microorganisms and has shown
 promising results for identification of animal species, particularly
 disease vectors and marine organisms. However, few studies have tested
 species identification across different orders and classes. In this study,
 we collected data from 1,246 specimens and 198 species to test species
 identification in a diverse dataset. We also evaluated different specimen
 preparation and data processing approaches for machine learning and
 developed a workflow to optimize classification using random forest. Our
 results showed high success rates of over 90%, but we also found that the
 size of the reference library affects classification error. Additionally,
 we demonstrated the ability of the method to differentiate marine
 cryptic-species complexes and to distinguish sexes within species.
description: Tissue for measurements was taken mainly from the marine
 organisms tissue bank of the Senckenberg am Meer, German Centre for Marine
 Biodiversity Research, which was established using samples from numerous
 studies (Knebelsberger and Thiel, 2014; Knebelsberger et al., 2014;
 Markert et al., 2014; Gebhardt and Knebelsberger, 2015; Raupach et al.,
 2015; Barco et al., 2016; Laakmann et al., 2016; Rossel et al., 2020b)
 (supplementary table S1 for accession numbers) on North Sea metazoans. The
 material from this collection was taken from specimens processed for
 COI-barcoding to create reference libraries for a variety of marine animal
 groups. During this process, tissue samples of the respective specimens
 were stored in ethanol at -80°C. Tissue samples were available for
 Bivalvia (muscle, 18 species), Cephalopoda (muscle from arm, 12 species),
 Gastropoda (muscle from foot, 24 species), Polyplacophora (muscle from
 foot, 2 species), Ascidiacea (tissue, 1 species), Teleostei (muscle, 67
 species), Elasmobranchii (muscle, 7 species), Malacostraca (muscle from
 foot or chelae, 39 species), Thecostraca (muscle from foot, 1 species),
 Pycnogonida (leg fragment, 1 species), Asteroidea (tube feet, 10 species),
 Ophiuroidea (tissue from arm, 10 species) and Echinoidea (tissue from the
 base of the tubercle, 6 species) (n<sub>species</sub>= 198,
 n<sub>specimens</sub>=1,246). Sample
 preparation The
 basic protocol of sample preparation was the same for all analyzed tissue
 samples. A very small tissue fragment (&lt; 1
 mm<sup>3</sup>) was incubated for 5 minutes in
 α-cyano-4-hydroxycinnamic acid (HCCA) as a saturated solution in 50%
 acetonitrile, 47.5% molecular grade water and 2.5% trifluoroacetic acid.
 Tissue from crustacean <em>Cancer pagurus</em> Linnaeus, 1758,
 the fish <em>Clupea harengus</em> Linnaeus, 1758, the
 cephalopod <em>Eledone cirrhosa</em> (Lamarck, 1798) and the
 echinoderm <em>Stichastrella rosea</em> (O.F. Müller, 1776)
 was used to find an optimal tissue to HCCA matrix ratio. Tissue was
 weighted on a METTLER TOLEDO XS3DU micro-balance and the amount of matrix
 was adjusted to tissue weight to obtain the desired ratios ranging from
 0.012 µg µl<sup>-1</sup> to 200 µg
 µl<sup>-1</sup>. After incubation, 1.5 µl of the solution was
 transferred to 10 spots on a target plate, respectively. Mass spectra were
 measured with a Microflex LT/SH System (Bruker Daltonics) using method
 MBTAuto. Peak evaluation was carried out in a mass peak range between 2 k
 – 10 k Dalton (Da) using a centroid peak detection algorithm, a signal to
 noise threshold of 2 and a minimum intensity threshold of 600. To create a
 sum spectrum, 160 satisfactory shots were summed
 up. Resulting from observations during this initial
 test, a fast applicable protocol was developed without the need to weigh
 each tissue sample. Matrix volume was added to tissue samples depending on
 tissue volume, i.e. tissue samples were always completely covered by HCCA
 matrix with a small layer (ca. 1 mm) of supernatant. Samples were
 incubated for 5 minutes and 1.5 µl of the solution were transferred to a
 single spot on a target plate for measurement. Each spot was measured
 between two to three times.
description: Data can be analyzed using Bruker proprietary software such as
 Bruker Flex analysis or Bruker Biotyper. Alternatively,
 data can be analyzed in R using R-packages MaldiQuant and
 MaldiQuantForeign following the vignette that can be found via:
 https://cran.r-project.org/web/packages/MALDIquant/index.html
description: # Data from: Proteomic fingerprinting as universal tool for species
 identification in marine metazoans on the road to best practices The data
 relates to the publication Proteomic fingerprinting as universal tool for
 species identification in marine metazoans on the road to best practices
 in Nature Scientific Reports. DOI:*10.1038/s41598-024-51235-z* This
 dataset contains an Excel file called ReferenceTable.csv and a .zip file
 containing raw Bruker mass spectrometry files for 1246 measurements from a
 variety of marine species. For each specimen, one folder is included in
 the .zip file. These files are named according to specimen names in the
 Excel file. Furthermore, the excel file contains information on
 taxonomical classification of each specimen. The .zip file contains a
 folder named Dryad in which folders for the 1246 specimens are deposited.
 These again containing folders for each specimen. The "specimen
 folders" are build up according to the default Bruker flex container
 system and contain one folder for each spot that was measured for a
 specimen. Each "spot folder" consists of folders for each
 technical replicate measurement on the respective spot. Within these
 folders the actual data produced by the instrument can be found. Data can
 be analyzed using Bruker proprietary software such as Bruker Flex analysis
 or Bruker Biotyper. Alternatively, data can be analyzed in R using
 R-packages MaldiQuant and MaldiQuantForeign following the vignette that
 can be found via:
 
 Additionally, the dataset contains three .R files. These are R scripts
 that as they were used in the referenced study. The file Workflow.R
 contains a script for default data processing in R to produce a data
 matrix that can be used for further analyses. The file
 Batch_script_Classification_posthoc_parallel.R contains a script for
 testing classification by discarding a specimen from the data and then
 have it classified using the remaining data. On the classification a
 post-hoc test is then applied. The script could be modified for
 classification on higher taxonomic level. This script is specifically
 created for parallel processing on a larger server. The file Data
 optimization.R is a script to vary Baseline iterations as well as
 halfWindowSize and signal to noise ratio during peak detection. In the
 end, the script creates a random forest model and saves the OOB error. The
 script is designed for parallel processing and can be used on a larger
 serve and allows the analyses of several variations at once.