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ABSTRACT: A reference-based taxonomy reinforces the evolutionary relationships of previously problematic earless lizards (Phyrnosomatidae: Holbrookia)

PROVIDER: PRJNA894589 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR22058000.fastq.gz	Fastqsanger.gz
	SRR22058001.fastq.gz	Fastqsanger.gz
	SRR22058002.fastq.gz	Fastqsanger.gz
	SRR22058003.fastq.gz	Fastqsanger.gz
	SRR22058004.fastq.gz	Fastqsanger.gz

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Project description:Integrative taxonomy is a fundamental part of biodiversity and combines traditional morphology with additional methods such as DNA sequencing or biochemistry. Here, we aim to establish untargeted metabolomics for use in chemotaxonomy. We used three thallose liverwort species Riccia glauca, R. sorocarpa and R. warnstorfii (order Marchantiales, Ricciaceae) with Lunularia cruciata (order Marchantiales, Lunulariacea) as an outgroup. Liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) were integrated with DNA marker-based sequencing of the trnL-trnF region and high-resolution bioimaging. Our untargeted chemotaxonomy methodology enables us to distinguish taxa based on chemophenetic markers at different levels of complexity: (1) molecules, (2) compound classes, (3) compound superclasses and (4) molecular descriptors. For the investigated Riccia species, we identified 71 chemophenetic markers at the molecular level, a characteristic composition in 21 compound classes, and 21 molecular descriptors largely indicating electron state, presence of chemical motifs and hydrogen bonds. Our untargeted approach revealed many chemophenetic markers at different complexity levels that can provide more mechanistic insight into phylogenetic delimitation of species within a clade than genetic-based methods coupled with traditional morphology-based information. However, analytical and bioinformatics analysis methods still need to be better integrated to link the chemophenetic information at multiple scales. To characterize, classify and name species, taxonomy is a fundamental part of biodiversity research. Integrative taxonomy combines traditional morphology-based methods with additional methods from different disciplines like sequencing. Bioinformatics analysis methods and research data are becoming increasingly important but greater integration is needed to link the information at multiple scales. Here, we present a reference dataset that investigates the principles of integrating metabolomics, sequencing, and phenotypic data into integrative taxonomy.

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