added riccia-chemotax use-case

README.md

@@ -1,13 +1,14 @@
 # iESTIMATE computational analysis framework for Eco-Metabolomics data
 iESTIMATE = Identifying ESsenTIal Molecular vAriables in Terrestrial Ecology
 
-Version: 0.3
+Version: 0.4
 
 ## Use-cases
 The iESTIMATE framework has been developed to be applied to eco-metabolomics studies to answer a wide range of research questions. The following use-cases demonstrate the power of the framework:
 - [Impact of hormone treatments on the metabolome of Radula complanata](https://github.com/ipb-halle/iESTIMATE/blob/main/use-cases/radula-hormones/)
 - [Host Tree and Geography Induce Metabolic Shifts in the Epiphytic Liverwort Radula complanata](https://github.com/ipb-halle/iESTIMATE/blob/main/use-cases/radula-environmental/)
 - [Chemical Diversity and Classification of Secondary Metabolites in Nine Bryophyte Species](https://github.com/ipb-halle/iESTIMATE/tree/main/use-cases/mtbls709)
+- [Investigating untargeted metabolomics for its use in integrative taxonomy - Linking metabolomics, DNA marker-based se-quencing and bioimaging of phenotypes](https://github.com/ipb-halle/iESTIMATE/tree/main/use-cases/riccia-chemotaxonomy)
 
 ## TODO
 - modularize
@@ -16,6 +17,10 @@ The iESTIMATE framework has been developed to be applied to eco-metabolomics stu
 
 ## History
 
+### Version 0.4:
+- added riccia-chemotaxonomy study
+- updated _functions.r
+
 ### Version 0.3:
 - added radula-environmental study
 

use-cases/riccia-chemotaxonomy/README.md

@@ -0,0 +1,14 @@
+# Use-case: Investigating untargeted metabolomics for its use in integrative taxonomy – Linking metabolomics, DNA marker-based se-quencing and bioimaging of phenotypes
+Kristian Peters, Kaitlyn Blatt-Janmaat, Natalia Tkach, Nicole M. van Dam and Steffen Neumann
+
+## Raw data
+Raw metabolomics data has been deposited in MetaboLights and is available under the identifier MTBLS4668.
+
+## Description
+Integrative taxonomy is a fundamental part of biodiversity and combines traditional morphology with additional methods like DNA sequencing or biochemistry. Here, we aim to establish untargeted metabolomics for use in chemotaxonomy. We use three thallose liverwort species Riccia glauca, R. sorocarpa and R. warnstorfii (order Marchantiales, Ricciaceae) and Lunularia cruciata (order Marchantiales, Lunulariacea) as outgroup. Liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) are inte-grated with DNA-marker based sequencing of the trnL-trnF region and high-resolution bioim-aging. Our untargeted chemotaxonomy methodology allows to distinguish taxa based on che-mophenetic 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 phyloge-netic 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.
+
+## R-Vignette
+- A vignette to recreate the plots used in the paper has been made available in [Zenodo](https://doi.org/10.5281/zenodo.7596018).
+
+## Publications
+TODO