Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
140
datasets available to search
ShareScore release 0.7.1
Dataset results
140 results for “secondary metabolite”
S75 | CyanoMetDB | Comprehensive database of secondary metabolites from cyanobacteria
<p>This is the collection associated with list S75 CyanoMetDB Comprehensive database of secondary metabolites from cyanobacteria on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>CyanoMetDB is a comprehensive database of secondary metabolites from cyanobacteria manually curated from primary references described in Jones et al (2021), DOI: <a href="https://doi.org/10.1016/j.watres.2021.117017">10.1016/j.watres.2021.117017</a> (preprint DOI: <a href="https://doi.org/10.1101/2020.04.16.038703">10.1101/2020.04.16.038703</a>). This upload contains the 2024 release. Please cite Jones et al (2021) DOI: <a href="https://doi.org/10.1016/j.watres.2021.117017">10.1016/j.watres.2021.117017</a> and this record Janssen et al (2024) DOI: <a href="https://doi.org/10.5281/zenodo.13854577">10.5281/zenodo.13854577</a> when using this CyanoMetDB Version 3!</p> <p><em><strong>Contents: </strong></em></p> <p><em><strong>CyanoMetDB XLSX database (2024 release): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_Version03.xlsx?download=1">CyanoMetDB_Version03.xlsx</a></strong></em></p> <p>Additional files for workflows:</p> <p>CSV format: <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024.csv?download=1">CyanoMetDB_V03_2024.csv</a><br>MetFrag local CSV file (original database abridged and reformatted for use in MetFrag): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024_MetFrag.csv?download=1">CyanoMetDB_V03_2024_MetFrag.csv</a><a href="https://zenodo.org/api/files/7d71e4a9-e5f2-4ca3-ac55-6467a356ab9a/CyanoMetDB_MetFrag_Feb2021.csv"> </a><br>Additional files for matching InChIKeys (rapid suspect flagging): <a href="https://zenodo.org/records/13854577/files/CyanoMetDB_V03_2024_InChIKeys.txt?download=1">CyanoMetDB_V03_2024_InChIKeys.txt</a></p> <p>Corresponding author: Elisabeth Janssen (Eawag): <a href="mailto:Elisabeth.Janssen@eawag.ch">Elisabeth.Janssen@eawag.ch</a></p>
Supplementary data of article "Domestication has altered gene expression and secondary metabolites in pea seed coat".
<p><strong>Table S1.</strong> Excel- GO_terms_MF_selected_WGCNA_modules.</p> <p><strong>Table S2.</strong> Excel- GO_terms_MF_DEGs_UP_and_DOWN.</p> <p><strong>Table S3.</strong> Excel- GO_terms_MF_DEGs_summary.</p> <p><strong>Table S4.</strong> Excel- List of DEGs involved in flavonoid pathway found in WILD gene set.</p> <p><strong>Table S5.</strong> Protein recoveries calculated for individual pea protein samples. Numbers 1, 2, 3 denote treatment groups corresponding to seed developmental stages (D1, D2 and mature seeds, respectively). Letters a–d denote biological replicates within the treatment groups.</p> <p><strong>Table S6.</strong> Excel- Annotation of proteins differentially expressed in wild and domesticated pea seed coat samples.</p> <p><strong>Table S7.</strong> Primary metabolites identified by spectral similarity library search and/or co-elution with authentic standards in pea seed coats aqua methanolic extracts. Metabolite analysis relied on GC-EI-Q-MS analysis after derivatization of the lyophilized extracts with methoxamine hydrochloride (MOA) and <em>N</em>-methyl-<em>N</em>-(trimethylsilyl)trifluoroacetamide (MSTFA).</p> <p><strong>Table S8.</strong> Primary metabolites detected in the aq. methanolic extracts of mature Cameor seed coats demonstrating statistically significant up- and down-regulation in comparison to those of wild JI261.</p> <p><strong>Table S9.</strong> Primary metabolites of mature JI92 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of wild JI261.</p> <p><strong>Table S10.</strong> Primary metabolites of mature JI1794 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S11.</strong> Primary metabolites of mature JI64 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S12.</strong> Mass analyzer settings applied for QqTOF-MS experiments in analysis of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S13.</strong> Cell wall-bound metabolites extracted from the seed coats of wild (JI64, JI1794, JI261) and domesticated (Cameor, JI92) peas upon alkali hydrolysis of corresponding isolated and purified cell wall material.</p> <p><strong>Table S14.</strong> Excel- Coordinates of markers in S-plot obtained from OPLS-DA analysis (FIA-ESI-HRTMS, negative ionization, lock mass uncorrected).</p> <p><strong>Table S15.</strong> List of identified significantly differential metabolites rising during seed coat development.</p> <p><strong>Table S16.</strong> List of identified significantly differential metabolites decreasing during seed coat development (positive ionization mode).</p> <p><strong>Table S17.</strong> List of identified metabolites with significantly higher content in wild compared cultivated genotypes in older developmental stages (D5-6).</p> <p><strong>Table S18.</strong> Excel- Expression of genes encoding enzymes of monolignol pathway in seed coats (SC) and embryos (E) of domesticated (Cameor, JI92 and <em>Pisum abyssinicum</em> PI358617) and wild (JI64, JI1794, JI261) peas over five seed developmental stages (13, 17, 20, 23, 28 DAP, labelled as 1-5). PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, 4CL: 4-coumaroyl: CoA ligase, HCT: hydroxycinnamoyl CoA:shikimate hydroxycinnamoyltransferase, COMT: caffeic acid O-methyltransferase, CSE: caffeoyl shikimate esterase, CAD: cinnamyl alcohol dehydrogenase, CCR: cinnamoyl CoA reductase, CCoAMT: caffeoyl CoA-3-methyltransferase, F5H: ferulate-5-hydroxylase</p> <p><strong>Table S19.</strong> Studied metabolites of phenylpropanoid pathway.</p> <p><strong>Table S20.</strong> Instrument settings used in the proteomics LIT-Orbitrap-MS and -MS/MS experiments.</p> <p><strong>Table S21.</strong> Procedures and specific settings for data processing and post-processing of the proteomics data.</p> <p><strong>Table S22.</strong> Gas chromatographic (GC) separation conditions and electron ionization-quadrupole-mass spectrometry (EI-Q-MS) settings for GC-EI-Q-MS analysis of the primary metabolites in pea seed coats.</p> <p><strong>Table S23.</strong> Chromatographic conditions used for UHPLC separation of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S24.</strong> Variable parameters of MS/cIMS/MS measurements.</p> <p><strong>Figure S1.</strong> The dynamics of gene expression between studied developmental stages within all genotypes (a) or among genotypes in particular developmental stages (b).</p> <p><strong>Figure S2.</strong> Twelve representative groups of transcription factors described within 20 gene modules of pea SC. Visualized by Cytoscape 3.9.0.</p> <p><strong>Figure S3.</strong> SDS-PAGE electropherograms of the total protein fractions isolated from the seed coats of JI92 (a, c, e) and JI64 (b, d, f) seeds before and after tryptic hydrolysis. Numbers 1, 2, 3 denote seed developmental stages: DS1, DS2 and mature seeds, respectively. Letters a-d denote biological replicates. The aliquots (10 μg) of samples before hydrolysis (a, b), the incompletely digested aliquots left on filter unit after peptide elution (c, d) and aliquots of tryptic hydrolysates (corresponding to 5 μg of protein), (e, f) were loaded on gels. Inter-gel normalization relied on the total density of the Protein Ladder (PageRuler™ Prestained Protein Ladder #26616, 10–180 kDa) lane (St); the ND (non-digested) sample represents a reference protein not subjected to hydrolysis.</p> <p><strong>Figure S4.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups) (c) identified in domesticated JI92 seed coats at developmental stages D1, D2 and D6. The tryptic digests (<em>n</em> =&thinsp;3) obtained from seed coats were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S5.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups, c) identified in wild pea JI64 seed coats at D1, D2 and D6 stages. The tryptic digests (<em>n</em> =&thinsp;3), obtained from pea seedlings, were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S6.</strong> Principal component analysis (PCA) with score plot representation (a) accomplished for seed coat proteins differentially expressed at developmental stages D1 and D2 and in the mature state (D6) and hierarchical clustering with a heatmap representation (b).</p> <p><strong>Figure S7.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D1. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S8.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D2. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of the domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S9.</strong> Prediction of sub-cellular localization of the proteins more expressed in the developing seeds of JI92 and JI64 with the BUSCA prediction tool.</p> <p><strong>Figure S10.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated Cameor by principal component analysis (PCA).</p> <p><strong>Figure S11.</strong> Representation of the differences in the metabolic profiles of the mature seed coats obtained from the wild JI261 and domesticated Cameor by the t-test with Volcano plot representation (a) and the top 30 differentially abundant metabolites demonstrating the most pronounced differences of corresponding GC-MS signals associated with seed dormancy (b).</p> <p><strong>Figure S12.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated JI92 by principal component analysis (PCA) with score plot representation (a) and hierarchical clustering with heatmap representation (b).</p> <p><strong>Figure S13.</strong> Principal component analysis (PCA) illustrating distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261.</p> <p><strong>Figure S14.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild pea genotypes, JI64 and JI261(control).</p> <p><strong>Figure S15.</strong> Evaluation of the differences in the patterns of the cell wall-bound metabolites obtained from mature seed coats of wild JI261 and domesticated Cameor: principal component analysis (PCA) with score plot representation (a), hierarchical clustering with heatmap representation (b) and <em>t</em>-test analysis with the Volcano-plot representation (c).</p> <p><strong>Figure S16.</strong> Statistical analysis (<em>t</em>-test with Volcano plot representation) characterizing the differences between the levels of mature seed coat cell wall-bound metabolites of Cameor compared with those of wild JI261.</p> <p><strong>Figure S17.</strong> Principal component analysis (PCA) illustrates the distribution of mature seed coat metabolic profiles of domesticated JI92 and wild JI261.</p> <p><strong>Figure S18.</strong> Principal component analysis (PCA) shows the distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261, control.</p> <p><strong>Figure S19.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild genotypes, JI64 and control JI261.</p> <p><strong>Figure S20.</strong> Annotated cell wall-bound metabolites extracted from the seed coats of the dormant wild pea genotype JI261 and the seed coats from several pea genotypes varying in their dormancy (Cameor, JI92, JI64, and JI1794) upon alkali hydrolysis of corresponding isolated and purified cell wall material. </p> <p><strong>Figure S21.</strong> Ion mobility separation of <em>m/z</em> 299.0841.</p> <p><strong>Figure S22.</strong> Ion mobility separation of <em>m/z</em> 701.1907. </p> <p><strong>Figure S23.</strong> Ion mobility separation of <em>m/z</em> 619.1041.</p> <p><strong>Figure S24.</strong> Ion mobility separation of <em>m/z</em> 631.1017.</p> <p><strong>Figure S25.</strong> Ion mobility separation of <em>m/z</em> 641.1139.</p> <p><strong>Figure S26.</strong> Ion mobility separation of <em>m/z</em> 771.1346. </p> <p><strong>Figure S27.</strong> Reconstructed chromatograms of p-hydroxybenzoic and salicylic acids in DS5 of dormant JI64 and domesticated landraces JI92 (LC/HRTMS, negative ionization mode).</p> <p><strong>Figure S28.</strong> Module-trait relationship depiction showing the correlation between expression of the gene modules and the abundance of identified metabolites of the monolignol pathway.</p>
Data from: Plant host traits mediated by foliar fungal symbionts and secondary metabolites
<p>Fungal symbionts living inside plant leaves ("endophytes") can vary from beneficial to parasitic, but the mechanisms by which the fungi affect the plant host phenotype remain poorly understood. Chemical interactions are likely the proximal mechanism of interaction between foliar endophytes and the plant, as individual fungal strains are often exploited for their diverse secondary metabolite production. Here, we go beyond single strains to examine commonalities in how 16 fungal endophytes shift plant phenotypic traits such as growth and physiology, and how those relate to plant metabolomics profiles. We inoculated individual fungi on switchgrass, <em>Panicum virgatum</em> L. This created a limited range of plant growth and physiology (2–370% of fungus-free controls on average), but effects of most fungi overlapped, indicating functional similarities in unstressed conditions. Overall plant metabolomics profiles included almost 2000 metabolites, which were broadly correlated with plant traits across all the fungal treatments. Terpenoid-rich samples were associated with larger, more physiologically active plants and phenolic-rich samples were associated with smaller, less active plants. Only 47 metabolites were enriched in plants inoculated with fungi relative to fungus-free controls, and of these, LASSO regression identified 12 metabolites that explained from 14–43% of plant trait variation. Fungal long-chain fatty acids and sterol precursors were positively associated with plant photosynthesis, conductance, and shoot biomass, but negatively associated with survival. The phytohormone gibberellin, in contrast, was negatively associated with plant physiology and biomass. These results can inform ongoing efforts to develop metabolites as crop management tools, either by direct application or via breeding, by identifying how associations with more beneficial components of the microbiome may be affected.</p>
Secondary metabolites from nectar and pollen: a resource for ecological and evolutionary studies
<p>Floral chemistry mediates plant interactions with herbivores, pathogens, and pollinators. The chemistry of floral nectar and pollen—the primary food rewards for pollinators—can affect both plant reproduction and pollinator health. Although the existence and functional significance of nectar and pollen secondary metabolites has long been known, comprehensive quantitative characterizations of secondary chemistry exist for only a few species. Moreover, little is known about intraspecific variation in nectar and pollen chemical profiles. Because the ecological effects of secondary chemicals are dose-dependent, heterogeneity across genotypes and populations could influence floral trait evolution and pollinator foraging ecology. To better understand within- and across-species heterogeneity in nectar and pollen secondary chemistry, we undertook exhaustive LC-MS and LC-UV-based chemical characterizations of nectar and pollen methanol extracts from 31 cultivated and wild plant species. </p> <p>Nectar and pollen were collected from farms and natural areas in Massachusetts, Vermont, and California, USA, in 2013 and 2014. For wild species, we aimed to collect 10 samples from each of 3 sites. For agricultural and horticultural species, we aimed for 10 samples from each of 3 cultivars. Our dataset (1535 samples, 102 identified compounds) identifies and quantifies each compound recorded in methanolic extracts, and includes chemical metadata that describe the molecular mass, retention time, and chemical classification of each compound. A reference phylogeny is included for comparative analyses.</p> <p>We found that each species possessed a distinct chemical profile; moreover, within species, few compounds were found in both nectar and pollen. The most common secondary chemical classes were flavonoids, terpenoids, alkaloids and amines, and chlorogenic acids. The most common compounds were quercetin and kaempferol glycosides. Pollens contained high concentrations of hydroxycinnamoyl-spermidine conjugates, mainly triscoumaroyl and trisferuloyl spermidine, found in 71% of species. When present, pollen alkaloids and spermidines had median nonzero concentrations of 23,000 µM (median 52% of recorded micromolar composition). Although secondary chemistry was qualitatively consistent within each species and sample type, we found significant quantitative heterogeneity across cultivars and sites. These data provide a standard reference for future ecological and evolutionary research on nectar and pollen secondary chemistry, including its role in pollinator health and plant reproduction.</p>
Fig. 3 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 3. Colony forming curves of culturable bacteria in soil microcosms harvested after 1, 7, and 14 days on non-selective agar media. For each harvest event the same plates were counted repeatedly. Statistical significant differences between the treatments at the last counting event of each harvest are indicated by different letters.
Fig. 4 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 4. Abundance of culturable protozoa in the four different soil microcosms. The protozoa were counted by MPN as fast-growing protozoa after 1 week of incubation and as total protozoa after 3 weeks of incubation by inspecting the same plates twice. Significant differences of treatments within each sampling time and incubation time are shown as different small letters above the bars. After one day protozoa was only counted in the control microcosm. Significant differences between the abundance of protozoa in the control microcosm are shown as capital letters. bd: below detection limit of 157 protozoa g–1 dw. nd: not determined.
Fig. 2 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 2. Fate of inoculated P. fluorescence CHA0/gfp1 and P. fluorescens CHA0/pME3424 during incubation in soil microcosms determined as CFU on selective agar media (see Materials and Methods for selective agents). The individual data points for each replicate are shown along with the linear regression line for each strain.
Fig. 1 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 1. Soil respiration measured as accumulated CO 2 during the incubation of microcosms consisting of soil, shredded barley straw and either of three bacterial inoculants: E. aerogenes, P. fluorescens CHA0/gfp1, P. fluorescens CHA0/pME3424. Control treatment did not receive any bacteria.
Fig. 2 in Anticoccidial activity of the secondary metabolites in alpine plants frequently ingested by wild Japanese rock ptarmigans
Fig. 2. The efficacy of the natural components against E. tenella sporozoites. The viability of sporozoites was determined at various concentrations of the compounds that showed effectiveness at 100 μM. The half maximal inhibitory concentration (IC50) value was determined from the approximate curves obtained from these results. SPZ: sporozoite.
Fig. 1 in Anticoccidial activity of the secondary metabolites in alpine plants frequently ingested by wild Japanese rock ptarmigans
Fig. 1. Direct effects of the natural components derived from alpine plants on E. tenella sporozoites. The viability of sporozoites treated with each natural component derived from alpine plants or lasalocid (positive control) with the viability of the DMSO-treated group set as 100%. The final concentration was 100 μM for the natural components, and 1 μM for lasalocid. SPZ: sporozoite; Las: lasalocid. Outliers were tested using Thompson's test (p <0.05), and the student's t-test was utilized to compare the data with the DMSO-treated group as a control (**p <0.01, ***p <0.001, ****p <0.0001).
Fig. 3 in Anticoccidial activity of the secondary metabolites in alpine plants frequently ingested by wild Japanese rock ptarmigans
Fig. 3. Confirmation of the active compounds using commercially available compounds and their efficacy. (A) The viability of sporozoites treated with each commercially available compound or lasalocid (positive control) was compared to the viability of the DMSO-treated group, which was set as 100%. The final concentration was 100 μM for the synthetic compounds, and 1 μM for lasalocid. SPZ: sporozoite, Las: lasalocid. The student's t-test was used for the comparisons (****p <0.0001) without outliers, as tested using Thompson's test (p <0.05). (B) The viability of sporozoites was determined at each concentration of the synthetic compounds that showed effectiveness at 100 μM. The half maximal inhibitory concentration (IC50) value was determined by approximating the curves obtained from the results.
Fig. 4 in Anticoccidial activity of the secondary metabolites in alpine plants frequently ingested by wild Japanese rock ptarmigans
Fig. 4. The inhibitory effects of the natural components derived from alpine plants on sporozoite cell invasion. The invasion rate of sporozoites treated with each natural component derived from alpine plants or lasalocid (positive control) with the viability of the DMSO-treated group set as 100%. Each compound was used at its maximum non-toxic concentration. Las: lasalocid. The student's t-test was utilized to compare the data with the DMSO-treated group as a control (**p <0.01, ***p <0.001, ****p <0.0001). Outliers were identified and removed using Thompson's test (p <0.05).
Integrated Omics-Based Discovery of Novel Genes, Secondary Metabolites Clusters, and Small Molecules in Penicillium spp. with Disparate Fungal Isolates
<p><em><span>Penicillium expansum</span></em><span> is a ubiquitous postharvest pathogen of pome fruit that causes blue mold decay of apple fruit while another member of the genus, <em>P. chrysogenum</em><span>,</span><em> </em>is a well-studied saprophyte used for antibiotic and small molecule production. While these two fungi have been investigated individually, the recent discovery of <em>P. chrysogenum </em>hindering <em>P. expansum</em> apple fruit infection has not been well studied. To shed light on this interaction between the two species, we conducted a comparative transcriptomic, metabolomic, and genomic study. Global transcriptional and metabolomic outputs were disparate between the species, nearly identical for the <em>P. chrysogenum </em>isolates, and different between <em>P. expansum </em>isolates. Further, the two <em>P. chrysogenum</em> genomes revealed secondary metabolite gene clusters that differed from <em>P. expansum</em>. This included the absence of an intact patulin gene cluster in <em>P. chrysogenum</em>, which corroborates the metabolomic data regarding the species’ inability to produce patulin. Additionally, <em>P. expansum </em>virulence gene homologues were identified in <em>P. chrysogenum </em>and were similarly transcriptionally regulated <em>in vitro</em>. Molecules with potential antimicrobial activity, and phytohormones like indole-3-acetic acid (IAA), were detected for the first time in <em>P. expansum</em> while pharmacological compounds like the well-studied antibiotic penicillin G were identified in <em>P. chrysogenum</em> culture supernatants. Our findings provide new omics-based resources that enable the study of small molecule production of interest, the potential of <em>Penicillium</em>-derived antimicrobials for postharvest decay control, and <em>P.</em> <em>expansum’s</em> metabolites roles in host-pathogen interactions. </span></p>
FIG. 15 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 15. — Cladonia novochlorophaea (Sipman) Brodo & Ahti: A, habit (BP[BP 9314]); B, spots of lichen secondary metabolites on chromatographic plates; C, distribution in Hungary. Abbreviations: H, homosekiaic acid; F, fumarprotocetraric acid; Z, zeorin; N, norstictic acid. Scale bar: A, 2 mm.
FIG. 8 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 8. — The mean diameter of soredia (µm) measured on podetia (n = 10). Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
FIG. 5 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 5. — Height of cup (mm) in different species. Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
FIG. 9 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 9. — Conditional inference tree presenting the five most important morphological variables separating species: CH, height of cup; CW, width of cup; PH, height of podetium; SW, width of podetium stalk. The order of the species at the end of the nodes is as follows: a, C. asahinae; c, C. chlorophaea; cr, C. cryptochlorophaea; g, C. grayi; m, C. merochlorophaea; n, C. novochlorophaea. Boxes represent the highest probability of a species occurrence on the tree node. A level of p <0.05 was considered for a significant difference.
FIG. 12 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 12. — Cladonia cryptochlorophaea Asahina: A, habit (BP[BP 48938]); B, spots of lichen secondary metabolites on chromatographic plates; C, distribution in Hungary. Abbreviations: c, cryptochlorophaeic acid; nR, norrangiformic acid; F, fumarprotocetraric acid; Z, zeorin; N, norstictic acid. Scale bar: A, 2 mm.
FIG. 7 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 7. — Width of podetium stalk (mm) in different species. Abbreviations: asa, C. asahinae (n = 22); chlo, C. chlorophaea (n = 55); cry, C. cryptochlorophaea (n = 53); gra, C. grayi (n = 17); mero, C. merochlorophaea (n = 70); novo, C. novochlorophaea (n = 10). The lines represent the minimum and maximum values, the box represents the 25% and 75% of the data, the thick line represents the median. Means with the same letter are not significantly different at 95% confidence.
FIG. 11 in Analysis of lichen secondary metabolites and morphometrics in the Cladonia chlorophaea species group (Cladoniaceae, lichenized Ascomycota) in Hungary
FIG. 11. — Cladonia chlorophaea (FlÖrke ex Sommerf.) Spreng.: A, habit (BP[BP 49014]); B, spots of lichen secondary metabolites on chromatographic plates; C, distribution in Hungary. Abbreviations: F, fumarprotocetraric acid; Z, zeorin; N, norstictic acid. Scale bar: A, 2 mm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.