Find research datasets worth reusing

opencc-by-4.0Dec 2024View details →

Untargeted metabolomics analysis of RPE cells during six month in culture

Primary RPE cell cultures were established with human fetal RPE cells acquired from ScienCell (Cat. No, 6540) seeded at passage 3 (P3) in 12-well Transwell® inserts (Corning Inc., Cat. No. CLSS3460) coated with 2% v/v Geltrex® matrix (Thermo Fisher Scientific; Cat. No. A1413202). Cells were cultured for a total time of 6 months (25 weeks), following the same protocol as previous works (16,59). Samples were acquired for protein immunolocalization, transcriptomic and metabolomics analysis from independent cell cultures along the total 6 months culture time (specifically at 4, 12, 17 and 25 weeks in culture), collecting 3 biological replicates per type of analysis and time point. Cell metabolites were quenched and extracted using MeOH:H2O (80:20, -20ºC) containing a spiked solution of isotopically enriched low molecular mass internal standard. Quality control (QC) samples were prepared by creating a pool of equal volumes from each biological replicate and were analyzed after a blank solution every fifth sample to monitor the performance, stability, and reproducibility of the LC-MS run. A liquid chromatography (LC) system 1290 Infinity II (Agilent Technologies) was coupled to an Agilent 6560B Ion Mobility quadrupole-time-of-flight mass spectrometer (IM-QTOF-MS) equipped with an Agilent G1607A dual jetstream ESI source and the MassHunter WorkSation 11.0. A reference solution containing purine and hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazene for mass correction was added using the second ESI source. Chromatographic, ion source and MS conditions were optimized using QCs and selected parameters are shown in Supporting Information. MS analysis was conducted with an untargeted approach, operating the instrument in both positive and negative ionization modes. All samples were analysed in a randomized order. Raw data was processed using the software Profinder B10.00 (Agilent) and refined data were exported as CEF files. Data were converted to mzml files using MS convert.

opencc-by-4.0Sep 2024View details →

vPro-MS peptide spectral library for the identification of human-pathogenic viruses by untargeted proteomics

The viral proteomics workflow (vPro-MS) enables identification of human-pathogenic viruses from patient samples by untargeted proteomics. vPro-MS is based on an in-silico derived peptide library covering the human virome in <a href="https://www.uniprot.org/" rel="nofollow">UniProtKB</a> (331 viruses, 20,386 genomes, 121,977 peptides). vPro-MS is intended to identify human-pathogenic viruses from DiaNN (<a href="https://github.com/vdemichev/DiaNN">https://github.com/vdemichev/DiaNN</a>) outputs of either DIA or diaPASEF data. A scoring algorithm (vProID) assesses the confidence of virus identification and the results are finally summarized in a report table.  The vPro Peptide Library folder contains 3 peptide FASTA files (Contaminants.fasta, Human.fasta, vPro.Virus.fasta), which were used to predict the spectral library (vPro-lib.predicted.speclib). Please note, that the additional commands “--cut” and “--duplicate-proteins” are needed to reprocess the prediction in DiaNN. This spectral library should be used to identify peptide sequences from samples of human origin using DiaNN. Furthermore, the folder contains the metadata file of the viral peptide sequences (vPro.Peptide.Library.txt) and a summary file of the virus taxonomy covered by the library (Taxonomy.Summary.txt). The metadata file is used by the vPro script to identify viruses from the DiaNN main report.

opencc-by-4.0Nov 2022View details →

Anti-inflammatory compounds in probiotic yeast revealed by untargeted metabolomics

Abstract The saccharomyces strain Saccharomyces cerevisiae var. boulardii has exhibited efficacy in ameliorating symptoms of gastrointestinal disorders, including inflammatory diseases. The molecular origin of the anti-inflammatory activity has remained largely elusive to this day. Earlier studies suggest a small, secreted, yet undefined, molecule as the active principle and thus an untargeted metabolomics approach towards its identification was adopted. We used LCMS-analysis to interrogate the secreted metabolome of S. cerevisiae var. boulardii in comparison to a S. cerevisiae reference strain. Statistical analysis of the data revealed several compounds unique to S. cerevisiae var. boulardii, that were partially annotated and confirmed by comparison with authentic standards. Furthermore, anti-inflammatory properties were experimentally assigned to several small molecules, indicating that this property of the yeast variant is due to several factors. Our data suggest that the anti-inflammatory properties of S. cerevisiae var. boulardii can be linked to the activity of small molecules in its secreted metabolome.

opencc-by-4.0Jan 2023View details →

HETDEX Public Source Catalog 1: 220 K Sources including over 50K Lyman Alpha Emitters From an Untargeted Wide-area Spectroscopic Survey

We present the first publicly released catalog of sources obtained from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). HETDEX is an integral field spectroscopic survey designed to measure the Hubble expansion parameter and angular diameter distance at 1.88 < z < 3.52 by using the spatial distribution of more than a million Lyα-emitting galaxies over a total target area of 540 deg2. The catalog comes from contiguous fiber spectra coverage of 25 deg2 of sky from January 2017 through June 2020, where object detection is performed through two complementary detection methods: one designed to search for line emission and the other a search for continuum emission.  The HETDEX public release catalog is dominated by emission-line galaxies and includes 51,863 Lyα-emitting galaxy (LAE) identifications and 123,891 [O II]-emitting galaxies at z < 0.5. Also included in the catalog are 37,916 stars, 5,274 low-redshift (z < 0.5) galaxies without emission lines, and 4,976 active galactic nuclei. The catalog provides sky coordinates, redshifts, line identifications, classification information, line fluxes, [O II] and Lyα line luminosities where applicable, and spectra for all identified sources processed by the HETDEX detection pipeline. Extensive testing demonstrates that HETDEX redshifts agree to within ∆z < 0.02, 96.1% of the time to those in external spectroscopic catalogs.  We measure the photometric counterpart fraction in deep ancillary Hyper Suprime-Cam imaging and find that only 55.5% of the LAE sample has an r-band continuum counterpart down to a limiting magnitude of r &sim; 26.2 mag (AB) indicating that an LAE search of similar sensitivity with photometric pre-selection would miss nearly half of the HETDEX LAE catalog sample. Two catalogs make up HETDEX Source Catalog 1:          1. The Source Observation Table: hetdex_sc1_vX.dat/.fits/.ecsv        With SPECTRA arrays included: hetdex_sc1_spec_vX.fits                 One row per source observation. The table provides basic coordinates/redshift/source information for each observation of a unique astornomical source. The larger file hetdex_sc1_spec_vX.fits contains the same info from the first table plus addition data units of spectral array data.              2. The Detection Information Table: hetdex_sc1_detinfo_vX.fits         One row per line or continuum detection. Bright sources can be comprised of multiple line or continuum emission. This catalog provides specific detection information such as line parameter info (S/N, line flux, line width)     3. Jupyter Notebook with access example: HETDEX_source_catalog_1.ipynb/.pdf/.html We request that the following acknowledgement be included in any paper using data or software from HETDEX public data releases: <blockquote> HETDEX is led by the University of Texas at Austin McDonald Observatory and Department of Astronomy with participation from the Ludwig-Maximilians-Universität München, Max-Planck-Institut für Extraterrestrische Physik (MPE), Leibniz-Institut für Astrophysik Potsdam (AIP), Texas A&M University, Pennsylvania State University, Institut für Astrophysik Göttingen, The University of Oxford, Max-Planck-Institut für Astrophysik (MPA), The University of Tokyo and Missouri University of Science and Technology. Observations for HETDEX were obtained with the Hobby-Eberly Telescope (HET), which is a joint project of the University of Texas at Austin, the Pennsylvania State University, Ludwig-Maximilians-Universität München, and Georg-August-Universität Göttingen. The HET is named in honor of its principal benefactors, William P. Hobby and Robert E. Eberly. The Visible Integral-field Replicable Unit Spectrograph (VIRUS) was used for HETDEX observations. VIRUS is a joint project of the University of Texas at Austin, Leibniz-Institut für Astrophysik Potsdam (AIP), Texas A&M University, Max-Planck-Institut fürExtraterrestrische Physik (MPE), Ludwig-Maximilians-Universität München, Pennsylvania State University, Institut für Astrophysik Göttingen, University of Oxford, and the Max-Planck-Institut fur Astrophysik (MPA). Funding for HETDEX has been provided by the partner institutions, the National Science Foundation, the State of Texas, the US Air Force, and by generous support from private individuals and foundations. </blockquote>

zenodo40/100

Untargeted leaf metabolomes of Noccaea praecox and N. caerulescens

The dataset represents untargeted leaf metabolomes of Noccaea caerulescens from the un-polluted site Lokovec (Slovenia), N. praecox from the same site, and N. praecox from metal-enriched site Zerjav (Slovenia).

opencc-by-4.0Apr 2024View details →

opencc-zeroOct 2020View details →

Light and temperature measurements and untargeted proteomic measurements

The right timing of animal physiology and behavior ensures the stability of populations and ecosystems. In order to predict anthropogenic impacts on these timings, more insight is needed into the interplay between environment and molecular timing mechanisms. This is particularly true in marine environments. Using high-resolution, long-term daylight measurements from a habitat of the marine annelid Platynereis dumerilii, we find that temporal changes in UVA/deep violet intensities, more than longer wavelengths, can provide annual time information, which differs from annual changes in photoperiod. We developed experimental setups that resemble natural daylight illumination conditions, and automated, quantifiable behavioral tracking. Experimental reduction of UVA/deep violet light (app. 370-430nm) under long photoperiod (LD16:8) significantly decreases locomotor activities, comparable to the decrease caused by short photoperiod (8:16). In contrast, altering UVA/deep violet light intensities does not cause differences in locomotor levels under short photoperiod. This modulation of locomotion by UVA/deep violet light under long photoperiod requires c-opsin1, an UVA/deep violet-sensor employing Gi-signalling. C-opsin1 also regulates the levels of rate-limiting enzymes for monogenic amine synthesis and of several neurohormones, including PDF, Vasotocin (Vasopressin/Oxytocin) and NPY-1. Our analyses indicate a complex inteplay between UVA intensities and photoperiod as indicators of annual time.

opencc-by-4.0Sep 2024View details →

Raw data and supporting files for "Modular comparison of untargeted metabolomics processing steps"

Raw data and supporting files for the Paper titled "Modular comparison of untargeted metabolomics processing steps". The dataset encompasses 42 samples, with 3 solvent blanks, 7 QC samples, and 32 biological samples (4 biological replicates: Banane, Bergrose, Narbe, Ricky) spiked with 42 compounds in different concentrations (0 ngmL, 30 ngmL, 100 ngmL, 300 ngmL). The files were uploaded in the vendor format (.raw) and in the open format (.mzML). Also, further supporting data for the processing results was uploaded as well.

opencc-by-4.0May 2022View details →

Untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community"

This dataset contains the untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community". The dataset has also been submitted to MetaboLights repository with ID "MTBLS3535". Please refer to the MetaboLights repository for the most up-to-date datasets.  Publication abstract: A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM12) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM12 interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, Enterococcus faecalis KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM12 interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.

opencc-by-4.0Apr 2024View details →

Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web

This is the data and analysis scripts needed to recreate the analyses shown in "Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web" The abstract of the manuscript summarizes the goal of the paper:   Stable isotope tracing is a crucial technique for understanding the metabolic wiring of biological systems, determining metabolic flux through pathways of interest, and detecting novel metabolites and pathways. Despite the potential insights provided by this technique, its application remains limited to a small number of targeted molecules and pathways. Because previous software tools usually require chemical formulas to find relevant features, and the data are highly complex, especially in untargeted metabolomics and when the reactions and metabolites downstream the labeled substrates are poorly characterized. We report here Khipu version 2 and its new user-friendly web application. New functions are added to enhance analyzing stable isotope tracing data including metrics that evaluate peak enrichment in labeled samples, scoring methods to facilitate robust detection of intensity patterns and integrated natural abundance correction. We demonstrate that this approach can be applied to untargeted metabolomics to systematically extract isotope-labeled compounds and annotate the unidentified metabolites.   This repository stores the code and data needed to recreate all presented analyses. The code and instructions are in the AnalysisCode.zip. The DDA in the dda_mzML.zip, the MS1 in the dataset_mzml.zip, and the asari results in the AsariResults.zip. The readme is in the AnalysisCode.zip and has more detailed instructions. This directory also has the output data in tabular format for the figures as the figures were mostly made with Excel.

opencc-by-4.0Jul 2024View details →

Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor

Metabolomics data from the study: Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor. Files are raw chromatograms. Metabolite profiling analysis Metabolite Extraction and Quantification. The whole metabolite extraction and quantification pipeline followed the guidelines as described in Giavalisco et al. (2011) and Salem et al. (2020). Briefly, 10-25 mg of fresh plant tissue was harvested and immediately frozen in N2, and ground using zirconia beads with the help of Tissue Lyser Mixer-Mill (Qiagen) 3 min at 25 Hz. After samples were extracted in 100% cold methanol, they were centrifuged for 10 min at 14000 rpm (Room temperature) and 3:1 chloroform was added. After vortexing thoroughly, 1:1 volume of water was added and the samples were subsequently vortexed and centrifuged. The semi-polar phase was used for analysis of semi-polar secondary and primary metabolites. For secondary semi-polar metabolites, the dried polar aliquots were resuspended in water:methanol (1:1 v/v). 3 Analysis of semi-polar metabolites was performed using a Thermo Q Exactive Focus coupled to a reverse-phase C18 column. The column was maintained at 40°C with a flow rate of 400 μl/min, and the eluent system consisted of water (eluent A) and acetonitrile (eluent B), both containing 0.1% formic acid. Mass spectra were acquired in full scan mode over a range of 100-1500 m/z using data-independent acquisition (DIA) with high-energy collisional dissociation (HCD) energy set at 30 eV in positive mode. 9 For primary metabolite metabolites, the dried polar was derivatized as described in Lisec et al. (2006). Derivatization was carried out at 37°C for 120 min using 40 μl of 20 mg/ml methoxyamine hydrochloride in pyridine, followed by a 30-min treatment at 37°C with 70 μl of trimethylsilyl-N-methyl trifluoroacetamide (MSTFA). The derivatized samples (1 μl) were injected in splitless mode into a gas chromatograph coupled to a time-of-flight mass spectrometer (Pegasus HT TOF-MS). Gas chromatography was performed on a 30-m DB-35 column using helium as the carrier gas. The initial temperature of the oven was 85°C, and it was ramped up at a rate of 15°C/min to a final temperature of 360°C. Mass spectra were recorded in the range of 70-600 m/z at a rate of 20 scans/s. Data Processing and Compound Annotation. LC-MS full scan data were processed using MS Refiner (Expressionist 14.0). Processing of chromatograms, peak detection, and integration were performed using RefinerMS (version 5.3; GeneData). Metabolite identification and annotation were performed using in-house reference compound library, tandem MS (MS/MS) 22 fragmentation, and metabolomics databases (Alseekh et al., 2021). For the annotation of metabolites measured by GC-MS the Golm Metabolome Database was used (Kopka et al., 2005). <div> <div> #heatmap of fold changes  </div> <div> ts <-read.csv("all_log2fc_heat_all.csv", sep=";", fileEncoding='latin1',check.names=F, header=TRUE)  </div> <div> ts1 <- ts  </div> <div> ts1$class <- NULL   </div> <div>   </div> <div> row.names(ts1) <- ts1$compound  </div> <div> ts1$compound <- NULL  </div> <div> m <- as.matrix(ts1)  </div> <div>   </div> <div> ann <- ts[1:2]  </div> <div> row.names(ann) <- ann$compound  </div> <div> ann$compound <- NULL  </div> <div>   </div> <div> install.packages("pheatmap")  </div> <div>   </div> <div> library("pheatmap")  </div> <div>   </div> <div> m1 <- m[rownames(ann), ]  </div> <div>   </div> <div> pheatmap(m1, annotation_row = ann, cluster_rows = F,cluster_cols = F, cellheight = 10,   </div> <div>          cellwidth = 10, gaps_row = cumsum(c(3,105,30,11,21,4,32,5,17)), gaps_col = cumsum(c(2,2,2)), scale = 'none')  </div> <div>   </div> <div> #boxplots  </div> <div> install.packages("ggplot2")  </div> <div> install.packages("readr")  </div> <div>   </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div>   </div> <div> install.packages("tidyverse")  </div> <div> library(tidyverse)  </div> <div> grid <- read.csv("sec_exp1_logfc_20.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> grid_g <- grid%>% gather("compound", "value", 6:25)  </div> <div>   </div> <div> d=ggplot(data = grid_g, aes(x=compound, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d+theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#ca5826","#e4ab92", "#008080", "#99cccc"))+  </div> <div>   labs(y="log2fc(metabolite content)",fill="gtype")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 5)) +   </div> <div>   theme_classic() +  </div> <div>   coord_flip() +  </div> <div>   facet_grid(condition ~ .)  </div> <div>   </div> <div> ggsave("logfcsum_exp1_15_bri1.png", width = 10, height = 10)  </div> <div>   </div> <div> #boxplots all data  </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div> library(viridis)  </div> <div> library(tidyverse)  </div> <div> library(dplyr)  </div> <div>   </div> <div> sPCA <-read.csv("GCMS_polar.csv", sep=",", fileEncoding='latin1', check.names = F, header=TRUE)  </div> <div>   </div> <div> row.names(sPCA) <- sPCA$Sample  </div> <div> sPCA_D <- sPCA[6:155]  </div> <div> sPCA_Dlog <- log(sPCA_D)  </div> <div> sPCA_Dlog10 <- log10(sPCA_D)  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, group = sPCA$Sample_group)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(group, everything())  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, sample = sPCA$Sample)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(sample, everything())  </div> <div>   </div> <div> write.csv(sPCA_Dlog10, "GCMS_polar_lg.csv")  </div> <div>   </div> <div> sPCA_Dlog_g <- sPCA_Dlog%>% gather("compound", "value", 3:152)  </div> <div>   </div> <div> d <- ggplot(sPCA_Dlog_g, aes(x=sample, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d + theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#CC6677","#882255","#44AA99", "#117733"))+  </div> <div>   labs(y="lg(normalized metabolite content)", x= "sample" , fill="group")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1)) +   </div> <div>   theme_classic() +  </div> <div>   geom_hline(yintercept=0, linetype="dashed", color = "black")  </div> <div> #PCA  </div> <div> install.packages("plotly")  </div> <div> install.packages("ggfortify")  </div> <div> library(plotly)  </div> <div> library(ggfortify)  </div> <div>   </div> <div> sPCA <-read.csv("prim.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> sPCA <- subset(sPCA, experiment!="2")  </div> <div>   </div> <div> sPCAtd <- all_d[7:240]  </div> <div> sPCAtdn <- sPCAtd[-c(24), ]  </div> <div> sPCAn <- sPCA[-c(24), ]  </div> <div>   </div> <div> #normal  </div> <div>   </div> <div> sec.pcat <- prcomp(sPCAtd, center = TRUE,scale. = TRUE)  </div> <div>   </div> <div> summary(sec.pcat)  </div> <div>   </div> <div> p <- autoplot(sec.pcat, data = all_d, colour = 'condition', shape = 'Genotype') +   </div> <div>   geom_text(aes(label = Replicate), nudge_y = 0.02) +  </div> <div>   theme_classic() +  </div> <div>   scale_color_manual(values=c("#56B4E9", "#E69F00"))  </div> <div>   </div> <div> ggplotly(p)  </div> <div> #experiment wise fold changes  </div> <div> write.csv(sec_fc_mean, "sec_foldchange_mean.csv")  </div> <div>   </div> <div> sec_fc_exp1 <- sect[1]  </div> <div>   </div> <div> sec_fc_exp1$bri1.drought <- foldchange(sect$X72.bri1.drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought1 <- foldchange(sect$X72.bri1.drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought2 <- foldchange(sect$X72.bri1.drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$bri1.control <- foldchange(sect$X72.bri1.control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control1 <- foldchange(sect$X72.bri1.control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control2 <- foldchange(sect$X72.bri1.control.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.drought <- foldchange(sect$X72.WT..drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought1 <- foldchange(sect$X72.WT..drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought2 <- foldchange(sect$X72.WT..drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.control <- foldchange(sect$X72.WT..control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control1 <- foldchange(sect$X72.WT..control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control2 <- foldchange(sect$X72.WT..control.2, sect$median.WT.c1)  </div> <div>   </div> <div>   </div> <div> write.csv(sec_fc_exp1, "sec_fc_exp1.csv")  </div> <div>   </div> <div> #all compounds for RNAsq comarision  </div> <div>   </div> <div> all_R <-read.csv("all_RNAsq.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> all_fc_exp2 <- all_R[1]  </div> <div>   </div> <div> all_fc_exp2$drought <- foldchange(all_R$bri1_drought, all_R$WT_drought)  </div> <div>   </div> <div> all_fc_exp2$control <- foldchange(all_R$bri1_control, all_R$WT_control)  </div> <div>   </div> <div> write.csv(all_fc_exp2, "all_foldchange_exp2.csv")  </div> <div>   </div> <div> #students t test  </div> <div>   </div> <div> secfc_2_t <-read.csv("sec_exp1_logfc.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> secfc_2_t <- secfc_2_t[-2]  </div> <div> secfc_2_t <- secfc_2_t[-(14:25)]  </div> <div>   </div> <div> st2<- secfc_2_t %>% gather("compound", "value", 6:171)  </div> <div> stat.test1 <- st2 %>%  </div> <div>   group_by(compound) %>%  </div> <div>   t_test(value ~ group)  </div> <div> add_significance()  </div> <div> stat.test1  </div> </div>

opencc-by-4.0Oct 2024View details →

Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking

Comparative analysis of SIRIUS to evaluate our method, Biotransformation-based Annotation Method (BAM). This dataset includes all scripts, data, and results relevant to this analysis. BAM can be found on GitHub (https://github.com/HassounLab/BAM).

opencc-by-4.0Nov 2024View details →

High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds

<h3>Content</h3> Dataset of the study: "High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds.

opencc-by-4.0Jan 2023View details →

Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation

The unprocessed and processed benchmarking data used in the paper of "Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation"

opencc-zeroFeb 2023View details →

MALDI-MS dataset for use with open-source untargeted metabolomic workflow for complex biological samples

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free to-use data processing and analysis tools exist for various untargeted MS approaches, but choosing the 'correct' pipeline isn't straight-forward. This data set can be used in conjunction with a user-friendly online guide which presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. The workflow provides practical advice concerning experimental design, organisation of data and downstream analysis, and offers details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/ changing methodologies and increased clarity and detail as user participation becomes more common allowing contributions and improvements to the workflow via the online repository.

opencc-by-4.0Jun 2023View details →

Untargeted metabolomic approach using UHPLC-HRMS to unravel the impact of fermentation on color and phenolic composition of Rosé wines

Color is a major quality trait of rosé wines due to their packaging in clear glass bottles. This color is due to the presence of phenolic pigments extracted from grapes to wines and products of reactions taking place during the wine-making process. This study focuses on changes occurring during alcoholic fermentation of Syrah, Grenache and Cinsault musts, conducted at laboratory (250 mL) and pilot (100 L) scales. Color and phenolic composition of the musts and wines were analyzed using UV-visible spectrophotometry and metabolomics fingerprints were acquired by Ultra-High Performance Liquid Chromatography−High Resolution Mass Spectrometry. The acquisition dataset protocol is available in the affiliated publication on Molecules

opencc-by-4.0Aug 2023View details →

TBT exposure dataset of the manuscript "Assessment of endocrine disruptors effects on zebrafish (Danio rerio) embryos by untargeted LC-HRMS metabolomic analysis"

Raw LC-HRMS data of the TBT exposure of zebrafish embryos (for more details see https://doi.org/10.1016/j.scitotenv.2018.03.369) The exposure protocol involved zebrafish embryos exposed in groups of 20 to various concentrations of chemical compounds, with five replicates per treatment. The concentrations ranged from the lowest observed effect concentrations (LOECs) to control levels. After exposure, embryos were collected, washed, frozen, and stored. Metabolites were extracted from individual embryo pools using methanol and methionine sulfone. The extraction process included vortexing, sonication, and centrifugation, followed by addition of water and chloroform. The aqueous fraction was dried and reconstituted using acetonitrile-water solution. Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) was used for analysis. Chromatographic separations were carried out on a hydrophilic interaction liquid chromatography (HILIC) column. Mass spectrometry was performed using an Orbitrap mass spectrometer with electrospray ionization in positive and negative modes. The mass spectra were acquired at high resolution, and fragmentation scans were used for metabolite identification. The overall process aimed to analyze the metabolomic profile of zebrafish embryos exposed to different chemical concentrations. Data files <blockquote> TBT ESI+ (tbt_pos.rar) - CDF files - QC (4 replicates) - Control (5 replicates) - TBT 3 nM (5 replicates) - TBT 10 nM (5 replicates) - TBT 30 nM (5 replicates) - TBT 100 nM (5 replicates) </blockquote>   <blockquote> TBT ESI- (tbt_neg.rar) - CDF files - QC (6 replicates) - Control (5 replicates) - TBT 3 nM (5 replicates) - TBT 10 nM (5 replicates) - TBT 30 nM (5 replicates) - TBT 100 nM (5 replicates) </blockquote>

publicMar 2021View details →

Light and temperature measurements and untargeted proteomic measurements

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publicAug 2024View details →

Untargeted metabolomics data from Faecalibacterium prausnitzii FAAH experiment

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