Project description:The demo dataset of ApuQuant for testing consists of DIA data acquired by Orbitrap Astral mass spectrometer. This submission includes two demo datasets together with the corresponding DIA-NN identification results. Demo Data I contains raw files from low-input samples with four technical replicates, as well as the corresponding DIA-NN identification results. Demo Data II contains raw files from dead cell samples with three technical replicates and large-sized cell samples with three technical replicates, together with the corresponding DIA-NN identification results. These datasets are provided as representative test data for evaluating the performance of ApuQuant on challenging DIA proteomics samples, including low-input samples and biologically distinct cell populations. The raw mass spectrometry files and search results can be used for demonstration, benchmarking, and reproducibility assessment of the ApuQuant workflow.
Project description:<p>Urine metabolomics is widely used for biomarker research in the fields of medicine and toxicology. As a consequence, characterization of the variations of the urine metabolome under basal conditions becomes critical in order to avoid confounding effects in cohort studies. Such physiological information is however very scarce in the literature and in metabolomics databases so far. Here we studied the influence of age, body mass index (BMI), and gender on metabolite concentrations in a large cohort of 183 adults by using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). We implemented a comprehensive statistical workflow for univariate hypothesis testing and modeling by orthogonal partial least-squares (OPLS).</p><p> This repository contains the data set from the negative ionization mode: 2 batches, 234 files (24 blanks + 26 QCs + 184 samples) in the Thermo .RAW (6.8 Go) and .mzML (18 Go) formats. The comprehensive analysis of this data set is publicly available on the Workflow4metabolomics.org e-infrastructure with two reference histories: 'W4M00002_Sacurine-comprehensive' corresponds to the preprocessing of the .mzML files, followed by signal drift and batch effect correction, normalization, filtering, statistics, and annotation of the peak table; 'W4M00001_Sacurine- statistics' starts with the peak table restricted to the 113 identified metabolites (see Roux et al. [1] for a full description and information about the annotation), and contains the statistical analysis (as described in associated publication except that the publication also describes the positive ionization mode). The intensities of the table provided in the m_sacurine.txt ISA file correspond to the peak table restricted to the 113 identified metabolites (i.e. are identical to the input of the 'W4M00001_Sacurine-statistics' history). Note that in both histories, the HU_096 sample is filtered out during the Hotelling/Quantile/MissingValue quality control sample filter, leading to 183 samples for the subsequent statistical analyzes. Notes: The 'sampling' field indicates the 9 successive weeks during which samples were collected. The 'subset' field indicates a subset of 36 files (6 blanks + 10 QCs + 20 samples) which still contain significant physiological variations (and can be used as e.g. demo or teaching material).</p><p> Acknowledgements: The authors are grateful to Philippe Rocca-Serra for his help in preparing the ISA files.</p><p><br></p><p> References:</p><p> [1] Roux A, Xu Y, Heilier JF, Olivier MF, Ezan E, Tabet JC, Junot C. 2012. Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer. Anal Chem. Aug 7;84(15):6429-37. doi: 10.1021/ac300829f.</p>
Project description:LC-MS/MS data of crude extracts produced by isolated bacteria recovered from Brazilian Rocas Atoll. Subset of MSV000083601 for use as an example dataset on the Workshop on Advanced Mass Spectometry 2025.
Project description:RNA sequencing of pig tissues for transcriptome annotation and expression analysis. Tissue specific RNA-seq data was generated to support annotation of coding and non-coding genes and to measure tissue specific expression. This study is part of the FAANG project, promoting rapid prepublication of data to support the research community. These data are released under Fort Lauderdale principles, as confirmed in the Toronto Statement (Toronto International Data Release Workshop. Birney et al. 2009. Pre-publication data sharing. Nature 461:168-170). Any use of this dataset must abide by the FAANG data sharing principles. Data producers reserve the right to make the first publication of a global analysis of this data. If you are unsure if you are allowed to publish on this dataset, please contact alan.archibald@roslin.ed.ac.uk, lel.eory@roslin.ed.ac.uk and faang@iastate.edu to enquire. The full guidelines can be found at http://www.faang.org/data-share-principle”.
Project description:Sonic hedgehog (Shh) signals via Gli transcription factors to stimulate proliferation of granule neuron precursor cells (GNPs) in the cerebellum. Deregulation of Shh target genes often results in unrestrained GNP proliferation and eventually medulloblastoma (MB), the most common pediatric brain malignancy. Gene expression profiling was coupled with transcription factor binding location analysis to determine the Gli1-controlled transcriptional regulatory networks in GNPs and medulloblastoma cells. We detected significant overlap, as well as differences, in the Gli1-controlled transcriptional regulatory networks in GNPs and MBs. We determined the presence of gene expression in each dataset. There were 9260 genes expressed in Gli1-FLAG GNPs and 9185 genes expressed in Gli1-FLAG;Ptc+/- tumors; 8691 of which are in common. The large overlap is consistent with the cellular origin of these tumors. When the genes detectably expressed were intersected with our binding data, there were only 132 putative Gli1 target genes shared by both cell populations. Due to the heightened activation of the Hh pathway in tumors relative to GNPs, we further deduced direct Gli1 target genes exclusive to tumors by determining significantly induced genes in tumors versus in Ptc+/- GNPs. We identified at least 116 tumor-specific Gli1 target genes. These data suggest that tumor formation is accompanied by a tremendous change in the battery of Gli target genes. Presence of gene expression was determined for all samples: Gli1-FLAG-expressing GNPs, Ptc+/- GNPs, and Gli1-FLAG;Ptc+/-medulloblastomas. These datasets were intersected with chIP-chip data to determine potential direct Gli1 target genes. Differential gene expression was determined by comparing expression profiles from medulloblastoma tumors to those from Ptc+/- GNPs.
Project description:The demo datasets available for MSCohort analysis. You can download to inspect their formats and practice using the software tool. This dataset contains raw files of 7 urine QC samples, spectronaut analysis results and MSCohort report results
Project description:Sonic hedgehog (Shh) signals via Gli transcription factors to stimulate proliferation of granule neuron precursor cells (GNPs) in the cerebellum. Deregulation of Shh target genes often results in unrestrained GNP proliferation and eventually medulloblastoma (MB), the most common pediatric brain malignancy. Gene expression profiling was coupled with transcription factor binding location analysis to determine the Gli1-controlled transcriptional regulatory networks in GNPs and medulloblastoma cells. We detected significant overlap, as well as differences, in the Gli1-controlled transcriptional regulatory networks in GNPs and MBs. We determined the presence of gene expression in each dataset. There were 9260 genes expressed in Gli1-FLAG GNPs and 9185 genes expressed in Gli1-FLAG;Ptc+/- tumors; 8691 of which are in common. The large overlap is consistent with the cellular origin of these tumors. When the genes detectably expressed were intersected with our binding data, there were only 132 putative Gli1 target genes shared by both cell populations. Due to the heightened activation of the Hh pathway in tumors relative to GNPs, we further deduced direct Gli1 target genes exclusive to tumors by determining significantly induced genes in tumors versus in Ptc+/- GNPs. We identified at least 116 tumor-specific Gli1 target genes. These data suggest that tumor formation is accompanied by a tremendous change in the battery of Gli target genes.
Project description:Example files for a GNPS Molecular Networking Workshop held online. Data are composed of four Malpighiaceae plant samples (a subset of MSV000085119). Data were acquired in a Maxis Impact Q-TOF mass spectrometer (Bruker Daltonics) equipped with an ESI source (positive ionization mode).