GNPS - Data files for metabolomics
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Exploris 480 MS
ORGANISM(S): Mus Sp. (ncbitaxon:10095)
SUBMITTER: 
Joshua Rabinowitz
PROVIDER: MSV000094432 | GNPS | Thu Mar 28 12:09:00 GMT 2024
REPOSITORIES: GNPS
Similar Datasets
Project description:mzXML files associated with Fig.S1B and Fig.S2C and D. Change of serum, spleen, and tumor metabolome over time.
See "Knowns-new-Hilic25min-QE - Knowns-new-Hilic25min-QE.csv" for retention time information.
2024-03-28 | MSV000094432 | MassIVE
Project description:Reactive oxygen species (ROS) are involved in the regulation of diverse physiological processes in plants, including various biotic and abiotic stress responses. Thus, oxidative stress tolerance mechanisms in plants are complex, and diverse responses at multiple levels need to be characterized in order to understand them. The MS raw data files were converted to mzXML files using massWolf (version 4.3.1). The MS mzXML and MASCOT xml files were parsed and processed with a program developed in-house. Briefly, each scan was subjected to smoothing using Savitzky-Golay filtering (second order polynomial, five data points, two iterations) and peak areas were calculated after noise reduction. Peak mass was set to the average of the three highest data points for each peak. Unique peptides identified with MASCOT were matched to the parsed MS data using the parameters detected m/z, charge state and retention time, using a retention time window of ± 1.0 min. Charge states were calculated by using the first three isotopic peaks of a peptide and the same mass tolerances for detecting the mono isotopic peak as in the MASCOT search.
2014-04-17 | PXD000532 | Pride
Project description:In this study, the efficiency of four RNA extraction methods was compared on 23 FFPE cardiac tissue specimens. The Qiagen AllPrep DNA/RNA FFPE kit (Method QP), Qiagen AllPrep DNA/RNA FFPE kit, with protocol modification on the ethanol wash step after deparaffinization (Method QE), CELLDATA RNAstorm 2.0 FFPE RNA Extraction kit (Method BP) and CELLDATA RNAstorm 2.0 FFPE RNA Extraction Kit with protocol modifications on the lysis step (Method BL). In comparing RNA quality metrics across FFPE RNA extract, nucleic acids extracted with Method QE and QP had the highest RNA yield. However, Method QE outperformed Method QP as more extract from Method QE had DV 200 values above 30%. Both method BL and BP produced a similar range of RNA purity and yield but more extract from Method BL had DV 200 values above 30% compared to Method BP. When accessing distribution value, Method BL outperformed Methods BP, QE, and QP as more extracts from Method BL had DV 200 values above 30% (16/23 samples) compared to other methods (PDV200<0.001; Kruskal-Wallis). However, method QE outperformed other methods in terms of RNA yield. The sequencing performance of RNA extracts from Method QE and Method BL was further tested on 8 matching samples with high RNA yield and high DV200 value respectively. RNA extracts from Method QE which yielded the highest RNA quantity among all methods exhibited comparable sequencing performance to extract obtained through Method BL, which yielded extract with high DV200 value. This study suggests that the DV200 and RNA yield are both reliable pre-analytic metrics in determining a suitable method for successful transcriptome sequencing of FFPE samples and have important implications for future studies exploring transcriptome sequencing of FFPE cardiac specimens.
2024-05-23 | GSE267659 | GEO
Project description:Here, we investigated the effects of Rhynchophylline (RHY) on the mouse brain spatial transcriptome. More precisely, we injected male and female mice intraperitoneally with either saline (NaCl) or RHY, either at Zeitgeber time (ZT referring to time in hour after light onset) 0 or ZT0 and ZT11. Brains were sampled at ZT4 or ZT14, respectively, and immediately frozen embedded in OCT. Brains were treated and libraries were prepared according to 10x Genomics protocols for Visium Spatial Gene Expression. Sequencing was conducted by Genome Quebec (Montreal, Quebec, Canada). Findings reveal molecular routes by which RHY acts on the brain in a sleep-relevant context. Please cite the original paper when using these data (Ballester Roig et al., Biol Direct, 2023) and see also the submission GSE217058.
2023-05-05 | GSE218537 | GEO
Project description:In this study, we extended the ProteomeTools peptide library (PROPEL, see PXD004732 and PXD010595) to train a deep neural network resulting in chromatographic retention time and fragment ion intensity predictions for (tryptic) peptides that exceed the quality of the experimental data.
2019-05-30 | PXD010871 | Pride
Project description:Single analyses of a Zebrafish whole plasma digest were analyzed by both CE-MS/MS and LC-MS/MS. The identified peptides and their corresponding retention or migration times were used to train a 'blind' retention/migration time model using linear regression. This trained model provided us with information on the migration/retention coefficients resulting from the individual amino acids. All acquired tandem mass spectrometry data was processed in the Trans-Proteomic Pipeline (TPP) embedded in the Taverna scientific workflow manager. The raw data was converted to mzXML using compassXport 3.0 (Bruker) and searched with X!Tandem. The X!Tandem scores were then converted to pepXML, modeled and translated to probabilities for each peptide-spectral match by PeptideProphet. The X!Tandem search was here performed allowing a random error ±0.5 Da, +1 or +2 Da isotopic error, carbamidomethylation as fixed and oxidation as variable modification and the k-score plug-in.
2012-12-18 | PXD000112 | Pride
Project description:This repository contains all the FASTQ files for the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) used in the article \\"An Atlas of Cells in The Human Tonsil,\\" published in Immunity in 2024. Inspired by the TCGA barcodes, we have named each fastq file with the following convention: [TECHNOLOGY].[DONOR_ID].[SUBPROJECT].[GEM_ID].[LIBRARY_ID].[LIBRARY_TYPE].[LANE].[READ].fastq.gz which allows to retrieve all metadata from the name itself. Here is a full description of each field: - TECHNOLOGY: scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics (Visium). We also include the fastq files associated with the multiome experiments performed on two mantle cell lymphoma patients (MCL). - DONOR_ID: identifier for each of the 17 patients included in the cohort. We provide the donor-level metadata in the file \\"tonsil_atlas_donor_metadata.csv\\", including the hospital, sex, age, age group, cause for tonsillectomy and cohort type for every donor. - SUBPROJECT: each subproject corresponds to one run of the 10x Genomics Chromium™ Chip. - GEM_ID: each run of the 10x Genomics Chromium™ Chip consists of up to 8 \\"GEM wells\\" (see https://www.10xgenomics.com/support/software/cell-ranger/getting-started/cr-glossary): a set of partitioned cells (Gel Beads-in-emulsion) from a single 10x Genomics Chromium™ Chip channel. We give a unique identifier to each of these channels. - LIBRARY_ID: one or more sequencing libraries can be derived from a GEM well. For instance, multiome yields two libraries (ATAC and RNA) and CITE-seq+scVDJ yields 4 libraries (RNA, ADT, BCR, TCR). - LIBRARY_TYPE: the type of library for each library_id. Note that we used cell hashing () for a subset of the scRNA-seq libraries, and thus the library_type can be \\"not_hashed\\", \\"hashed_cdna\\" (RNA expression) or \\"hashed_hto\\" (the hashtag oligonucleotides). - LANE: to increase sequencing depth, each library was sequenced in more than one lane. Important: all lanes corresponding to the same sequencing library need to be inputed together to cellranger, because they come from the same set of cells. - READ: for scATAC-seq we have three reads (R1, R2 or R3), see cellranger-atac's documentation. While we find these names to be the most useful, they need to be changed to follow cellranger's conventions. We provide a code snippet in the README file of the GitHub repository associated with the tonsil atlas to convert between both formats (https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas/). Besides the fastq files, cellranger (and other mappers) require additional files, which we also provide in this repository: - cell_hashing_metadata.csv: as mentioned above, we ran cell hashing (10.1186/s13059-018-1603-1) to detect doublets and reduce cost per cell. This file provides the sequence of the hashtag oligonucleotides in cellranger convention to allow demultiplexing. - cite_seq_feature_reference.csv: similar to the previous file, this one links each protein surface marker to the hashtag oligonucleotide that identified it in the CITE-seq experiment. - V10M16-059.gpr and V19S23-039.gpr: these correspond to the two slides of the two Visium experiments performed in the tonsil atlas. They are needed to run spaceranger. - [GEM_ID]_[SLIDE]_[CAPTURE_AREA].jpg: 8 images associated with the Visium experiments. Here, GEM_ID refers to each of the 4 capture areas in each slide. - [TECHNOLOGY]_sequencing_metadata.csv: the GEM-level metadata for each technology. It includes the relationship between subproject, gem_id, library_id, library_type and donor_id. These are the other repositories associated with the tonsil atlas: - Expression and accessibility matrices: https://zenodo.org/records/10373041 - Seurat objects: https://zenodo.org/records/8373756 - HCATonsilData package: https://bioconductor.org/packages/release/data/experiment/html/HCATonsilData.html - Azimuth: https://azimuth.hubmapconsortium.org/ - Github: https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/TonsilAtlas
2024-05-02 | E-MTAB-13687 | biostudies-arrayexpress
Project description:see publication
| PRJEB20259 | ENA