Multiplexed quantitative lipidomics using LC-MS/MS
Ontology highlight
ABSTRACT:
INSTRUMENT(S): QE-MS
ORGANISM(S): Homo
SUBMITTER: 
PI
PROVIDER: MSV000091937 | MassIVE | Sun May 14 00:00:00 BST 2023
REPOSITORIES: MassIVE
Similar Datasets
Project description:The MassIVE Knowledge Base (MassIVE-KB) is a set of peptide spectral libraries. They are distilled from 31TB of human proteomics HCD data. Users can peek at the inside of these libraries, browse the source data, and track full provenance of analysis tasks that created these libraries.
Browsing Libraries
You can browse MassIVE-KB libraries to explore what is in them, and what is the provenance. Here is a view of the status page of a massive-kb library:
2023-05-14 | MSV000091937 | GNPS
Project description:Forum domains are stretches of chromosomal DNA that are excised from eukaryotic chromosomes during their spontaneous non-random fragmentation. Mostly forum domains are of 50-200 kb in length, although larger domains, up to 500 - 700 kb, are also observed. <br><br>We performed a genome-wide mapping of forum domains termini in Drosophila Schneider 2 cultured cells using Drosophila melanogaster genomic tiling microarray consisting of overlapping 3 kb fragments. We found that forum domains contain clusters of several or many genes inside. The largest forum domains correspond to the coordinately expressed main clusters of homeotic genes inside BX-C and ANTP-C, cluster of histone genes and clusters of piRNAs. PRE/TRE and binding sites of transcription factors are often reside inside domains, and do not overlap with forum domains termini. The terminal regions of domains often correspond to chromosomal sites containing different classes of mobile elements, binding sites of SuUR protein and regions of intercalary heterochromatin. Our results indicate that forum domains correspond to big multi-gene chromosomal units some of which could be co-coordinately activated or repressed.
2010-11-01 | E-MEXP-2468 | biostudies-arrayexpress
Project description:ImageMol is a Representation Learning Framework that utilizes molecule images for encoding molecular inputs as machine readable vectors for downstream tasks such as bio-activity prediction, drug metabolism analysis, or drug toxicity prediction. The approach utilizes transfer learning, that is, pre-training the model on massive unlabeled datasets to help it in generalizing feature extraction and then fine tuning on specific tasks. This model is fine tuned on 13 assays concerned with a number of target categories ranging from viral entry to toxicity in humans. These interactions are formulated as binary classification tasks.
Model Type: Predictive machine learning model.
Model Relevance: Probability of HIV inhibition.
Model Encoded by: Dhanshree Arora (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos6hy3
2024-05-13 | MODEL2405130001 | BioModels
Project description:A total of 18 libraries from Setaria viridis were constructed using the Illumina TruSeq sample preparation method. We used two biological replicate libraries from the leaf, whole panicles (inside leaf sheath), whole panicles (coming out of leaf sheath), whole panicles (completely out of leaf sheath), whole panicles (completely out of leaf sheath, after pollination), spikelet (inside leaf sheath), spikelet (coming out of leaf sheath), and spikelet (completely out of leaf sheath).
2018-07-28 | GSE111490 | GEO
Project description:ImageMol is a Representation Learning Framework that utilizes molecule images for encoding molecular inputs as machine readable vectors for downstream tasks such as bio-activity prediction, drug metabolism analysis, or drug toxicity prediction. The approach utilizes transfer learning, that is, pre-training the model on massive unlabeled datasets to help it in generalizing feature extraction and then fine tuning on specific tasks. This model is fine tuned on 13 assays concerned with a number of target categories ranging from viral entry to toxicity in humans. These interactions are formulated as binary classification tasks.
Model Type: Predictive machine learning model.
Model Relevance: SARS-CoV-2 Anti viral screening.
Model Encoded by: Dhanshree Arora (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos4cxk
2024-05-08 | MODEL2405080005 | BioModels
Project description:The individual contributions of many laboratories and experiments have advanced the proteomics community but the lack of an error-controlled community wide knowledge base that fully tracks provenance limits the ability for researchers to benefit from this progress. MassIVE-KB is a spectral library resource that aggregates identified spectra from over 30TB of human HCD proteomics data into a reusable resource that can be used to estimate the progress of the proteomics community. MassIVE-KB contains over 2.1 million precursors from 19,610 proteins and increased coverage to over 6 million amino acids (54% of the proteome) with strict false discovery controls at the spectrum, precursor, and protein level. Further, MassIVE-KB reveals previously missing proteomics evidence for 723 proteins, complete with fully traceable provenance back to the data, search parameters, and library creation procedures. This MassIVE-KB resource capitalizes on the whole community’s data to better inform experimental design, identify new data using library search tools, and provide the tools for the community to build their own specialized knowledge bases.
2017-06-06 | MSV000081142 | MassIVE
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Florencia Pauli mailto:fpauli@hudsonalpha.org). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track is produced as part of the ENCODE project. The track displays copy number variation (CNV) as determined by the Illumina Human 1M-Duo Infinium HD BeadChip assay and circular binary segmentation (CBS). The Human 1M-Duo contains more than 1,100,000 tagSNP markers and a set of ~60,000 additional CNV-targeted markers. The median spacing between markers is 1.5 kb and the mean spacing is 2.4 kb. The B-allele frequency and genotyping single nucleotide polymorphism (SNP) data generated by the experiment are not displayed, but are available for download from the Downloads page. Where applicable, biological replicates of each cell line are reported separately. Possible uses of the data include correction of copy number in peak-calling for ChIP-seq, transcriptome, DNase hypersensitivity, and methylation determinations. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2012-09-07 | GSE40698 | GEO
Project description:The array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC class I immunopeptidome is ubiquitous in mammals and represents a critical component of the adaptive immune system, very little is known about its baseline composition across all tissues and organs in vivo. In this study, we applied mass spectrometry (MS) technologies to draft a tissue-based atlas of the MHC class I immunopeptidome in the C56BL/6 mouse model. Overall, 19 normal mouse tissues were extracted and a total number of ~30,000 high-confidence H2Db/Kb-associated peptides were identified and annotated in the atlas. The raw MS output files, the catalogue of H2Db/Kb-associated peptides, the tissue origin(s) of individual peptides and the H2Db/Kb-specific peptide spectral libraries, which consist of consensus spectra calculated from repeat measurements of the same peptide sequence, were shared via the SysteMHC Atlas project (dataset identifier SYSMHC00018). We expect that this unique dataset will be further expended in the future and will be re-used by the community to 1) investigate the tissue-specificity of the immunopeptidome, 2) perform highly reproducible spectral library-based analysis of immunopeptidomes from mice cohorts, and 3) rapidly identify disease-specific H2Db/Kb peptide antigens from various mouse models of autoimmune diseases and cancers.
2018-06-19 | PXD008733 | Pride
Project description:We introduce high-throughput and massive paired-end mapping (PEM), a large-scale genome sequencing method to identify SVs 3 kb or larger that combines the rescue and capture of paired-ends of 3 kb fragments, massive 454 Sequencing, and a computational approach to map DNA reads onto a reference genome. PEM was used to map SVs in an African and putatively European individual and identified shared and divergent SVs relative to the reference genome. Overall, we fine-mapped more than 1000 SVs and documented that the number of SVs among humans is much larger than initially hypothesized; many of the SVs potentially affect gene function. The breakpoint junction sequences of more than 200 SVs were deduced with a novel pooling strategy and computational analysis. Array-CGH was used for validation. Keywords: array CGH 2 samples were analyzed with 8 different Nimblegen chips (385k); thus ~30M probes were used to interrogate copy number variants in NA15510 (using NA18505 as control) at high resolution.
2007-09-28 | E-GEOD-9002 | biostudies-arrayexpress
Project description:Here, we introduce XMAS, a bundle that allows users to load results from several XL-MS search engines directly into ChimeraX and map the information onto protein structures. Besides automatically locating distance constraints on protein structures, XMAS offers the possibility to work with replicate experiments and/or different crosslinkers, and filter this data based on how many replicates for which a given distance constraint was detected, thereby increasing the data quality. Additionally, we introduce the concept of self-links, which allows easy modeling of homo-dimeric interactions. Core functionality is extended by the implementation of seamless connections to the HADDOCK suite to streamline otherwise time-consuming tasks in structural modeling pipelines. We then demonstrate these key elements of the XMAS bundle by modeling crosslinking data obtained from human fibrin clots. This software is freely available from the ChimeraX toolshed, with an extensive user manual and example datasets.
2022-05-06 | PXD033409 | Pride