GNPS Synthetic biology microbial samples - LC-IM-MS All Ions
Ontology highlight
ABSTRACT:
INSTRUMENT(S): 6560 Q-TOF LC/MS
ORGANISM(S): Rhodosporidium Toruloides Np11 (ncbitaxon:1130832) Pseudomonas Putida Group (ncbitaxon:136845) Aspergillus Niger (ncbitaxon:5061) Aspergillus Pseudoterreus (ncbitaxon:1565506)
SUBMITTER: 
Aivett Bilbao Kristin Burnum-Johnson
PROVIDER: MSV000089733 | GNPS | Sun Jun 26 17:20:00 BST 2022
REPOSITORIES: GNPS
Similar Datasets
Project description:We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.
2020-06-05 | GSE142245 | GEO
Project description:The Mammalian Methylation Consortium aimed to characterize the relationship between cytosine methylation levels and a) species characteristics such as maximum lifespan and b) individual sample characteristics such as age, sex, tissue type. Both supervised machine learning approaches and unsupervised machine learning approaches were applied to the data as described in the citations. To facilitate comparative analyses across species, the mammalian methylation consortium applied a single measurement platform (the mammalian methylation array, GPL28271) to n=15216 DNA samples derived from 70 tissue types of 348 different mammalian species (331 eutherian-, 15 marsupial-, and 2 monotreme species). Most of the CpGs are located in highly conserved stretches of DNA but not all CpGs apply to all species as detailed in the description of the platform, GPL28271 and on https://github.com/shorvath/MammalianMethylationConsortium/.
2023-08-05 | GSE223748 | GEO
Project description:The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package (https://github.com/InfectionMedicineProteomics/BINN).
2023-09-05 | PXD044264 | Pride
Project description:These data files and results are supplementary material for the manuscript "PeakDecoder: A Machine Learning-Based Metabolite Identification Algorithm for Multidimensional Mass Spectrometry Measurements and its Application in Synthetic Biology".
2022-06-26 | MSV000089733 | MassIVE
Project description:This model has been trained using the GROVER transformer and Predicts activity of compounds in the Tox21 toxicity panel, comprising of 12 toxicity pathways, as part of the MoleculeNet benchmark datasets.
Model Type: Predicitive machine learning model.
Model Relevance: Toxicity measurements against 12 biological targets.
Model Encoded by: Amna Ali (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos5smc
2024-06-05 | MODEL2406050005 | BioModels
Project description:We present the first comprehensive map of spatiotemporal heterogeneity of the human proteome by integrating proteomics at subcellular resolution, single-cell transcriptomics, and pseudotime measurements of each cell within the cell cycle. The cell cycle transcriptomic data in this series provide context for single-cell proteomic measurements of the cell cycle, and this subsequent single-cell proteogenomic analysis that can be found in this code repository: https://github.com/CellProfiling/SingleCellProteogenomics .
2020-12-11 | GSE146773 | GEO
Project description:Global proteomics datasets to study ash utilization by R. toruloides. PeakDecoder is a workflow for metabolite identification and accurate profiling in multidimensional LC-IM-MS-DIA measurements. It is available at https://github.com/EMSL-Computing/PeakDecoder
2023-02-03 | MSV000091202 | MassIVE
Project description:The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18\% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74\%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377\% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87\%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at (\url{https://github.com/InfectionMedicineProteomics/gps})
2023-07-20 | PXD038367 | Pride
Project description:The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18\% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74\%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377\% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87\%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at (\url{https://github.com/InfectionMedicineProteomics/gps})
2023-07-20 | PXD038377 | Pride
Project description:The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at https://github.com/InfectionMedicineProteomics/gps
2023-07-20 | PXD038394 | Pride