Project description:The molecular networks underlying Alzheimer’s disease (AD) are not well-defined. We present temporal profiling of >14,000 proteins and >34,000 phosphosites at the asymptomatic and symptomatic stages of AD, deep proteomics analysis of transgenic mouse models.
Project description:The molecular networks underlying Alzheimer’s disease (AD) are not well-defined. We present temporal profiling of >14,000 proteins and >34,000 phosphosites at the asymptomatic and symptomatic stages of AD, deep proteomics analysis of transgenic mouse models.
Project description:inSPIRE is an open-source tool for spectral rescoring of mass spectrometry search results. For this project, inSPIRE was applied to MaxQuant, PEAKS DB, and Mascot search results from a tryptic digestion of the K562 proteome. Here we provide the RAW files and search results using MaxQuant, PEAKS DB, and Mascot. We also reprocessed RAW data from the PXD031709 and PXD031812 repositories for which we provide the search result files. Additionally, we provide PEAKS search results from RAW files from the PXD015489 repository which was used as training data for a predictor used within inSPIRE. Michele Mishto, Head of the research group Molecular Immunology at King’s College London and the Francis Crick Institute, London (UK). Email: michele.mishto@kcl.ac.uk,
Project description:The molecular networks underlying Alzheimer’s disease (AD) are not well-defined. We present temporal profiling of >14,000 proteins and >34,000 phosphosites at the asymptomatic and symptomatic stages of AD, deep proteomics analysis of transgenic mouse models.
Project description:Active protein translation can be assessed and measured using ribosome profiling sequencing strategies. Existing approaches make use of sequence fragment length or frame occupancy to differentiate between active translation and background noise, however they do not consider additional characteristics inherent to the technology which limits their overall accuracy. Here, we present an analytical tool that models the overall tri-nucleotide periodicity of ribosomal occupancy using a classifier based on spectral coherence. Our software, SPECtre, examines the relationship of normalized ribosome profiling read coverage over a rolling series of windows along a transcript against an idealized reference signal. A comparison of SPECtre against current methods on existing and new data shows a marked improvement in accuracy for detecting active translation and exhibits overall high sensitivity at a low false discovery rate. Classification of actively translated transcripts in ribosome profiling data derived from human neuroblastoma SH-SY5Y cells, and data previously published derived from mouse embryonic stem cells and zebrafish embryos.
Project description:The molecular networks underlying Alzheimer’s disease (AD) are not well-defined. We present temporal profiling of >14,000 proteins and >34,000 phosphosites at the asymptomatic and symptomatic stages of AD, deep proteomics analysis of transgenic mouse models.
Project description:The retina plays a crucial role in processing and decoding visual information, both in normal development and during myopia progression. Recent advancements have introduced a library-independent approach for data-independent acquisition (DIA) analyses. This study demonstrates deep proteome identification and quantification in individual mice retinas during myopia development, with an average of 6,263 ± 86 unique protein groups. We anticipate that this robust quantification and in-depth retinal-specific spectral library will contribute to a better understanding of the proteome complexity of retina. Furthermore, a comprehensive mice retinal-specific spectral library was generated, encompassing a total identification of 9,401 protein groups, 70,041 peptides, 95,339 precursors, and 761,868 transitions acquired using SWATH-MS acquisition on a ZenoTOF 7600 mass spectrometer. This dataset surpasses the spectral library generated through high-pH reversed-phase fractionation by data-dependent acquisition (DDA). The data is available via ProteomeXchange with the identifier PXD046983. It will also serve as an indispensable reference for investigations in myopia research and other retinal or neurological diseases.
Project description:Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.