Estimation of peptide elongation times from ribosome profiling spectra
Ontology highlight
ABSTRACT:
ORGANISM(S): Escherichia coli
PROVIDER: GSE145571 | GEO | 2021/04/23
REPOSITORIES: GEO
Similar Datasets
Project description:Estimation of peptide elongation times from ribosome profiling spectra
| PRJNA607566 | ENA
Project description:Ribosome profiling spectra bear rich information on translation control and dynamics. Yet, due to technical biases in library generation, extracting quantitative measures of discrete translation events has remained elusive. Using maximum likelihood statistics and data set from Escherichia coli we develop a robust method for neutralizing technical biases (e.g. base specific RNase preferences in ribosome-protected mRNA fragments (RPF) generation), which allows for correct estimation of translation times at single codon resolution. Furthermore, we validated the method with available datasets from E. coli treated with antibiotic to inhibit isoleucyl-tRNA synthetase, and two datasets from Saccharomyces cerevisiae treated with two RNases with distinct cleavage signatures. We demonstrate that our approach accounts for RNase cleavage preferences and provides bias-corrected translation times estimates. Our approach provides a solution to the long-standing problem of extracting reliable information about peptide elongation times from highly noisy and technically biased ribosome profiling spectra.
| S-EPMC8136808 | biostudies-literature
Project description:Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we propose a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrated that predicted spectral libraries can be used for analyzing DIA data of glycopeptides as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.
2023-12-17 | PXD045248 |
Project description:I evaluated TIMSTOF single cell fragmentation spectra (real human single cells) for previously reported y-ion fragment issues. While I see these in Orbitrap data as previously reported, I do not see these at the same levels in TOF spectra.
2022-11-03 | MSV000090635 | MassIVE
Project description:RPF-1 binds promoters of genes modulated by its induction in HEK/RPF-1 transfectant Chromatin IP (ChIP) combined to chromatin array analysis (ChIP-chip) shows that RPF-1 transcription factor bind to promoter elements of genes repressed or activated by RPF-1 expression
2016-09-06 | GSE55270 | GEO
Project description:Abstract Here we present IPSA, an innovative web-based spectrum annotator that visualizes and characterizes peptide tandem mass spectra. A tool for the scientific community, IPSA can visualize peptides collected using a wide variety of experimental and instrumental configurations. Annotated spectra are customizable via a selection of interactive features and can be exported as editable scalable vector graphics to aid in the production of publication-quality figures. Single spectra can be analyzed through provided forms, while multiple peptide spectral matches can be uploaded directly to the server as CSV, MGF, or mzML files. IPSA facilitates the characterization of experimental MS/MS performance through the optional export of fragment ion statistics from one to many peptide spectral matches. This resource is made freely accessible at http://interactivepeptidespectralannotator.com, and the source code is available for inspection at https://github.com/dbrademan/IPSA-dev for custom implementations. This repository contains the raw data, sequence databases, and peptide identifications utilized in the manuscript.
2019-05-15 | PXD011695 | Pride
Project description:RPF-1 binds promoters of genes modulated by its induction in HEK/RPF-1 transfectant Chromatin IP (ChIP) combined to chromatin array analysis (ChIP-chip) shows that RPF-1 transcription factor bind to promoter elements of genes repressed or activated by RPF-1 expression Comparison of cells induced for RPF-1 expression (Tet-) over uninduced control cells (Tet+)
2016-09-06 | E-GEOD-55270 | biostudies-arrayexpress
Project description:PurposeReliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) 1 H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T2 relaxation times for seven MM components.MethodsA single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo 1 H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel.ResultsA very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T2 ranged between 12 and 24 ms for seven MM peaks.ConclusionModerate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.
| S-EPMC8596437 | biostudies-literature
Project description:RPF-1, a transcription factors encoded by POU6F2 gene, appears to be expressed in the developing nervous system and embryonic kidney. We generated a stable HEK/RPF-1 transfectant expressing the protein in a tetracycline (Tet)-dependent induction and addressed the whole transcriptome regulated by RPF-1. We detected a gross alteration of gene expression that is consistent with its occupancy at proximal promoters, and gel retardation assay and promoter-reporter activity, addressing a few target genes, indicated a dependence by RPF-1 of transcriptional response. GO analysis supports a role for this factor in the genetic programs guiding early differentiation of embryonic kidney and neuronal fate. RPF-1, a transcription factors encoded by POU6F2 gene, appears to be expressed in the developing nervous system and embryonic kidney. We generated a stable HEK/RPF-1 transfectant expressing the protein in a tetracycline (Tet)-dependent induction and addressed the whole transcriptome regulated by RPF-1. We detected a gross alteration of gene expression that is consistent with its occupancy at proximal promoters, and gel retardation assay and promoter-reporter activity, addressing a few target genes, indicated a dependence by RPF-1 of transcriptional response. GO analysis supports a role for this factor in the genetic programs guiding early differentiation of embryonic kidney and neuronal fate. Gene array analysis shows that RPF-1 transcription factor modify the transcriptome of HEK293 cells by regulating a genetic program endowed with developmental programs Comparison of HEK293 cells induced for RPF-1 expression (Tet-) over uninduced control (Tet+) and Mock cells. RPF-1 cDNA was inserted into the pTRE2puro vector to generate stable Tet-inducible KEK293 transfectants. Upon Tet removal from the medium, HEK/RPF-1 transfectants expressed the transcription factor RPF-1. Cells were then seeded in 10 cm dishes, cultured for 48 h either in complete medium supplemented with Tet (+) or in Tet-depleted medium (-), harvested, and spun down before RNA isolation. Comparative analysis between HEK/RPF-1 induced (Tet-) and uninduced (Tet+) transfectants allowed us to identify genes transcriptionally regulated by RPF-1. Residual effects on gene expression due to Tet supplementation into culture media was ruled out by comparison with the Mock transfectant.
2016-09-06 | E-GEOD-55472 | biostudies-arrayexpress
Project description:Despite the development of massively parallel computing hardware including inexpensive graphics processing units (GPUs), it has remained infeasible to simulate the folding of atomistic proteins at room temperature using conventional molecular dynamics (MD) beyond the microsecond scale. Here, we report the folding of atomistic, implicitly solvated protein systems with folding times τ ranging from ∼10 μs to ∼100 ms using the weighted ensemble (WE) strategy in combination with GPU computing. Starting from an initial structure or set of structures, WE organizes an ensemble of GPU-accelerated MD trajectory segments via intermittent pruning and replication events to generate statistically unbiased estimates of rate constants for rare events such as folding; no biasing forces are used. Although the variance among atomistic WE folding runs is significant, multiple independent runs are used to reduce and quantify statistical uncertainty. Folding times are estimated directly from WE probability flux and from history-augmented Markov analysis of the WE data. Three systems were examined: NTL9 at low solvent viscosity (yielding τf = 0.8-9 μs), NTL9 at water-like viscosity (τf = 0.2-2 ms), and Protein G at low viscosity (τf = 3-200 ms). In all cases, the folding time, uncertainty, and ensemble properties could be estimated from WE simulation; for Protein G, this characterization required significantly less overall computing than would be required to observe a single folding event with conventional MD simulations. Our results suggest that the use and calibration of force fields and solvent models for precise estimation of kinetic quantities is becoming feasible.
| S-EPMC6660137 | biostudies-literature