Biology subjects

Deutsch, E. W.

Publications and source records attributed to Deutsch, E. W..

30 records · Page 2Linked to original sources

bioRxiv2021

The Arabidopsis thaliana PeptideAtlas; harnessing world-wide proteomics data for a comprehensive community proteomics resource

We developed a new resource, the Arabidopsis PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/), to solve central questions about the Arabidopsis proteome, such as the significance of protein splice forms, post-translational modifications (PTMs), or simply obtain reliable information about specific proteins. PeptideAtlas is based on published mass spectrometry (MS) analyses collected through ProteomeXchange and reanalyzed through a uniform processing and metadata annotation pipeline. All matched MS-derived peptide data are linked to spectral, technical and biological metadata. Nearly 40 million out of [~]143 million MSMS spectra were matched to the reference genome Araport11, identifying [~]0.5 million unique peptides and 17858 uniquely identified proteins (only isoform per gene) at the highest confidence level (FDR 0.0004; 2 non-nested peptides [≥] 9 aa each), assigned canonical proteins, and 3543 lower confidence proteins. Physicochemical protein properties were evaluated for targeted identification of unobserved proteins. Additional proteins and isoforms currently not in Araport11 were identified, generated from pseudogenes, alternative start, stops and/or splice variants and sORFs; these features should be considered for updates to the Arabidopsis genome. Phosphorylation can be inspected through a sophisticated PTM viewer. This new PeptideAtlas is integrated with community resources including TAIR, tracks in JBrowse, PPDB and UniProtKB. Subsequent PeptideAtlas builds will incorporate millions more MS data. One sentence summaryA new web resource providing the global community with mass spectrometry-based Arabidopsis proteome information and its spectral, technical and biological metadata integrated with TAIR and JBrowse

plant biology↗

bioRxiv2021

Profiling the Human Phosphoproteome to Estimate the True Extent of Protein Phosphorylation

Mass spectrometry-based phosphoproteomics allows large-scale generation of phosphorylation site data. However, analytical pipelines need to be carefully designed and optimised to minimise incorrect identification of phosphopeptide sequences or wrong localisation of phosphorylation sites within those peptides. Public databases such as PhosphoSitePlus (PSP) and PeptideAtlas (PA) compile results from published papers or openly available MS data, but to our knowledge, there is no database-level control for false discovery of sites, subsequently leading to the likely overestimation of true phosphosites. It is therefore difficult for researchers to assess which phosphosites are "real" and which are likely to be artefacts of data processing. By profiling the human phosphoproteome, we aimed to estimate the false discovery rate (FDR) of phosphosites based on available evidence in PSP and/or PA and predict a more realistic count of true phosphosites. We ranked sites into phosphorylation likelihood sets based on layers of accumulated evidence and then analysed them in terms of amino acid conservation across 100 species, sequence properties and functional annotations of associated proteins. We demonstrated significant differences between the sets and developed a method for independent phosphosite FDR estimation. Remarkably, we estimated a false discovery rate of 86.1%, 95.4% and 82.2% within sets of described phosphoserine (pSer), phosphothreonine (pThr) and phosphotyrosine (pTyr) sites respectively for which only a single piece of identification evidence is available (the vast majority of sites in PSP). Overall, we estimate that [~]56,000 Ser, 10,000 Thr and 12,000 Tyr phosphosites in the human proteome have truly been identified to date, based on evidence in PSP and/or PA, which is lower than most published estimates. Furthermore, our analysis estimated [~]91,000 Ser, 49,000 Thr and 26,000 Tyr sites that are likely to represent false-positive phosphosite identifications. We conclude that researchers should be aware of the significant potential for false positive sites to be present in public databases and should evaluate the evidence behind the phosphosites used in their research.

bioinformatics↗

bioRxiv2020

Universal Spectrum Identifier for mass spectra

Mass spectra provide the ultimate evidence for supporting the findings of mass spectrometry (MS) proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USIs enable greater transparency for providing spectral evidence in support of key findings in publications, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.

bioinformatics↗

bioRxiv2020

A wide-ranging Pseudomonas aeruginosa PeptideAtlas build: a useful proteomic resource for a versatile pathogen.

Pseudomonas aeruginosa is an important opportunistic pathogen with high prevalence in nosocomial infections. This microorganism is a good model for understanding biological processes such as the quorum-sensing response, the metabolic integration of virulence, the mechanisms of global regulation of bacterial physiology, and the evolution of antibiotic resistance. Till now, P. aeruginosa proteomic data, although available in several on-line repositories, were dispersed and difficult to access. In the present work, proteomes of the PAO1 strain grown under very different conditions and from diverse cellular compartments have been analyzed and joined to build the Pseudomonas PeptideAtlas. This resource is a comprehensive mass spectrometry-derived peptide and inferred protein database with 71.3% coverage of the total predicted proteome of P. aeruginosa PAO1. This is the highest published coverage among the eight bacterial PeptideAtlas datasets currently available. The proteins in the Pseudomonas PeptideAtlas cover 84% of metabolic proteins, 71% of proteins involved in genetic information processing, 72% of proteins responsible for environmental information processing, more than 80% of proteins related to quorum sensing and biofilm formation, and 81% of proteins responsible for antimicrobial resistance. It exemplifies a necessary tool for targeted proteomics studies, system-wide observations, and cross-species observational studies. Here we describe how this resource was built and some of the physiologically important proteins of this pathogen. SignificancePseudomonas aeruginosa is among the most versatile bacterial pathogens. Studies of its proteome are very important as they can reveal virulence factors and mechanisms of antibiotic resistance. The construction of a proteomic resource such as the PeptideAtlas enables targeted proteomics studies, system-wide observations, and cross-species observational studies.

microbiology↗

bioRxiv2020

Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison

Here we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can either be provided manually by the user (table format), or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other websites (https://github.com/kusterlab/universal_spectrum_explorer).

bioinformatics↗

bioRxiv2020

mzMLb: a future-proof raw mass spectrometry data format based on standards-compliant mzML and optimized for speed and storage requirements

With ever-increasing amounts of data produced by mass spectrometry (MS) proteomics and metabolomics, and the sheer volume of samples now analyzed, the need for a common open format possessing both file size efficiency and faster read/write speeds has become paramount to drive the next generation of data analysis pipelines. The Proteomics Standards Initiative (PSI) has established a clear and precise XML representation for data interchange, mzML, receiving substantial uptake; nevertheless, storage and file access efficiency has not been the main focus. We propose an HDF5 file format mzMLb that is optimised for both read/write speed and storage of the raw mass spectrometry data. We provide extensive validation of write speed, random read speed and storage size, demonstrating a flexible format that with or without compression is faster than all existing approaches in virtually all cases, while with compression, is comparable in size to proprietary vendor file formats. Since our approach uniquely preserves the XML encoding of the metadata, the format implicitly supports future versions of mzML and is straightforward to implement: mzMLbs design adheres to both HDF5 and NetCDF4 standard implementations, which allows it to be easily utilised by third parties due to their widespread programming language support. A reference implementation within the established ProteoWizard toolkit is provided.

bioinformatics↗

bioRxiv2019

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0

The Human Proteome Organizations (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted [~]20,000 human proteins encoded by the human genome.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC=\"FIGDIR/small/733576v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@8765f2org.highwire.dtl.DTLVardef@136235aorg.highwire.dtl.DTLVardef@14aef76org.highwire.dtl.DTLVardef@af33d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline

Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. Therefore, we developed PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events. Localization of any type of mass modification (e.g., phosphorylation) is supported. PTMProphet applies Bayesian mixture models to compute probabilities for each site/peptide spectrum match where a PTM has been identified. These probabilities can be combined to compute a global false localization rate at any threshold to guide downstream analysis. We describe the PTMProphet tool, its underlying algorithms and demonstrate its performance on ground-truth synthetic peptide reference datasets, one previously published small dataset, one new larger dataset, and also on a previously published phospho-enriched dataset where the correct sites of modification are unknown. Data have been deposited to ProteomeXchange with identifier PXD013210.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioinformatics↗

bioRxiv2019

Proteomics Standards Initiative Extended FASTA Format (PEFF)

Mass spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs), in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI Extended FASTA Format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backwards compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff.

bioinformatics↗

bioRxiv2018

Fast and simple comparison of semi-structured data, with emphasis on electronic health records

We present a locality-sensitive hashing strategy for summarizing semi-structured data (e.g., in JSON or XML formats) into data fingerprints: highly compressed representations which cannot recreate details in the data, yet simplify and greatly accelerate the comparison and clustering of semi-structured data by preserving similarity relationships. Computation on data fingerprints is fast: in one example involving complex simulated medical records, the average time to encode one record was 0.53 seconds, and the average pairwise comparison time was 3.75 microseconds. Both processes are trivially parallelizable.\n\nApplications include detection of duplicates, clustering and classification of semi-structured data, which support larger goals including summarizing large and complex data sets, quality assessment, and data mining. We illustrate use cases with three analyses of electronic health records (EHRs): (1) pairwise comparison of patient records, (2) analysis of cohort structure, and (3) evaluation of methods for generating simulated patient data.

bioinformatics↗

bioRxiv2018

Reproducible big data science: A case study in continuous FAIRness

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility--thus ensuring that big data are not hard-to-(re)use data. We compare and contrast our approach with other approaches to big data analysis and reproducibility.

bioinformatics↗

bioRxiv2017

The proBAM and proBed standard formats: enabling a seamless integration of genomics and proteomics data.

On behalf of The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI), we are here introducing two novel standard data formats, proBAM and proBed, that have been developed to address the current challenges of integrating mass spectrometry based proteomics data with genomics and transcriptomics information in proteogenomics studies. proBAM and proBed are adaptations from the well-defined, widely used file formats SAM/BAM and BED respectively, and both have been extended to meet specific requirements entailed by proteomics data. Therefore, existing popular genomics tools such as SAMtools and Bedtools, and several very popular genome browsers, can be used to manipulate and visualize these formats already out-of-the-box. We also highlight that a number of specific additional software tools, properly supporting the proteomics information available in these formats, are now available providing functionalities such as file generation, file conversion, and data analysis. All the related documentation to the formats, including the detailed file format specifications, and example files are accessible at http://www.psidev.info/probam and http://www.psidev.info/probed.

bioinformatics↗