Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

De novo nine-species benchmark peptide identifications for Casanovo and other methods

ABSTRACT: Predicted peptides for the 9-species de novo sequencing benchmark MSV000090982 as described in Yilmaz et al. [Yilmaz2023]. FTP directory contains outputs of 5 de novo peptide sequencing methods on the 9-species benchmark: Casanovo, Casanovo_bm (benchmark), PointNovo, DeepNovo and Novor. Output files for Casanovo contain scan numbers and run names to allow matching to spectra files. [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux*, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

INSTRUMENT(S): Q Exactive

ORGANISM(S): Bacillus Subtilis (ncbitaxon:1423) Candidatus Thiodiazotropha Endoloripes (ncbitaxon:1818881) Vigna Mungo (ncbitaxon:3915) Solanum Lycopersicum (ncbitaxon:4081) Saccharomyces Cerevisiae (ncbitaxon:4932) Homo Sapiens (ncbitaxon:9606) Mus Musculus (ncbitaxon:10090) Methanosarcina Mazei (ncbitaxon:2209) Apis Mellifera (ncbitaxon:7460)

SUBMITTER: William Noble

PROVIDER: MSV000094434 | MassIVE | Thu Mar 28 21:29:00 GMT 2024

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

De novo nine-species benchmark peptide identifications for Casanovo and other methods

Project description:Predicted peptides for the 9-species de novo sequencing benchmark MSV000090982 as described in Yilmaz et al. [Yilmaz2023]. FTP directory contains outputs of 5 de novo peptide sequencing methods on the 9-species benchmark: Casanovo, Casanovo_bm (benchmark), PointNovo, DeepNovo and Novor. [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux*, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

2024-02-01 | MSV000093979 | MassIVE

Casanovo de novo peptide sequencing immunopeptidomics, metaproteomics and dark proteome

Project description:Predicted peptides for the immunopeptidomics, metaproteomics and dark proteome samples described in Yilmaz et al. [Yilmaz2023]. FTP directory contains mzTab outputs for Casanovo for these 3 datasets. [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux*, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

2024-02-01 | MSV000093980 | MassIVE

NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing

Project description:De novo peptide sequencing is a fundamental research area in mass spectrometry (MS) based proteomics. However, those methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) and the significance of de novo peptide-spectrum matches (PSMs). Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species), and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide sequencing methods on target-decoy spectra and to estimate their FDRs. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide sequencing methods, and how their performances depend on specific applications and the types of data. Our FDR estimation also shows that some tools may perform better than the others in distinguishing between de novo PSMs and random matches, and can be used to assess the significance of de novo PSMs.

2024-08-28 | PXD055277 | Pride

Soil metaproteomics data analysis based on de novo sequencing with the deep learning-based Kaiko model

Project description:We generated a protein database directly from soil metaproteomic data by identifying the microbial composition using the Kaiko model's de novo sequencing methods. We first analyzed the mass spectra de novo (without a database), identifying species from the observed peptides. We next gathered full proteomic databases for the identified species and searched the mass spec data using MS-GF+ and this custom-assembled protein sequence database.

2020-10-20 | MSV000086336 | MassIVE

Application of de novo sequencing to large-scale complex proteomics datasets

Project description:Dependent on concise, pre-defined protein sequence databases, traditional search algorithms perform poorly when analyzing mass spectra derived from wholly uncharacterized protein products. Conversely, de novo peptide sequencing algorithms can interpret mass spectra without relying on reference databases. However, such algorithms have been difficult to apply to complex protein mixtures, in part due to a lack of methods for automatically validating de novo sequencing results. Here, we present novel metrics for benchmarking de novo sequencing algorithm performance on large scale proteomics datasets, and present a method for accurately calibrating false discovery rates on de novo results. We also present a novel algorithm (LADS) which leverages experimentally disambiguated fragmentation spectra to boost sequencing accuracy and sensitivity. LADS improves sequencing accuracy on longer peptides relative to other algorithms and improves discriminability of correct and incorrect sequences. Using these advancements, we demonstrate accurate de novo identification of peptide sequences not identifiable using database search-based approaches.

2016-01-12 | PXD003317 | Pride

Bering Strait surface water and Chukchi Sea bottom water microbiome metaproteomics

Project description:Ocean microbiome dataset published by May et al. [May2016] and the corresponding peptide identifications. The LC-MS/MS spectra are from triplicate acquisitions of peptides, acquisitions 51-53 from the Bering Strait (BSt) and acquisitions 45-47 from the Chukchi Sea (CS). For each sampling location, there are two sets of peptide identifications: one based on a metapeptide database specific to the location and one based on a non-redundant environmental database. Peptide identifications were obtained with Tide search and Percolator as described in Yilmaz et al. [Yilmaz2023]. [May2016] May, D. H. et al. "An Alignment-Free Metapeptide Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing", Journal of Proteome Research, 2016. [Yilmaz2023] Yilmaz, Melih et al. "Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023.

2024-03-27 | MSV000094415 | MassIVE

Assessing protein sequence database suitability using de novo sequencing

Project description:The analysis of samples from unsequenced and/or understudied species as well as samples where the proteome is derived from multiple organisms poses two key questions. The first is whether the proteomic data obtained from an unusual sample type even contains peptide tandem mass spectra. The second question is whether an appropriate protein sequence database is available for proteomic searches. We describe the use of automated de novo sequencing for evaluating both the quality of a collection of tandem mass spectra and the suitability of a given protein sequence database for searching that data. Applications of this method include the proteome analysis of closely related species, metaproteomics, and proteomics of extant organisms.

2019-11-20 | PXD015083 | Pride

Upload for 2012 MCP manuscript - "Shotgun protein sequencing with meta-contig assembly."

Project description:Shotgun protein sequencing with meta-contig assembly. Full-length de novo sequencing from tandem mass (MS/MS) spectra of unknown proteins such as antibodies or proteins from organisms with unsequenced genomes remains a challenging open problem. Conventional algorithms designed to individually sequence each MS/MS spectrum are limited by incomplete peptide fragmentation or low signal to noise ratios and tend to result in short de novo sequences at low sequencing accuracy. Our shotgun protein sequencing (SPS) approach was developed to ameliorate these limitations by first finding groups of unidentified spectra from the same peptides (contigs) and then deriving a consensus de novo sequence for each assembled set of spectra (contig sequences). But whereas SPS enables much more accurate reconstruction of de novo sequences longer than can be recovered from individual MS/MS spectra, it still requires error-tolerant matching to homologous proteins to group smaller contig sequences into full-length protein sequences, thus limiting its effectiveness on sequences from poorly annotated proteins. Using low and high resolution CID and high resolution HCD MS/MS spectra, we address this limitation with a Meta-SPS algorithm designed to overlap and further assemble SPS contigs into Meta-SPS de novo contig sequences extending as long as 100 amino acids at over 97% accuracy without requiring any knowledge of homologous protein sequences. We demonstrate Meta-SPS using distinct MS/MS data sets obtained with separate enzymatic digestions and discuss how the remaining de novo sequencing limitations relate to MS/MS acquisition settings.

2014-01-16 | MSV000078530 | MassIVE

Prochlorococcus marinus MED4 shotgun proteome dataset from “Protein cycling in the eastern tropical North Pacific oxygen deficient zone: a de novo-discovery peptidomic approach”

Project description:For this manuscript, the Prochlorococcus MED4 strain shotgun proteome dataset was used for benchmarking a de novo-directed sequencing approach. De novo peptide sequencing, where the sequence of amino acids is determined directly from mass spectra rather than by comparison (or peptide spectrum matching) to a selected database. We perform a benchmarking experiment using Prochlorococcus culture data, demonstrating de novo peptides are sufficiently accurate and taxonomically specific to be useful in environmental studies. The MED4 dataset herein represents the output from peptide spectrum matching using COMET within the transproteomic pipeline (TPP). Additional MED4 data outside this manuscript are included for both trypsin and Glu-C protease digestions as well as TPP output for post-translational modification searches. De novo output data derived from Peaks Studio can be found by referencing the manuscript publication.

2022-01-06 | PXD027589 | Pride

A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data

Project description:A core computational challenge in the analysis of mass spectrometry data is the de novo sequencing problem, in which the generating amino acid sequence is inferred directly from an observed fragmentation spectrum without the use of a sequence database. Recently, deep learning models have made significant advances in de novo sequencing by learning from massive datasets of high confidence labeled mass spectra. However, these methods are primarily designed for data-dependent acquisition (DDA) experiments. Over the past decade, the field of mass spectrometry has been moving toward using data-independent acquisition (DIA) protocols for the analysis of complex proteomic samples due to their superior specificity and reproducibility. Hence, we present a new de novo sequencing model called Cascadia, which uses a transformer architecture to handle the more complex data generated by DIA protocols. In comparisons with existing approaches for de novo sequencing of DIA data, Cascadia achieves improved performance across a range of instruments and experimental protocols. Additionally, we demonstrate Cascadia’s ability to accurately discover de novo coding variants and peptides from the variable region of antibodies.

2024-06-21 | PXD053291 | panorama

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data