Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Homologue series detection and management in LC-MS data with homologueDiscoverer.

ABSTRACT:

Summary

Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities.

Availability and implementation

homologueDiscoverer is freely available at GitHub https://github.com/kevinmildau/homologueDiscoverer.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Mildau K

PROVIDER: S-EPMC9665864 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Homologue series detection and management in LC-MS data with homologueDiscoverer.

Mildau Kevin K van der Hooft Justin J J JJJ Flasch Mira M Warth Benedikt B El Abiead Yasin Y Koellensperger Gunda G Zanghellini Jürgen J Büschl Christoph C

Bioinformatics (Oxford, England) 20221101 22

<h4>Summary</h4>Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their ev ...[more]

PMID: 36165687

Similar Datasets

Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS data.

Project description:In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements.

| S-EPMC3792097 | biostudies-literature

MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data.

Project description:BackgroundThe advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches.ResultsWe have developed the MAss SPECTRometry Analysis System (MASPECTRAS), a platform for management and analysis of proteomics LC-MS/MS data. MASPECTRAS is based on the Proteome Experimental Data Repository (PEDRo) relational database schema and follows the guidelines of the Proteomics Standards Initiative (PSI). Analysis modules include: 1) import and parsing of the results from the search engines SEQUEST, Mascot, Spectrum Mill, X! Tandem, and OMSSA; 2) peptide validation, 3) clustering of proteins based on Markov Clustering and multiple alignments; and 4) quantification using the Automated Statistical Analysis of Protein Abundance Ratios algorithm (ASAPRatio). The system provides customizable data retrieval and visualization tools, as well as export to PRoteomics IDEntifications public repository (PRIDE). MASPECTRAS is freely available at http://genome.tugraz.at/maspectrasConclusionGiven the unique features and the flexibility due to the use of standard software technology, our platform represents significant advance and could be of great interest to the proteomics community.

| S-EPMC1906842 | biostudies-literature

Raw Metabolome (LC-MS) data of Serum

Project description: Not available

| S-HECA171 | biostudies-other

Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data.

Project description:Feature detection is a critical step in the preprocessing of liquid chromatography-mass spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as liquid chromatograph-Fourier transform mass spectrometry (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/ .

| S-EPMC3624888 | biostudies-literature

A data-driven approach for the detection of internal standard outliers in targeted LC-MS/MS assays.

Project description:Heavy-labelled internal standard (IS) compounds are commonly used in liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays to control for stochastic and systematic variation. Identifying samples that suffer from unwanted variation is critically important in order to avoid factitiously inaccurate results. Current approaches for outlier detection typically employ arbitrary thresholds and ignore systematic drift. To improve this, we applied robust linear mixed-effects models (LMMs) to capture the within- and between-run variability in IS signal and generate data-driven acceptance ranges for routine use. Data from in-house LC-MS/MS assays for 25-hydroxyvitamin D3 and D2 and prednisolone were retrospectively collected. The variation in the percentage deviation of the internal standard area from the mean of the calibrators was modelled through the use of robust LMMs. The fitted LMMs revealed significant positive drift in IS signal over the analytical runs for vitamin D, with slope coefficients of 0.118 (95% CI: 0.098, 0.138) and 0.192 (0.168, 0.215) for D3 and D2, respectively. In contrast, the models for prednisolone demonstrated a significant negative drift in IS signal, with a slope coefficient of -0.164 (-0.297, -0.036). Non-parametric, cluster bootstrap resampling enabled us to define acceptance ranges for use in future assays. Here, we have described a computational approach to extensively characterise the variation in IS signal in routinely-performed LC-MS/MS assays. This approach facilitates a robust quality assessment of IS outliers in routine practice and thus has the potential to improve patient safety. Importantly, this approach is applicable to other MS assays where linear variation in IS signal is observed.

| S-EPMC8600994 | biostudies-literature

Interrogating data-independent acquisition LC-MS/MS for affinity proteomics.

Project description:Data-Independent Acquisition (DIA) LC-MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities.Supplementary informationThe online version contains supplementary material available at 10.1007/s42485-024-00166-4.

| S-EPMC11452513 | biostudies-literature

Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS.

Project description:Data-independent acquisition LC-MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which, in turn, is detrimental for accurate quantification. The nonoutlier fragment ion (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high-priority fragment ions, these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative data set (i.e., the SWATH Gold Standard) indicates that NOFI on average is able to accurately quantify 11-25% more peptides than the commonly used Top-N library intensity ranking method. The sum of the area of the Top3-5 NOFIs produces similar coefficients of variation as compared to that with the library intensity method but with more accurate quantification results. On a biologically relevant human dendritic cell digest data set, NOFI properly assigns low-priority ranks to 85% of annotated interferences, resulting in sensitivity values between 0.92 and 0.80, against 0.76 for the Spectronaut interference detection algorithm.

| S-EPMC4739871 | biostudies-literature

Identifying proteomic LC-MS/MS data sets with Bumbershoot and IDPicker.

Project description:The identification of peptides and proteins by LC-MS/MS requires the use of bioinformatics. Tools developed in the Tabb Laboratory contribute significant flexibility and discrimination to this process. The Bumbershoot tools (MyriMatch, DirecTag, TagRecon, and Pepitome) enable the identification of peptides represented by MS/MS scans. All of these tools can work directly from instrument capture files of multiple vendors, such as Thermo RAW format, or from standard XML-based formats, such as mzML or mzXML. Peptide identifications are written to mzIdentML or pepXML format. Protein assembly is handled by the IDPicker algorithm. Raw identifications are filtered to a confident set by use of the target-decoy strategy. IDPicker arranges large sets of input files into a hierarchy for reporting, and the software applies a parsimony algorithm to report the smallest possible number of proteins to explain the observed peptides. This protocol details the use of these tools for new users.

| S-EPMC4547833 | biostudies-literature

Peptide Markers for Rapid Detection of KPC Carbapenemase by LC-MS/MS.

Project description:Carbapenemase producing organisms (CPOs) represent an urgent public health threat, and the need for new rapid methods to detect these organisms has been widely recognized. CPOs carrying the Klebsiella pneumoniae carbapenemase (bla KPC ) gene have caused outbreaks globally with substantial attributable mortality. Here we describe the validation of a rapid MS method for the direct detection of unique tryptic peptides of the KPC protein in clinical bacterial isolates with an isolate-to-result time of less than 90 minutes. Using a genoproteomic discovery approach that combines theoretical peptidome analysis and liquid chromatography-tandem MS (LC-MS/MS), we selected three high abundance peptide markers of the KPC protein that can be robustly detected following rapid tryptic digestion. Protein BLAST analysis confirmed that the chosen peptide markers were unique to KPC. A blinded validation set containing 20 KPC-positive and 80 KPC-negative clinical isolates, performed in triplicate (300 runs) demonstrated 100% sensitivity and 100% specificity (60/60 positive identifications, 240/240 negative identifications) using defined rules for positive calls. The most robust tryptic peptide marker in the validation was LTLGSALAAPQR. The peptide discovery and detection methods validated here are general and should be broadly applicable to allow the direct and rapid detection of other resistance determinants.

| S-EPMC5451396 | biostudies-literature

Rapid detection of colistin resistance protein MCR-1 by LC-MS/MS.

Project description:BackgroundColistin (polymyxin E) and polymixin B are important bactericidal antibiotics used in the treatment of serious infections caused by multi-drug resistant Gram-negative organisms. Transferrable plasmid-mediated colistin resistance, conferred by the product of the mcr-1 gene, has emerged as a global healthcare threat. Consequently, the rapid detection of the MCR-1 protein in clinical bacterial isolates has become increasingly important. We used a genoproteomic approach to identify unique peptides of the MCR-1 protein that could be detected rapidly by liquid chromatography tandem mass spectrometry (LC-MS/MS).MethodsMCR-1 tryptic peptides that were efficiently ionized and readily detectable were characterized in a set of mcr-1-containing isolates with triple quadrupole LC-MS. Three optimal peptides were selected for the development of a rapid multiple reaction monitoring LC-MS/MS assay for the MCR-1 protein. To investigate the feasibility of rapid detection of the MCR-1 protein in bacterial isolates using this assay, a blinded 99-sample test set was built that included three additional mcr-1-containing clinical isolates tested in triplicate (9 samples) and 90 negative control isolates.ResultsAll of the mcr-1-containing isolates in the test set were accurately identified with no false positive detections by three independent, blinded operators, yielding an overall performance of 100% sensitivity and specificity for multiple operators. Among the three peptides tested in this study, the best performing was DTFPQLAK. The isolate-to-result time for the assay as implemented is less than 90 min.ConclusionsThis work demonstrates the feasibility of rapid detection of the MCR-1 protein in bacterial isolates by LC-MS/MS.

| S-EPMC6390366 | biostudies-literature

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