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Peak alignment software tools | Mass spectrometry-based untargeted proteomics

Liquid chromatography-mass spectrometry is widely used for comparative replicate sample analysis in proteomics, lipidomics and metabolomics. Before statistical comparison, registration must be established to match corresponding analytes from run to run. Alignment, the most popular correspondence approach, consists of constructing a function that warps the content of runs to most closely match a given reference sample.

Source text:
(Smith et al., 2015) LC-MS alignment in theory and practice: a comprehensive algorithmic review. Brief Bioinform.

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Allows to manage and analyse Liquid chromatography coupled to mass spectrometry (LC-MS) data. OpenMS is a programming library and tool collection integrated into full-featured workflow systems, such as KNIME, Galaxy and WS-PGRADE, to facilitate bioinformatics research in the field of MS on all levels. The software provides pre-built and ready-to-use tools for analysis of both proteomics and non-targeted metabolomics data.
An LC/MS-based data analysis approach which incorporates novel nonlinear retention time alignment, feature detection, and feature matching. The XCMS software reads and processes LC/MS data stored in netcdf , mzXML, mzData and mzML files. It provides methods for feature detection, non-linear retention time alignment, visualization, relative quantization and statistics. XCMS is capable of simultaneously preprocessing, analyzing, and visualizing the raw data from hundreds of samples. XCMS is freely available under an open-source license.
ptw / Parametric Time Warping
Aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation).
AMSRPM / AMSRobust Point Matching
A package for the registration of either chromatograms or peak lists. AMSRPM implements a variant of the Robust Point Matching algorithm that is tailored for the alignment of LC and LC/MS experiments. The algorithm does not rely on pre-specified landmarks, it is insensitive towards outliers and capable of modeling nonlinear distortions. The main limitation of amsrpm currently is the computational burden involved in repetitively calculating the distance matrix and the smooth monotone regression estimate.
Allows to view mass spectrometry (MS1) data in an mzXML file. msInspect is An open-source application comprising algorithms and visualization tools for the analysis of multiple LC-MS experimental measurements. It can be used to built-in tools to inspect data, identify peptide features, generate peptide arrays using data from multiple runs, and export data to external applications for further analysis and collaboration. It offers also a full Accurate Mass and Time (AMT) workflow for combining LC-MS and LC-MS/MS peptides.
Regression-based alignment methods
Permits a linear and non-linear retention time (rt) alignment of peak-picked data in liquid chromatography and mass spectrometry (LC/MS) runs. Many alignment methods have been proposed in the literature and each of them need huge amounts of computer memory due to the size of the input data, thus making it impractical the analyse of hundreds of runs. This procedure of alignment seems to be highly competitive compared to many other available algorithms.
Combines hierarchical pairwise correspondence estimation with simultaneous alignment and global retention time correction. SIMA employs a tailored multidimensional kernel function and a procedure based on maximum likelihood estimation to find the retention time distortion function that best fits the observed data. In a comparison with seven alternative methods on four different datasets, SIMA yields competitive and superior performance on real-world data. Conceptually, SIMA is not limited to the alignment of LC/MS data: by redefinition of the thresholded squared Mahalanobis distance function, it can easily be adapted to any time series with discrete events
MetPP / METabolomics Profiling Pipeline
Analyzes metabolomics data generated from the 2D gas chromatography time-of-flight mass spectrometry (GCxGC-TOF MS) instrument. MetPP is able to recognize the concentration difference of the spiked-in metabolite standards between sample groups. It is composed of different modules for: retention index matching, peak filtering and merging, peak list alignment, quant mass conversion, normalization, statistical significance tests and pattern recognition.
A direct matching peak alignment method for LC/MS data that incorporates related peaks information (within each LC/MS run) and investigate its effect on alignment performance (across runs). The groupings of related peaks necessary for this method can be obtained from any peak clustering method and are built into a pairwise peak similarity score function. The similarity score matrix produced is used by an approximation algorithm for the weighted matching problem to produce the actual alignment result.
DISCO / DIstance and Spectrum Correlation Optimization
Conducts alignments for two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS)-based metabolomics. DISCO uses Euclidean distances of two-dimensional retention times and mass spectrum correlation of two corresponding metabolite peaks. It can correct the two-dimensional retention time shifts of all metabolite peaks present in each peak list using a two-step approach. This tool is useful for pattern recognition and statistical significance testing in metabolomics study.
Performs retention time alignment on set of peaks. CowCoDA combines mass chromatogram selection using CODA with a modified COW algorithm in order to take the local information in Liquid Chromatography-Mass Spectrometry (LC-MS) chromatograms into account. The performance of the COW-CODA algorithm was evaluated on three types of complex data sets obtained from the LC-MS analysis of samples commonly used for biomarker discovery. CowCoDA is also applicable to high-resolution data using Gaussian smoothing and data reduction in mass dimension.
A software tool for the analysis of MALDI data. This is an application that covers the whole process of MALDI data analysis, from data preprocessing to complex data analyses. Mass-Up incorporates the most common analyses, aside from protein identification and focusing in biomarker discovery, such as statistical tests-based biomarker discovery, clustering, PCA, and classification. In addition, other less common analyses such as quality control and biclustering are also included. Therefore, Mass-Up provides users with a wide range of tools to analyze and explore their MALDI data.
Generates a peptide versus sample array from a set of Liquid Chromatography-Mass Spectrometry (LC-MS) data. SpecArray is a software suite that fulfills the important second step of the LC-MS approach for quantitative proteomics. Data collected from four repeat LC-MS analyses of a glycoFIG peptide sample were used to illustrate the main features of SpecArray. The software can be useful for analyzing LC-MS data and can serve as an effective software platform in the LC-MS approach for quantitative proteomics.
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An automated analysis tool for label-free quantitative proteomics. IDEAL-Q accepts mzXML raw data format and Mascot xml and ProtXML/PepXML for identification result. IDEAL-Q uses an elution time prediction and peak alignment algorithms to quantify peptides across different LC-MS runs and increase quantitation coverage. Furthermore, the tool adopts a stringent validation step on Signal-to-noise ratio, Charge state, isotopic distribution (SCI validation) to ensure quantitation accuracy. IDEAL-Q provides variously optional normalization tools for flexible workflow design such as addition of fractionation strategies and multiple spiked internal standards.
HDP-Align / Hierarchical Dirichlet Process Alignment
Aligns peaks via a hierarchical cluster model using both peak mass and retention time. HDP-Align provides confidence values in the form of posterior probabilities allowing the user to distinguish between aligned peaksets of high and low confidence. HDP-Align is able to produce alignment results competitive to the benchmark alignment methods, with the added benefit of being able to provide a measure of confidence in the alignment quality.
Contains functions to align peaks from gas chromatography (GC) and GC-mass spectrometry (MS) data based on retention times and evaluate the resulting alignments. GCalignR, a user-friendly R package for aligning GC data based on retention times. It provides a simple tool that guides the user through the alignment of large datasets prior to the statistical analysis of multivariate chemical data. The package also implements a suite of dynamic visualisation tools to facilitate inspection of the resulting alignments and can be integrated within a broader workflow in R to facilitate downstream multivariate analyses.
Constructs optimal signal maps between mass spectrometry (MS) experiments and a practical application where these can be used to increase sensitivity and throughput. ChAMS was developed to rely on signal maps that associate raw data between experiments and the deferral of individual feature recognition to the last analysis stage. This approach can be expected to decrease inter- and intra-experiment biases and improve the signal-to-noise ratio, thus improving sensitivity and throughput.
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