Assists users to detect candidate proteins whose abundances are post-transcriptionally controlled by the anaphase promoting complex/cyclosome (APC/C). PSS is a program utilizing abundance profiles from quantitative proteomics experiments with the isobaric mass tagging (IMT) strategy. It introduces advances in exploratory data analysis that enables protein-level inference based on peptide-level measurements.
Calculates chemical formulas for fragment ions where the structure of the molecular ion is known. FFC is able to cope with general molecular rearrangement reactions occurring during GC/MS measurements.
Allows automated predictive analysis of mass spectrometry data. FAST-AIMS offers a sequence of analysis in aid of developing protocol standardization for proteomic data analysis. The software can (a) generate a classification model by optimizing the parameters of analysis algorithms to ensure its optimal performance; (b) obtain an unbiased estimate of the future classification performance of the optimized model; (c) generate a model and estimate classification performance in tandem; and (d) apply an existing model to a new set of samples.
Assists users in the automatization of species identification. This algorithm is a machine learning approach that aims to reduce the manual work required for analyzing high-throughput collagen peptide mass fingerprints (PMFs) data of ancient bone samples. This method was able to reach taxonomic resolution at family/sub-family levels within the vertebrata.
Permits users to utilize mass spectral data to detect miRNA by sequence, name, mass, accession number, and RNA modification. MicroRNA MultiTool contains features for eliminating the need of performing tandem mass spectrometry on a miRNA for the sole purpose of its identification. In summary, it has three main functionalities: (1) miRNA search and mass calculator; (2) modified miRNA mass calculator; and (3) miRNA fragment search.
Detects urine peptide biomarkers, which can discriminate healthy controls from the oncological samples. CancerUBM employs the urine mass spectrometric data to proceed. It can be used to validate biomarkers for a variety of diseases and physiological changes. This tool includes models devoted to the analysis and prediction of oncological status from proteomics data. It provides three options to make its prediction: Mass-CE Spectra, Peptide Sequence and Protein Expression.
Measures the mass of a peptide or protein sequence by selecting average mass results. NIST Mass and Fragment Calculator proceeds by calculating sequence masses with the accumulated occurrences of all elements within each residue contained in the input sequence. Users can execute calculations on multi-subunit proteins by splitting sequences with an ampersand. This tool allows users to display the total mass of the sub-unit and pyroglutamation, glycosylation and disulfide bonds.
Uses MALDI-TOF mass spectra fingerprints to track bacteria, yeasts and fungi, including agar plates-grown ones, in a given sample. SARAMIS offers an identification system leaning on an updated database allowing users to investigate colonies, including at sub-species levels.
Intends to analyze mass spectrometry (MS) proteomics data. Pyproteome is a python program that contains several modules for loading, processing, and studying proteomics data collected by mass spectometry. It allows users to automatically filter, normalize, and merge together data from proteome search files. Moreover, it is able to cluster peptides that show correlated changes, and perform motif and pathway enrichment analysis to study cell signaling events.
Selects important genes/biomarkers for the classification of noisy data. R-SVM consists of a method that aims to analyze high-throughput proteomics and microarray data and recover informative features. It uses the class means to represent the samples for feature selection. It also includes features to avoid taking the outlier samples as support vectors (SVs).
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