A search engine independent platform for interpretation of proteomics identification results from multiple search engines, currently supporting X!Tandem, MS-GF+, MS Amanda, OMSSA, MyriMatch, Comet, Tide, Mascot and mzIdentML. By combining the results from multiple search engines, while re-calculating PTM localization scores and redoing the protein inference, PeptideShaker attempts to give you the best possible understanding of your proteomics data.
Analyzes multiple data-independent acquisition (DIA) data files simultaneously. Group-DIA is an untargeted analysis method which combines the elution profiles of precursor ions and fragment ions from all data files to determine precursor-fragment pairs. This method includes the following main steps: (i) Retention-time alignment, (ii) Similarity comparison and spectra generation, and (iii) Peak rediscovery and interference removal.
An R package for analyzing groups of regulated proteins in a network context, e.g. as defined by clusters of protein-protein interactions. NetWeAvers provides a method for analyzing proteomics data integrated with biological networks. The method includes an algorithm for finding dense clusters of proteins and a permutation algorithm to calculate cluster P-values. Optional steps include summarizing quantified peptide values to single protein values and testing for differential expression, such that the data input can simply be a list of identified and quantified peaks.
Allows interpretation of collisional-induced dissociation (CID) data from oligonucleotides. RAMM can map interpreted tandem mass spectrometry (MS/MS) sequences onto RNA sequences. It enables higher throughput RNA modification mapping by liquid chromatography coupled tandem mass spectrometry (LC-MS/MS). This tool supports five different ribonucleases for in silico digestion: RNase T1, RNase U2, RNase A, RNase MC1 and Cusativin.
Determines the validity of peptide identification by calculating p-values from tandem mass spectra (MS/MS). MS-GF can be used in addition to the decoy searches or on its own. The software can also generate a list of all peptides whose score exceeds a threshold and match these in a protein database.
Is dedicated to the visualization and comparison of peptides detected by Tandem mass spectrometry (MS/MS). The principal advantage of Peptigram is that it provides visualizations at both the protein and peptide level, allowing users to simultaneously visualize the peptide distributions of one or more samples of interest, mapped to their parent proteins. In this way rapid comparisons between samples can be made in terms of their peptide coverage and abundance. Moreover, Peptigram integrates and displays key sequence features from external databases and links with peptide analysis tools to offer the user a comprehensive peptide discovery resource.
Permits users to import, integrate, manage and compare numerous proteomics datasets at the same time. PACOM is a Java standalone tool that provides an innovative, easy-to-use and powerful way of data comparison, generating a lot of different charts with protein and peptide features. This method offers a rich set of graphical representation of the most common proteomics data features.
A fully automated computer algorithm that can be applied to complex mass spectra of peptides and proteins. THRASH uses a subtractive peak finding routine to locate possible isotopic clusters in the spectrum. The program should be generally applicable to classes of large molecules, including DNA and polymers. It also includes methods for calculating background noise levels, determining charge state using the Fourier-Transform/Patterson technique, calculating theoretical profiles, and for subsequent fitting with observed isotopic profiles.
Provides extension points to enable built-in import capabilities for binary or textual instrument vendors’ data formats. OpenChrom is an extensible cross-platform open source software for the mass spectrometric analysis of chromatographic data. This application offers extension points that enable the implementation of different baseline detectors as well as peak detectors and integrators. An option to implement filters, used to increase the chromatographic quality, is also available.
Finds optimal feature vectors which are extremely sparse, allowing a highly accurate classification, and robust against noise. Sparse Proteomics Analysis (SPA) is an algorithm based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. Its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data. This method is robust against random and systematic noise.
Performs various analyses based on the proBAM files. proBAMtools includes functions for genome-based proteomics data interpretation, protein and gene inference, count-based quantification, and data integration.
A program that was developed to facilitate the characterization of PTMs using spectral counting and a novel scoring algorithm to accelerate the identification of differential PTMs from complex data sets. The software tool facilitates multi-sample comparison by collating, scoring, and ranking PTMs and by summarizing data visually.
Assigns and interprets the results of electron transfer driven reactions. MassTodon considers a set of known chemical reactions triggered by the electron transfer. This tool contains several functions, such as: (1) the preprocessing of the spectrum; (2) the generation of potentially observable chemical formulas; (3) the deconvolution of spectra; and (4) the pairing of fragment ions, resulting in estimates of the probabilities of the considered reactions and fragmentations.
Assists in enhancing signal processing in data-independent acquisition (DIA)-data. DIA-NN introduces artificial neural nets and a quantification strategy. It implements all stages of DIA data processing in a single program, taking a set of raw data files as input and reporting quantitative values for precursor ions and protein groups. This method was developed to be useful for automated handling of data generated in large-scale experiments.
Processes data and plots a variety of common and less common graphs for the analysis of proteomics datasets. GiaPronto is fully automated and provides both quality control and biological interpretation of protein and post-translational modifications (PTMs) level data. It can be useful for scientists not specialized in proteomics, who want to incorporate proteomic analysis into their research programs.
Allows analysis of mass spectral data based on multivariate analysis. DrDMASS+ performs four stages: (1) Peak Correction that allows correction of experimental m/z values, (2) Multivariate Data Processing that permits assessment of reproducibility of samples with iterative measurement, and selection of useful peaks to separate groups of samples, (3) Unsupervised Learning that allows visualization of multivariate data and (4) Supervised Learning to obtain the regression equation.
Offers a standardized method of protein selection. Annotator addresses the issue of small sample sizes inherent in liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data by using unified heuristics and statistical measures of significance. It furnishes a quantitative method to express conclusions about cellular behavior, independent of standard enrichment analyses or network models.
Provides a way to overview a complete chromatograph analysis. Brukin2D allows visualization, evaluation, procession and comparison of Liquid Chromatography-Mass Spectrometry (LC-MS) data. It incorporates tools to compare two contour plots and corresponding peptide mixes from different chromatograph runs and to find the differences in the compound mass spectra and the chromatogram.
Dr. Yashwanth Subbannayya obtained his M.Sc. degree in Medical Biochemistry from Manipal University. He qualified the competitive CSIR-UGC National Eligibility Test and joined the Institute of Bioinformatics, Bangalore as a UGC Junior Research Fellow. As part of his Ph.D. work, he studied the molecular mechanisms of gastric cancer in clinical specimens using quantitative proteomic technologies. This study, the results of which were published in Cancer Biology and Therapy, yielded a novel therapeutic target for gastric cancer- CAMKK2. Further, he also studied the serum proteome of gastric cancer patients and developed assays for potential markers using the revolutionary multiple reaction monitoring approach. The results of this study were published in Journal of Proteomics. In addition to his research work, he also trained extensively in sample preparation for mass spectrometry, fractionation techniques and gained expertise in quantitative proteomic techniques and data analysis. In addition, he also trained extensively in various validation platforms including immunohistochemsitry, multiple reaction monitoring and Western blot. He has also worked as a curator for several biological databases including NetPath, Human Protein Reference Database (HPRD) and Breast cancer database. His work in various research projects have yielded him 23 publications either as lead author or co-author in peer reviewed journals. He is a reviewer for the journal Proteomics.
Dr. Yashwanth Subbannayya joined the YU-IOB Center for Systems Biology and Molecular Medicine in June, 2015. During the initial period, his job consisted of assisting other personnel of the university in the establishment of YU-IOB Center for Systems Biology and Molecular Medicine. He was also involved in training of Ph.D. students in biological aspects. After the establishment of the center, he trained in cell culture techniques and metabolomics analysis. At YU-IOB CSBMM, he is studying the molecular mechanisms in various cancers including oral cancer. In addition, he is studying the molecular mechanisms as well as the metabolic constituents of traditional medicine formulations using mass spectrometry technologies. In June 2016, he convened the national symposium “Genomics in clinical practice: Future of precision medicine” held at Yenepoya University on June 1 and 2, 2016. The resource persons included 16 individuals from various academic organizations as well as industry. The symposium was attended by 218 participants from 24 institutions around India. He is a member of the Scientific Review Board of Yenepoya Research Centre where he facilitates timely scientific review of research projects.