Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers.

Platform Design Info for Affymetrix miRNA-2_0.

Platform Design Info for Affymetrix AraGene-1_1-st.

This package provides a toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools.

Platform Design Info for Affymetrix RaEx-1_0-st-v1.

Platform Design Info for The Manufacturer's Name MOE430B.

Platform Design Info for The Manufacturer's Name X_tropicalis.

Platform Design Info for NimbleGen feinberg_hg18_me_hx1.

This package provides a data package of SELDI-TOF protein mass spectrometry data of 167 breast cancer and normal samples.

Platform Design Info for Affymetrix CyRGene-1_1-st.

Platform Design Info for The Manufacturer's Name U133_X3P.

The package ptairData contains two raw datasets from Proton-Transfer-Reaction Time-of-Flight mass spectrometer acquisitions (PTR-TOF-MS), in the HDF5 format. One from the exhaled air of two volunteer healthy individuals with three replicates, and one from the cell culture headspace from two mycobacteria species and one control (culture medium only) with two replicates. Those datasets are used in the examples and in the vignette of the ptairMS package (PTR-TOF-MS data pre-processing). There are also used to gererate the ptrSet in the ptairMS data : exhaledPtrset and mycobacteriaSet.

Pviz adapts the Gviz package for protein sequences and data.

This is a supplemental data package for the plotgardener package. Includes example datasets used in plotgardener vignettes and example raw data files. For details on how to use these datasets, see the plotgardener package vignettes.

Platform Design Info for The Manufacturer's Name E_coli_2.

Platform Design Info for Affymetrix RUSGene-1_0-st.

Platform Design Info for Affymetrix MoEx-1_0-st-v1.

The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame.

Platform Design Info for The Manufacturer's Name HG_U95Av2.

The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was Poplar\_probe\_tab.

Platform Design Info for Affymetrix RJpGene-1_0-st.

PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation.

ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings.