This package provides tools for large-scale protein motif analysis and visualization in R. PMScanR facilitates the identification of motifs using external tools like PROSITE's ps_scan (handling necessary file downloads and execution) and enables downstream analysis of results. Key features include parsing scan outputs, converting formats (e.g., to GFF-like structures), generating motif occurrence matrices, and creating informative visualizations such as heatmaps, sequence logos (via seqLogo/ggseqlogo). The package also offers an optional Shiny-based graphical user interface for interactive analysis, aiming to streamline the process of exploring motif patterns across multiple protein sequences.

Platform Design Info for Affymetrix RaGene-1_1-st-v1.

Platform Design Info for The Manufacturer's Name MG_U74B.

PWMEnrich pre-compiled background objects for Drosophila melanogaster and MotifDb D. melanogaster motifs.

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

An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained.

Platform Design Info for The Manufacturer's Name HG_U95B.

Platform Design Info for The Manufacturer's Name Bsubtilis.

This package provides a package containing an environment representing the Poplar.cdf file.

Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies.

ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009).

Platform Design Info for The Manufacturer's Name MG_U74Cv2.

Platform Design Info for Affymetrix Atdschip_tiling.

Platform Design Info for The Manufacturer's Name Tomato.

This package provides a Bioconductor data package for the Ross-Adams (2015) Prostate Cancer dataset.

Platform Design Info for The Manufacturer's Name Barley1.

Demultiplexing and filtering utilities intended for reads with combinatorial barcodes (i.e. PETRI-seq and SPLiT-seq). The demultiplexer algorithm uses the position of the segments to extract and compare the barcodes with the reference (whitelist). A Shiny application is provided to interactively select cutoffs for which barcode combinations to keep.

FHCRC Nelson Lab pedbarrayv10 Annotation Data (pedbarrayv10) assembled using data from public repositories.

Two experimental datasets to illustrate running and analysing phylogenetic profiles with PhyloProfile package.

Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups.

In genomics, differential analysis enables the discovery of groups of genes implicating important biological processes such as cell differentiation and aging. Non-parametric tests of differential gene expression usually detect shifts in centrality (such as mean or median), and therefore suffer from diminished power against alternative hypotheses characterized by shifts in spread (such as variance). This package provides a flexible family of non-parametric two-sample tests and K-sample tests, which is based on theoretical work around non-parametric tests, spacing statistics and local asymptotic normality (Erdmann-Pham et al., 2022+ [arXiv:2008.06664v2]; Erdmann-Pham, 2023+ [arXiv:2209.14235v2]).

The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats.

This package provides tools for TMT based quantitative proteomics data analysis.

The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data.