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PWMEnrich pre-compiled background objects for H. sapiens (human) and MotifDb H. sapiens motifs.
The package pRolocGUI comprises functions to interactively visualise spatial proteomics data on the basis of pRoloc, pRolocdata and shiny.
This package contains the data employed in the vignette of the PathNet package. These data belong to the following publication: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J., Source Code for Biology and Medicine 2012 Sep 24;7(1):10.
The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> for more details.
Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources.
Platform Design Info for The Manufacturer's Name Zebrafish.
Platform Design Info for The Manufacturer's Name Cotton.
Platform Design Info for The Manufacturer's Name Mu11KsubB.
Platform Design Info for The Manufacturer's Name MG_U74C.
PartheenMetaData http://swegene.onk.lu.se Annotation Data (PartheenMetaData) assembled using data from public repositories.
Platform Design Info for The Manufacturer's Name Xenopus_laevis.
Platform Design Info for Affymetrix PorGene-1_1-st.
Platform Design Info for The Manufacturer's Name HG_U95D.
piRNAs (short for PIWI-interacting RNAs) and their PIWI protein partners play a key role in fertility and maintaining genome integrity by restricting mobile genetic elements (transposons) in germ cells. piRNAs originate from genomic regions known as piRNA clusters. The piRNA Cluster Builder (PICB) is a versatile toolkit designed to identify genomic regions with a high density of piRNAs. It constructs piRNA clusters through a stepwise integration of unique and multimapping piRNAs and offers wide-ranging parameter settings, supported by an optimization function that allows users to test different parameter combinations to tailor the analysis to their specific piRNA system. The output includes extensive metadata columns, enabling researchers to rank clusters and extract cluster characteristics.
Platform Design Info for The Manufacturer's Name Yeast_2.
Platform Design Info for The Manufacturer's Name S_aureus.
Platform Design Info for NimbleGen hg18_60mer_expr.
Platform Design Info for Affymetrix RaGene-2_0-st.
High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.
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 Affymetrix FinGene-1_0-st.
Platform Design Info for The Manufacturer's Name Ecoli.
Platform Design Info for Affymetrix FelGene-1_0-st.
Platform Design Info for Affymetrix miRNA-3_0.