Search Results for "bio sample"

dataMAPPs allows routine and efficient processing of data from immunogenicity studies applying the MAPPs peptidomics technology to detect potential MHCI- or MHC-II epitopes as presented by dendritic cells (DC). It features quality control of the raw data, across-sample/across-donor normalization and visualization of results in a heatmap style (heatMAPPs). dataMAPPs' core is a generic R library that can be tailored to specific projects via dedicated control scripts which also allow...

BSpipe is a comprehensive pipeline from sequence quality control and mapping to functional analysis of differentially methylated regions: (1) sequencing quality assessment, (2) sequence cleaning, (3) sequence read mapping, (4) methylation quantification, (4) sample comparisons based on methylation profile, (5) identification of DMRs (differentially methylated regions), (6) annotation of DMRs, (7) functional analysis of differentially methylated genes, (8) generation of input files...

This package provides high-level interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax...

R-gregmisc is a set of extension package for the open-source statistical environment/language "R''. It includes functions for plotting (gplots), data manupulation (gdata), modeling (gmodels), and programming tools (gtools), creation and management of sub-processes (fork), determing sample sizes for -omics experiments (ssize), and a variety of other packages.

..., BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems).

A population-based method for DNA copy number analysis: recurrent copy number aberration indentification in multiple samples (with no need of single-sample calling). Developed for a quick analysis of high resolution and large population data.