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 HG-U133A\_2\_probe\_tab.

Package with metadata for genotyping Illumina 1M Duo arrays using the crlmm package.

Affymetrix hugene21 annotation data (chip hugene21stprobeset) assembled using data from public repositories.

Sample dataset obtained from http://www.hapmap.org.

Affymetrix huex10 annotation data (chip huex10sttranscriptcluster) assembled using data from public repositories.

Package with metadata fast genotyping Illumina 1M arrays using the crlmm package.

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 HG-Focus\_probe\_tab.

Package with metadata for genotyping Illumina Omni2.5 Quad arrays using the crlmm package.

This package provides a package containing an environment representing the Hu6800subD.CDF file.

This package provides a package containing an environment representing the HG-U219.cdf file.

Affymetrix Affymetrix HT_MG-430B Array annotation data (chip htmg430b) assembled using data from public repositories.

Sample dataset obtained from http://www.hapmap.org.

Package with metadata for genotyping Illumina Omni Express 12 arrays using the crlmm package.

This package was created by frmaTools version 1.19.3 and hgu133ahsentrezgcdf version 19.0.0.

Agilent Human 1 cDNA Microarray Kit annotation data (chip hgug4100a) assembled using data from public repositories.

Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease).

Affymetrix hugene10 annotation data (chip hugene10sttranscriptcluster) assembled using data from public repositories.

This software is meant to be used for classification of images of cell-based assays for neuronal surface autoantibody detection or similar techniques. It takes imaging files as input and creates a composite score from these, that for example can be used to classify samples as negative or positive for a certain antibody-specificity. The reason for its name is that I during its creation have thought about the individual picture as an archielago where we with different filters control the water level as well as ground characteristica, thereby finding islands of interest.

Illumina MouseWG6v1 annotation data (chip illuminaMousev1) assembled using data from public repositories.

MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide. Reproducible and consistent annotation of HLA alleles in large-scale bioinformatics workflows remains challenging, because the available reference databases and software tools often use different HLA naming schemes. The package immunotation provides tools for consistent annotation of HLA genes in typical immunoinformatics workflows such as for example the prediction of MHC-presented peptides in different human donors. Converter functions that provide mappings between different HLA naming schemes are based on the MHC restriction ontology (MRO). The package also provides automated access to HLA alleles frequencies in worldwide human reference populations stored in the Allele Frequency Net Database.

Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.

Characterization of miRNAs and isomiRs, clustering and differential expression.

Plots protein properties and visualizes position of peptide immunogens within protein sequence. Allows evaluation of immunogens based on structural and functional annotations to infer suitability for antibody-based methods aiming to detect native proteins.

It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies.