illuminaio
Tools for parsing Illumina's microarray output files, including IDAT.
README
illuminaio: Parsing Illumina Microarray Output Files Installation R package illuminaio is available on Bioconductor and can be installed in R as: Pre-release version To install the pre-release version that is available in Git branch develop on GitHub, use: This will install the package from source. Because of this and because this package also compiles native code, Windows users need to have Rtools installed and macOS users need to have Xcode installed. Contributing To contribute to this…
- Repository
- github.com/henrikbengtsson/illuminaio
Source attribution
- GitHub — github.com/henrikbengtsson/illuminaio
- Bioconductor — illuminaio
Related resources
The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package.
Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure.
The package imports the result of tRNAscan-SE as a GRanges object.
Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.
A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages.
Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats.