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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("NGScopyData")
In most cases, you don't need to download the package archive at all.
NGScopyData
Subset of BAM files of human tumor and pooled normal sequencing data (Zhao et al. 2014) for the NGScopy package
Bioconductor version: 3.0
Subset of BAM files of human lung tumor and pooled normal samples by targeted panel sequencing. [Zhao et al 2014. Targeted Sequencing in Non-Small Cell Lung Cancer (NSCLC) Using the University of North Carolina (UNC) Sequencing Assay Captures Most Previously Described Genetic Aberrations in NSCLC. In preparation.] Each sample is a 10 percent random subsample drawn from the original sequencing data. The pooled normal sample has been rescaled accroding to the total number of normal samples in the "pool". Here provided is the subsampled data on chr6 (hg19).
Author: Xiaobei Zhao [aut, cre, cph]
Maintainer: Xiaobei Zhao <xiaobei at binf.ku.dk>
Citation (from within R,
enter citation("NGScopyData")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("NGScopyData")
Documentation
| Reference Manual |
Details
| biocViews | DNASeq, ExperimentData, Sequencing, TargetedResequencing |
| Version | 0.99.2 |
| License | GPL (>=2) |
| Depends | |
| Imports | |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | http://www.bioconductor.org/packages/release/data/experiment/html/NGScopyData.html |
| Depends On Me | |
| Imports Me | |
| Suggests Me | NGScopy |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | NGScopyData_0.99.2.tar.gz |
| Windows Binary | NGScopyData_0.99.2.zip |
| Mac OS X 10.6 (Snow Leopard) | NGScopyData_0.99.2.tgz |
| Mac OS X 10.9 (Mavericks) | NGScopyData_0.99.2.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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