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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("synapter")
In most cases, you don't need to download the package archive at all.
synapter
Label-free data analysis pipeline for optimal identification and quantitation
Bioconductor version: 2.14
The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy.
Author: Laurent Gatto, Nick J. Bond and Pavel V. Shliaha with contributions from Sebastian Gibb.
Maintainer: Laurent Gatto <lg390 at cam.ac.uk>
Citation (from within R,
enter citation("synapter")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("synapter")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("synapter")
| R Script | Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GUI, MassSpectrometry, Proteomics, Software |
| Version | 1.6.0 |
| In Bioconductor since | BioC 2.11 (R-2.15) |
| License | GPL-2 |
| Depends | R (>= 2.15), methods, MSnbase |
| Imports | hwriter, tcltk, tcltk2, RColorBrewer, lattice, qvalue, multtest, utils, Biobase, knitr, Biostrings, cleaver, BiocParallel |
| Suggests | synapterdata, xtable |
| System Requirements | |
| URL | http://lgatto.github.com/synapter/ |
| Depends On Me | synapterdata |
| Imports Me | |
| Suggests Me | pRoloc, RforProteomics |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | synapter_1.6.0.tar.gz |
| Windows Binary | synapter_1.6.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | synapter_1.6.0.tgz |
| Mac OS X 10.9 (Mavericks) | synapter_1.6.0.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
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