Hi Ivan,
Just look at the output of the source()
> source("http://bioconductor.org/biocLite.R";)
BioC_mirror = http://bioconductor.org
Change using chooseBioCmirror().
Cheers
On Fri, Apr 15, 2011 at 5:41 PM, Ivan Gregoretti <ivang...@gmail.com> wrote:
> Great.
>
> Would you mind showing how you can update the packages using any of
> those mirrors?
>
> Thank you,
>
> Ivan
>
>
>
> On Fri, Apr 15, 2011 at 11:34 AM, Dan Tenenbaum <dtene...@fhcrc.org>
> wrote:
> > On Fri, Apr 15, 2011 at 8:10 AM, Ivan Gregoretti <ivang...@gmail.com>
> wrote:
> >> Hello Dan,
> >>
> >> Updating Bioconductor devel packages as instructed in
> >>
> >> http://bioconductor.org/install/
> >>
> >> usually leads to stalled downloads when it retrieves large packages.
> >> The BSgenome packages are notorious for this.
> >>
> >> Can you or anybody recommend a solution that does not involve manually
> >> downloading the tar balls one by one? (Perhaps the is a mirror for
> >> devel.)
> >
> > Hi Ivan,
> >
> > There are mirrors for release and devel here:
> >
> > http://bioconductor.org/about/mirrors/
> >
> > Dan
> >
> >
> >>
> >> Thank you,
> >>
> >> Ivan
> >>
> >>
> >>
> >> On Thu, Apr 14, 2011 at 5:33 PM, Dan Tenenbaum <dtene...@fhcrc.org>
> wrote:
> >>> Bioconductors:
> >>>
> >>> We are pleased to announce Bioconductor 2.8, consisting of 466
> >>> software packages and more than 500 up-to-date annotation packages.
> >>> There are 48 new software packages, and many updates and improvements
> >>> to existing packages. Two software packages that were in the previous
> >>> version have been removed. Bioconductor 2.8 is compatible with
> >>> R 2.13.0, and is supported on Linux, 32- and 64-bit Windows, and Mac
> >>> OS. Visit
> >>>
> >>> http://bioconductor.org
> >>>
> >>> for details and downloads.
> >>>
> >>> Contents
> >>> ========
> >>>
> >>> * Getting Started with Bioconductor 2.8
> >>> * New Software Packages
> >>> * Using Bioconductor in the cloud
> >>>
> >>> Getting Started with Bioconductor 2.8
> >>> =====================================
> >>>
> >>> To install Bioconductor 2.8:
> >>>
> >>> 1. Install R 2.13.0. Bioconductor 2.8 has been designed expressly for
> >>> this version of R.
> >>>
> >>> 2. Follow the instructions here:
> >>>
> >>> http://bioconductor.org/install/
> >>>
> >>> Please visit http://bioconductor.org for details and downloads.
> >>>
> >>> New Software Packages
> >>> =====================
> >>>
> >>> There are 48 new packages in this release of Bioconductor.
> >>>
> >>> a4
> >>>
> >>> Automated Affymetrix Array Analysis Umbrella Package
> >>>
> >>> a4Base
> >>>
> >>> Automated Affymetrix Array Analysis Base Package
> >>>
> >>> a4Classif
> >>>
> >>> Automated Affymetrix Array Analysis Classification Package
> >>>
> >>> a4Core
> >>>
> >>> Automated Affymetrix Array Analysis Core Package
> >>>
> >>> a4Preproc
> >>>
> >>> Automated Affymetrix Array Analysis Preprocessing Package
> >>>
> >>> a4Reporting
> >>>
> >>> Automated Affymetrix Array Analysis Reporting Package
> >>>
> >>> AnnotationFuncs
> >>>
> >>> Annotation translation functions
> >>>
> >>> anota
> >>>
> >>> ANalysis Of Translational Activity
> >>>
> >>> chopsticks
> >>>
> >>> The snp.matrix and X.snp.matrix classes
> >>>
> >>> Clonality
> >>>
> >>> Clonality testing
> >>>
> >>> clst
> >>>
> >>> Classification by local similarity threshold
> >>>
> >>> clstutils
> >>>
> >>> Tools for performing taxonomic assignment
> >>>
> >>> clusterProfiler
> >>>
> >>> statistical analysis and visulization of
> >>> functional profiles for genes and gene clusters
> >>>
> >>> cn.farms
> >>>
> >>> Factor Analysis for copy number estimation
> >>>
> >>> ENVISIONQuery
> >>>
> >>> Retrieval from the ENVISION bioinformatics data portal into R
> >>>
> >>> ExiMiR
> >>>
> >>> R functions for the normalization of Exiqon miRNA array data
> >>>
> >>> flowPhyto
> >>>
> >>> Methods for Continuous Flow Cytometry
> >>>
> >>> flowPlots
> >>>
> >>> analysis plots and data class for gated flow cytometry data
> >>>
> >>> gaia
> >>>
> >>> An R package for genomic analysis of significant
> >>> chromosomal aberrations
> >>>
> >>> genefu
> >>>
> >>> Relevant Functions for Gene Expression Analysis,
> >>> Especially in Breast Cancer
> >>>
> >>> genoset
> >>>
> >>> Provides classes similar to ExpressionSet for copy number analysis
> >>>
> >>> GSVA
> >>>
> >>> Gene Set Variation Analysis
> >>>
> >>> ibh
> >>>
> >>> Interaction Based Homogeneity for Evaluating Gene Lists
> >>>
> >>> inveRsion
> >>>
> >>> Inversions in genotype data
> >>>
> >>> IPPD
> >>>
> >>> Isotopic peak pattern deconvolution for Protein Mass
> >>> Spectrometry by template matching
> >>>
> >>> joda
> >>>
> >>> JODA algorithm for quantifying gene deregulation using knowledge
> >>>
> >>> lol
> >>>
> >>> Lots Of Lasso
> >>>
> >>> mcaGUI
> >>>
> >>> Microbial Community Analysis GUI
> >>>
> >>> mgsa
> >>>
> >>> Model-based gene set analysis
> >>>
> >>> MLP
> >>>
> >>> Mean Log P Analysis
> >>>
> >>> mosaics
> >>>
> >>> MOdel-based one and two Sample Analysis and Inference for ChIP-Seq
> >>>
> >>> MSnbase
> >>>
> >>> Base Functions and Classes for MS-based Proteomics
> >>>
> >>> NCIgraph
> >>>
> >>> Pathways from the NCI Pathways Database
> >>>
> >>> phenoDist
> >>>
> >>> Phenotypic distance measures
> >>>
> >>> phenoTest
> >>>
> >>> Tools to test correlation between gene expression and phenotype
> >>>
> >>> procoil
> >>>
> >>> Prediction of Oligomerization of Coiled Coil Proteins
> >>>
> >>> pvac
> >>>
> >>> PCA-based gene filtering for Affymetrix arrays
> >>>
> >>> qrqc
> >>>
> >>> Quick Read Quality Control
> >>>
> >>> RNAinteract
> >>>
> >>> Estimate Pairwise Interactions from multidimensional features
> >>>
> >>> Rsubread
> >>>
> >>> a super fast, sensitive and accurate read aligner for mapping
> >>> next-generation sequencing reads
> >>>
> >>> seqbias
> >>>
> >>> Estimation of per-position bias in high-throughput sequencing data
> >>>
> >>> snm
> >>>
> >>> Supervised Normalization of Microarrays
> >>>
> >>> snpStats
> >>>
> >>> SnpMatrix and XSnpMatrix classes and methods
> >>>
> >>> survcomp
> >>>
> >>> Performance Assessment and Comparison for Survival Analysis
> >>>
> >>> TDARACNE
> >>>
> >>> Network reverse engineering from time course data
> >>>
> >>> TEQC
> >>>
> >>> Quality control for target capture experiments
> >>>
> >>> TurboNorm
> >>>
> >>> A fast scatterplot smoother suitable for microarray normalization
> >>>
> >>> Vega
> >>>
> >>> An R package for copy number data segmentation
> >>>
> >>>
> >>> Using Bioconductor in the cloud
> >>> ===============================
> >>>
> >>> This release features the Bioconductor Amazon Machine
> >>> Image (AMI), which allows easy access to R and Bioconductor
> >>> within the Elastic Compute Cloud (EC2). It's easy to run
> >>> parallelizable tasks on MPI clusters, run R from within
> >>> your web browser using RStudio Server, and more. No
> >>> installation required. Information available at:
> >>>
> >>> http://bioconductor.org/help/bioconductor-cloud-ami/
> >>>
> >>> _______________________________________________
> >>> Bioc-sig-sequencing mailing list
> >>> Bioc-sig-sequencing@r-project.org
> >>> https://stat.ethz.ch/mailman/listinfo/bioc-sig-sequencing
> >>>
> >>
> >
>
> _______________________________________________
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>
--
João Moura
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