The bigmemory package in combination with doMC provides at least a partial solution for sharing a large data set across multiple cores in R. With this solution you can work on the same matrix using several threads. It is also a very scalable solution. ...
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Retrieving transcriptome sequences for RNASeq analysis
One approach for analyzing RNASeq data from an organism with a well-annotated genome, is to align the reads to mRNA (cDNA) sequences instead of the genome. To do that you need to extract the transcript sequences from a database. This is how to extract ensembl transcript sequences from UCSC from within R:
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library(GenomicFeatures)
library(BSgenome.Hsapiens.UCSC.hg18)
tr
tr_seq
write.XStringSet(tr_seq, file="hg18.ensgene.transcripts.fasta", 'fasta', width=80, append=F)
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Next steps...
Retrieving transcriptome sequences for RNASeq analysis
One approach for analyzing RNASeq data from an organism with a well-annotated genome, is to align the reads to mRNA (cDNA) sequences instead of the genome. To do that you need to extract the transcript sequences from a database. This is how to extract ensembl transcript sequences from UCSC from within R:
_________________________________________________
library(GenomicFeatures)
library(BSgenome.Hsapiens.UCSC.hg18)
tr
tr_seq
write.XStringSet(tr_seq, file="hg18.ensgene.transcripts.fasta", 'fasta', width=80, append=F)
_________________________________________________
Next steps...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
