RNA-Seq: A survey of statistical software for analysing RNA-seq data.

[AronaldJ]

Thank for schmima

[schmima]

searched for the title:

https://www.math.hkbu.edu.hk/~tongt/papers/HumanGenomics2.pdf

[lry198010]

Oh, I will wait for it is in public accession!

[greigite]

The paper isn't posted yet- just listed in "forthcoming content" on the journal website.

[AronaldJ]

Who can get the paper? sent it to me.
Thank you

[bioinfosm]

I was also looking for a copy, but no luck yet..

[lcollado]

I haven't been able to download it from it's main site: http://henrystewart.metapress.com/ap...lts,1:121141,1 but maybe your institution has a subscription.

Greetings,
Leonardo

[paul z]

Has anybody found a copy of this article yet...?

[lcollado]

Haha ok Anyhow, I did end up download several of the papers

[ECO]

Haha! It was in fact that all of his (its?) posts were approved instead of the usual weeding out that I do. The search terms I use pick up a lot of extraneous stuff that isn't relevant.

[lcollado]

wow! Newsbot had a yummmy Turkey it seems.. so many posts :P

no kidding! so many posts for 1 day

[bioinfosm]

wow! Newsbot had a yummmy Turkey it seems.. so many posts :P

[Newsbot!]

Syndicated from PubMed RSS Feeds

A survey of statistical software for analysing RNA-seq data.

Hum Genomics. 2010 Oct 1;5(1):56-60

Authors: Gao D, Kim J, Kim H, Phang TL, Selby H, Tan AC, Tong T

High-throughput RNA sequencing is rapidly emerging as a favourite method for gene expression studies. We review three software packages - edgeR, DEGseq and baySeq - from Bioconductor http://bioconductor.org for analysing RNA-sequencing data. We focus on three aspects: normalisation, statistical models and the testing employed on these methods. We also discuss the advantages and limitations of these software packages.

PMID: 21106489 [PubMed - in process]



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