Rna Sequencing Vs Beadarray/Microarray

Question

[Dave Bridges]

We are interested in transcriptional changes in a tissue. Our first instinct is to use a beadarray to profile the two transcriptomes, but are considering using RNA sequencing technology instead. We do not have a in house bioinformatician to help analyse the data, but are competent in using R/Bioconductor and python. Are there reasonably easy to use programs to analyse RNA sequencing data, or is this pointless without an expert bioinformatician to help?

Answer 1

[Eric Fournier]

There is no dearth of RNA-Seq software. Anyone who is competent in R and python should be able to use most of them, with various degrees of effort.

I would suggest reading 'From RNA-seq reads to differential expression results' (Alicia Oshlack, 2011) for a primer on RNA-Seq analysis and 'Microarrays, deep sequencing and the true measure of the transcriptome' (John H Malone, 2011) for a microarray vs RNA-Seq comparison.

Answer 2

[seidel]

One relatively easy and straightforward path is to use tophat and cufflinks. If you can install those two freely available suites, you should be able to go from sequence reads to quantified transcripts in only a few steps. Installing tophat also involves installing bowtie and SAM tools, but it's explained pretty clearly in the tophat documentation. With those tools in place, a typical workflow requires some alignment indices (available prebuilt at tophat site, or you can make your own with the provided tools and some fasta files), and a gtf or gff file of transcripts you'd like to quantify (usu. available from ensembl). Call tophat on your fastq files to create alignment results. Then call cufflinks on your alignment results to quantify gene or transcript levels, or run cuffdiff to quantify differences between samples. So overall, it just involves running a few programs. If something breaks there can be some tweaking involved (which can be solved by an expert bioinformatician faster than a non-expert), but nothing out of reach for a highly motivated biologist.