how to analyse gene expression using RNA-seq data

Question

[ashish]

I want know how do we calculate gene expression using RNA-seq data available on NCBI. For example I want analyze gene expression of a family of transcription factors, I've the sequences of all these transcription factors and then i download the RNA-seq data from NCBI's SRA database. What do i do next in order to get the FPKM value for creating a heatmap. I am very new to Bioinformatics and learning on my own and also do not know any language except some Biopython and R. Thank you

[Medhat]

RNA-seq workflow: gene-level exploratory analysis and differential expression

[Daniel]

Check out RNA-seqlopedia, lots of information on understanding the process, not just the technical bioinformatics http://rnaseq.uoregon.edu

Answer 1

[Vivek]

You could visit a few RNA seq tutorials. They are by far the easiest to find in bioinformatics. Alternatively you can read through the manuals for the cufflinks suite.

http://cole-trapnell-lab.github.io/cufflinks/manual/

The basic workflow is Reads -> Mapping to genome (STAR/Tophat etc are good splice aware aligners for this job) -> Perform a guided/unguided transcriptome assembly (Cufflinks) -> Create a merged assembly from your samples (Cuffmerge) -> Quantify against a known reference (Cuffcompare)

If you found the sequences from SRA may be you could also find the associated BAM files and reduce your analysis time.

[ashish]

Thanks Vivek. That is exactly what I was looking for. Can you also explain what is guided/unguided transcriptome assembly?

[Vivek]

In guided transcriptome assembly you give the assembler a GTF file of known transcripts/isoforms for the organism you are interested in to aid as a guiding reference. In unguided assembly, you don't which can lead the assembler into assembling more de novo isoforms, if that is your objective.

[ashish]

Alright I got it. That means I'll be doing a guided assembly.

Answer 2

[Nicolas Rosewick]

Take a look at DESeq2. Here's a very nice tutorial from their authors : https://f1000research.com/articles/4-1070/v2