Question: Analysis past the differentially expressed genes: RNAseq
2
gravatar for biogirl
3.9 years ago by
biogirl170
European Union
biogirl170 wrote:

Hi all,

I have a very open-ended question, as I just wanted to gauge what people's thoughts were rather than get a concrete answer.  There are a lot of papers coming through now where RNAseq has been used and differentially expressed (DE) genes have been identified, and perhaps some gene ontology analysis has been done.  But is there anything else that can be done, anything a bit different, or dare I saw 'cooler'?  

I realise that one can infer gene regulatory networks from time series data, but is there anything else that can be done for single time point (which is quite common) data?

Thanks!

ADD COMMENTlink modified 3.9 years ago by Nicolas Rosewick8.0k • written 3.9 years ago by biogirl170
2
gravatar for cpad0112
3.9 years ago by
cpad011211k
India
cpad011211k wrote:
  1. GO analysis (mentioned above) (For all the 3 components)
  2. Pathway analysis \
  3. PPI /Signaling network analysis (IPA is best for this, in my opinion). Sting is also good
  4. Cluster analysis
  5. Immediate promoter analysis collect immediate promoter sequences (1000 bp upstream of TSS) for significant genes and see if they share same transfactors.
  6. Chromosomal location analysis Map the DE genes on their chromosomal locations and see if they cluster (For eg. MHC genes cluster in humans, mice)
  7. Protein motif analysis See if they share any common protein motifs and motif binding factors
  8. miRNA analysis See if they share similar miRNA regulation. Confirm it either by public db or in-house experiments 9) Call variants from RNA seq and see if they match with WES else where. If there are any discrepancies (barring experimental and technical errors). Look for RNA editing events. Not sure if this works. Look for NMD transcripts.
  9. Look at the missense/nonsense/truncated transcripts and model them (homology or abinitio). See if they change any of physical, chemical and functional properties of the protein
  10. Look at the somatic and germ line variants from RNAseq and classify them based on disease,drug target databases. Check which is most represented.drug/disease and compare with your experimental observations.
  11. Look for transcript switching

There are plenty of applications for transcriptomics data (RNAseq, miRNAseq, Exon arrays). I am sure scientists here, will come up with 1000 more analyses.

ADD COMMENTlink modified 3.9 years ago • written 3.9 years ago by cpad011211k

Brilliant suggestions, a very thorough list of analyses, thank you

ADD REPLYlink written 3.9 years ago by biogirl170
2
gravatar for Nicolas Rosewick
3.9 years ago by
Belgium, Brussels
Nicolas Rosewick8.0k wrote:

- fusion gene

- alternative splicing

- lncRNA/lincrna/circRNA detection 

- virus detection

ADD COMMENTlink written 3.9 years ago by Nicolas Rosewick8.0k

Thanks for these suggestions - IncRNA is something I really hadn't considered, and definitely falls in the 'cooler' category!

ADD REPLYlink modified 3.9 years ago • written 3.9 years ago by biogirl170
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