Dec 26, 2014
Fri, Dec 26, 2014 at 11:13 AM
Customer: We have a generated knockout mice and would like to perform RNA-seq experiments for the knock-out and wildtype to see changes in gene expression of specific pathways. Can you suggest the types of analysis you might be able to do?
Fri, Dec 26, 2014 at 2:20 PM
AccuraScience LB: What we would recommend includes the following: (1) Differential expression analysis between wild type and the knock-out. This analysis will generate lists of genes that are differentially expressed between the three groups, (2) Pathway enrichment analysis, which will produce a list of GO terms (or pathways, e.g. signaling pathways) that are significantly enriched for the differentially expressed genes, each GO term (or pathway) annotated with a false discovery rate (FDR) level. (3) Optionally, when supplemented with some public data, e.g., protein-protein interaction data, we could perform a network analysis followed by the pathway enrichment analysis. The network analysis component will incorporate genes that are not detected directly as differentially expressed but are known to be interacting with the differentially expressed genes, therefore it has the potential to produce more meaningful pathways for examination.
Sat, Dec 27, 2014 at 12:48 AM
Customer: In addition to the data analysis you recommended, will you be able to look at spliced variants and possibly identification of novel non-coding RNA species in the knockouts?
Sat, Dec 27, 2014 at 10:44 AM
AccuraScience LB: Yes. Since you would be interested in identifying long non-coding RNAs, it would be a good idea to use an rRNA-depletion protocol rather than poly-A enrichment protocol in sample preparation. The former would preserve a higher proportion of long non-coding RNAs that do not carry poly-A tails.
We could also produce a list of transcript isoforms for each gene and compare across all samples/groups. The lists will be long and may not be easy to examine manually, though. We could check the identified transcripts against known long non-coding RNAs, and predict coding potential for novel transcripts to come up with a list of expressed long non-coding RNAs. Not many long non-coding RNAs have been functionally characterized, thus the challenges towards this direction will be to design downstream experiments to characterize what those differentially expressed long non-coding RNAs are doing.
Back to Other Selected Recent Inquiries
Note: LB stands for Lead Bioinformatician. An AccuraScience LB is a senior bioinformatics expert and leader of an AccuraScience data analysis team.
Disclaimer: This text was selected and edited based on genuine communications that took place between a customer and AccuraScience data analysis team at specified dates and times. The editing was made to protect the customer’s privacy and for brevity. The edited text may or may not have been reviewed and approved by the customer. AccuraScience is solely responsible for the accuracy of the information reflected in this text.