Dear scientists,
I am aware that tumor exome-seq data without a matched normal will have difficulty for distinguishing somatic and germline mutations. I aim to find "important" somatic mutations from the data. I know incorporating 1000 genome project would help a bit. However, there are still too many mutations. Is there a way to redeem this kind of suffering?
Thanks,
Woody
I would start with this software.
IntOGen-mutations identifies cancer drivers across tumor types. Nature
good luck
This is a indeed a challenge. We are trying to build our own "common normal" over here that uses one of our matched normal samples as a "base" and adding other common polymorphisms on top, but it is still messy. Finding significantly recurrently mutated genes or hotspot mutations across a large cohort after comparing with such a "common normal" may probably be the best current way to find your "important" somatic mutations.
@Hannah: Thanks!!! With no disrespect, I think that screening for hotspot mutations such as BRAF V600E or KRAS don't need sequencing. DNA arrays or simple staining can do the job quicker and cheaper. I found that it is difficult to deliver the idea to pathologists who consider matched normal as unnecessary. From their perspective, in the past, there are many BRAF V600E mutations studies purely relied on tumor samples. Why bother to have matched normal? It is the question I am looking for direct answer. (e.g. a research paper)
A new paper on using a virtual normal: Discriminating somatic and germline mutations in tumour DNA samples without matching normals
Saskia Hiltemann1, Guido Jenster, Jan Trapman, Peter van der Spek and Andrew Stubbs
Genome Res. 2015 Jul 24. pii: gr.183053.114. [Epub ahead of print]
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