Identifying Mutation Frequency Changes in MSAs over time?

Question

[Saran]

Hello,

I have 6 sample fasta files total over 3 time points: WEEK 3, 5 & 7. There are two samples per time-point which are Control and Positive for a Specific Gene. These samples are YU2 HIV viral transcriptomes.

In each Fasta File, there ~8000 bp reads where each read represents a YU2 transcriptome and there are ~500 reads across the 6 sample fasta files. I want to analyze how the mutation frequency changes overtime. I have created an MSA using Clustal Omega for each of my 6 Fasta samples. I have attempted to use dnadiff function from Mummer but I don't think it is giving me the appropriate output.

Is there a software that could help me to compare the mutation frequencies most accurately between the MSAs?

Thanks, Sara

[LauferVA]

sara (saran?) -

this is slightly outside my domain of expertise, so please take this as a comment, not an answer... if i understand you correctly, the problem you are describing is actually one of the simpler steps you need to complete; rather, you first need to conduct variant calling in order to distinguish sequencing artifacts from true positive variants.

what i mean is, you have 500 reads and six samples, so ~83 reads per sample. Suppose 82 of 83 of the reads have a polymorphism, but the last one doesnt. In this case, its clear that the one that doesnt is probably in error, even though it is reference at that position (because that is far more likely than the alternative that the other 82 are spurious calls). however, its not always that clear. sometimes a greater fraction of the reads might have a variant, while the rest dont, so you are uncertain which is the truer alternative; here a dedicated variant calling algorithm is the way to go.

once variant calling is complete, it is relatively simple to count the differences between transcriptomes using the variant call file. once you have that in hand, you could start by conducting a google search for "mutational velocity" to get a sense for how this is measured in different contexts. As a primer, this script measures velocity of mutations over time for cancer data.

hope this helps..