Our research group works with viral detection in human samples through PCR-based methods. We use to sequence the PCR amplicons to confirm the specific amplification of viral sequences in a Sanger-based platform (Applied Biosystems 3500 genetic analyzer). When analyzing the electropherograms generated it is common to observe degenerated bases (usually Ys and Ws) that seems to be not generated by errors in sequencing process, but to rather represent intra-host variability in the viral sequences.
This raised our interest in further investigate these candidate variations and search for possible active mutational processes, specifically we are interested in quantify the possible influence of APOBEC cytidine deaminases in generating these variations (by searching for mutations in APOBEC specific recognition sites, namely 5'-TC-3' over random candidate mutations). Is there any software, package or pipeline adapted for this analysis?
I've read about and downloaded the Minnor Variant Finder software (MVF, from Applied Bisystems), but it seems to be not suited for this question, once it was developed to identify low-frequency human variants and requires the parallel sequencing of a control sequence, which I don't know what could be in my case.
Thank you!
Hi Glauco...
The sequence specificity of APOBEC deamination and its tight association with ssDNA, may resulted in the fraction of the genome where specific mutations may escape the bulk of APOBEC mutagenesis. You may use: CRAVAT on-line software package. Hopefully it can help.
Best luck.
APOBEC-mediated hypermutation is very common in HIV, so we made a tool to help with this:
https://www.hiv.lanl.gov/content/sequence/HYPERMUT/hypermut.html
Thank you, Dr. Foley!
I think this tool should help me a lot!
All of our tools provide a sample input set for "test driving" the tool. I recommend the "View Sites Along Sequence : Cumulative Matches (try me!)" output option. I attach two examples here. The sample input is a small file. The SIVMAC_result is from a large alignment of complete genomes and illustrates that we sometimes observe a hypermutated region with most of the genome not being hypermutated.
Dear Glauco,
Hypermut, as suggested by Brian Foley, is to the best of my knowledge, quite good at estimating APOBEC-driven mutations, although it is more specific to A3G and A3F nucleotide contexts. The algorithm calculates the probability of a given seqeunce to be hypermutated by APOBECs by comparing the unbalance between mutated cytidines at APOBEC versus non-APOBEC (random) contexts, and provides a p-value related to that comparison.
Thank you so much for your help, Dr Foley and Dr Soares!
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