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Hello,
I'm currently in a class, and the professor is having us run through some NGS practice data from HIV. I tried to find an exact answer in the forums but couldn't. I feel like the professor may be wrong with what he is asking us to do.
He gave us fastq files, and told us to clean them up, align them to the genome, remove duplicates with picard tools, and then do variant calling. The problem is, the coverage is insanely high (~200,000x) so every position has many many mapping reads.
If I understand correctly, Picard tools will select one read (is it the best mapping quality read, or most highly represented read?) at each mapping location and discard the rest.
I feel like this strategy isn't right. If the program is selecting for the best mapping read, I feel like this would select for variants that match the genome reference at that location even if they are a minor variant. If the program is using the most abundant read, I feel like every minor variant would definitely be lost. Am I wrong in my thinking??
I'm currently in a class, and the professor is having us run through some NGS practice data from HIV. I tried to find an exact answer in the forums but couldn't. I feel like the professor may be wrong with what he is asking us to do.
He gave us fastq files, and told us to clean them up, align them to the genome, remove duplicates with picard tools, and then do variant calling. The problem is, the coverage is insanely high (~200,000x) so every position has many many mapping reads.
If I understand correctly, Picard tools will select one read (is it the best mapping quality read, or most highly represented read?) at each mapping location and discard the rest.
I feel like this strategy isn't right. If the program is selecting for the best mapping read, I feel like this would select for variants that match the genome reference at that location even if they are a minor variant. If the program is using the most abundant read, I feel like every minor variant would definitely be lost. Am I wrong in my thinking??