Originally posted by aaronrjex
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Hello folks,
I am trying to understand the genome size estimation method, and here is the part that is not clear to me.
BGI is estimating coverage depth by peak of frequency histogram, but in highly repetitive genomes the peak shifts down. E.g. check the second chart here, where we generated simulated reads from sea urchin genome at 50X coverage, but the histogram peak came at ~40 due to repeats.
If that is true, how does BGI estimate genome size so precisely? In later section of bat paper, they claimed that the assembly size is close to the 'estimated size', which is puzzling given the impreciseness in genome size estimation.
Maybe I am missing something. Please help !!
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Edit. I am rerunning the above simulation to make sure everything is done correctly. Results will be reported here.Last edited by samanta; 04-10-2013, 04:59 PM.
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Lizhenyu from BGI explained where I made the error in thinking. When a read has 100 nucleotides and is split into 21-mers, the read will produce only 80 k-mers, not 100. Here is his full response, which agrees with aaronrjex's post above.
"Hi,
I think you mixed the concepts of base coverage depth and kmer coverage depth.
When you refered 50X genome coverage, it meant base coverage which is obtained by
total_base_num/genome_size=(read_num*read_length)/genome_size.
Similarly, the kmer coverage depth, the peak value in kmer frequency curve, is calculated by
total_kmer_num/genome_size=read_num*(read_length-kmer_size+1)/genome_size.
So the relationship between base coverage depth and kmer coverage depth is:
kmer_coverage_depth = base_coverage_depth*(read_length-kmer_size+1)/read_length.
In your case, kmer_coverage_depth = 50 * (100 - 21 + 1)/100 = 40, which is exactly
the peak value in you plot.
best,"
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