Comments (1)
The cutoff program is not trying to be in any way smart. You can take a look how it guesses L and U respectively.
L is 1.25x of the coverage of the first local minimum (we expect that there will be a detectable coverage peak associated with the genome sequencing)
U is 99.8% of the data.
So what likely happens is that you don't have any detectable coverage peak - which means that the first local minimum is actually among really high coverage, higher than 99.8% of your data.
That's the thing. If you have a genome skim (less than 5x of coverage), I am afraid you won't be able to use smudgeplot, it's not designed for such data. You can find more about input here.
k-mers in general however can be useful. You can check what you can do with genome skims on this workshop webpage. Hope this helps.
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Related Issues (20)
- Process was automatically killed HOT 3
- none results generating with huge dump files HOT 6
- smudgeplot.py hetkmers massive memory and walltime required HOT 3
- Interpretation of (maybe) hybrid genomes - multiple species HOT 1
- ploidyplot never finishes HOT 2
- script execution never ends HOT 2
- Error in estimate_1n_coverage_1d_subsets(total_pair_cov, minor_variant_rel_cov) HOT 2
- solve speckiness problem of smudgeplot HOT 2
- unable to start device PNG HOT 1
- plot err HOT 3
- Interpreting a possible haploid HOT 5
- Hexaploid Plant Analysis and Genome Assembly Questions HOT 2
- SPloidyPlot detecting two smudges at the same positions warning HOT 4
- Error while plotting Smudgeplot HOT 3
- script execution never ends (Extremely Large .jf Files) HOT 1
- Hexaploid plant interpretation and coverage HOT 2
- quantile filtering does not take freq into account
- PloidyPlot: Table part ./.finall.ktab.1 is missing ? HOT 1
- sploidyplot results: diploid or tetraploid HOT 4
- smudge_pairs: command not found Error HOT 1
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from smudgeplot.