Comments (2)
Thanks for noting this! The way CheckM2 was trained was using a ceiling of ~35-40% contamination, meaning that contamination numbers beyond that do not scale meaningfully with real biology. A reported contamination score of more than 35% is CheckM2 stating the genome is of very bad quality and is unusable, regardless of what it is or how complete it is.
If you throw enough genes at it however, completeness scores do seem to creep up. We will look into whether explicitly training CheckM2 to disregard eukaryotic genomes as 0% complete may help.
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What about using the eukaryota_odb10 marker set to make a small false positive model? That is, if it has these then it's likely a eukaryote and flag it as such? I admit I haven't read the methods for your paper yet so this might not be applicable at all.
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Related Issues (20)
- TypeError: metaclass conflict HOT 7
- coding density
- Can Checkm2 be fine-tuned for euMAGs? HOT 1
- Got different results of the same genome from the different runs of checkm2 HOT 3
- testrun error: AttributeError: module '__main__' has no attribute '__spec__'
- Testrun ERROR: No DIAMOND annotation was generated. HOT 2
- test run yields different results HOT 1
- Database download failed HOT 3
- Naming Output TSV File
- Average_Gene_Length HOT 1
- How to remove the contamination sequence from MAGs HOT 2
- Cannot install ChekM2 HOT 1
- Question about Checkm2 Annotation HOT 1
- Checkm2 flags certain species and genera as contaminated on a consistent basis.
- the version checkm2 v1.0.2 was not available on conda HOT 1
- Error in DIAMOND execution HOT 2
- [Feature Request] Use #!/usr/bin/env python instead of hardcoding python path
- AttributeError: 'Predictor' object has no attribute '__set_up_prodigal_thread' HOT 2
- Random errors on clusters HOT 3
- Database update? HOT 2
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