Clarification on high Nfail counts in modkit pileup output despite valid coverage

Start with the modkit pileup entry point and the definitions of Nfail, valid_coverage, count_modified, and count_canonical in the documentation. Reproduce the reported command with the stated filters, then document which filters produce Nfail and how min-mod-prob affects interpretation; done means the questions have a clear, supported explanation.

Hi modkit team,

I am analyzing CpG methylation using modkit pileup on Oxford Nanopore data
(mapped BAMs, reference-guided).

Across multiple samples, I observe that the majority of reads contributing to
coverage end up in the Nfail column, even when valid_coverage ≥ 3.

Some details:

• Genome: Plasmodium falciparum 3D7

• Command used (example):

  modkit pileup
  --reference PlasmoDB-64_Pfalciparum3D7_Genome.fasta
  --modified-bases C
  --combine-mods
  input.bam output.bed

• In the resulting bedMethyl files:

  • valid_coverage is often high (≥3 for many CpGs)
  • count_modified and count_canonical are low
  • most reads appear to be classified as Nfail
  • fraction of methylated CpGs among covered sites is very low

This behavior is consistent across samples and across different coverage cutoffs.

My questions:

1. Is a high Nfail count expected behavior in cases of low-confidence or low-level CpG methylation?
2. Which filters most commonly cause reads to be classified as Nfail?
   (e.g. modification probability threshold, basecall quality, alignment flags, context mismatch)
3. Is there a recommended way to summarize or interpret datasets where
   valid coverage exists but most reads fail confidence filters?
4. Would adjusting parameters like min-mod-prob be appropriate to explore this further?

The behavior seems biologically plausible for this organism, but I would like
to confirm that my interpretation of Nfail is correct.

Thanks for the great tool, and for any clarification!