Hacktoberfest 2026: los issues que los mantenedores marcaron para octubre, abiertos y aptos para principiantes. Explorar issues de Hacktoberfest

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

Abierto
#577 1 comentario 0 reacciones 0 asignados Ver en GitHub

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
30/100
Tipo de issue
Documentación
Claridad
Necesita aclaración
Estado de actividad
Estancado
Stack tecnológico
rust
Área
bioinformatics, cli

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

question

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!

Lenguaje dominante
Rust
Estrellas
274
Forks
33
Sin PR fusionados en 30 d

Guía de contribución

No hay ninguna guía de contribución indexada para este repositorio

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de nanoporetech/modkit

Todos los issues de nanoporetech/modkit

Issues similares

Más issues de Rust

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.