# Output¶

Pydamage generates both a tabular and a visual output.

The tabular outputs are comma-separated file (`.csv`

) with the following columns, for each analysed reference:

`pydamage_results.csv`

¶

`reference`

: name of the reference genome/contig`predicted_accuracy`

: Predicted accuracy of Pydamage prediction, from the GLM modelling`null_model_p0`

: parameter`p0`

of the null model`null_model_p0_stdev`

: standard error of the null model paramater`p0`

`damage_model_p`

: parameter`p`

of the damage model`damage_model_p_stdev`

: standard error of the parameter`p`

of the damage model`damage_model_pmin`

: paramater`p_min`

of the damage model.*This is the modelled damage baseline*`damage_model_pmin_stdev`

: standard error of the paramater`p_min`

of the damage model`damage_model_pmax`

: paramater`p_max`

of the damage model.*This is the modelled amount of damage on the 5’ end.*`damage_model_pmax_stdev`

: standard error of the paramater`p_max`

of the damage model`pvalue`

: p-value calculated from the likelihood-ratio test-statistic using a chi-squared distribution`qvalue`

: p-value corrected for multiple testing using Benjamini-Hochberg procedure.*Only computed when multiple references are used*`RMSE`

: residual mean standard error of the model fit of the damage model`nb_reads_aligned`

: number of aligned reads`coverage`

: average coverage along the reference genome`CtoT-N`

: Proportion of CtoT substitutions observed at position`N`

from 5’ end`GtoA-N`

: Proportion of GtoA substitutions observed at position`N`

from 5’

`pydamage_filtered_results.csv`

¶

Same file as above, but with contigs filtered with `qvalue <= 0.05`

and `predicted_accuracy >= threshold`

with a user defined filtering threshold (default = 0.5), or determined with the kneedle method.

## Plots¶

The visual output are PNG files, one per reference contig. They show the frequency of observed C to T, and G to A transition at the 5’ end of the sequencing data and overlay it with the fitted models for both the null and the damage model, including 95% confidence intervals. Furthermore, it provides a “residuals versus fitted” plot to help evaluate the fit of the pydamage damage model. Finally, the plot contains informtion on the average coverage along the reference and the p-value calculated from the likelihood-ratio test-statistic using a chi-squared distribution.

The visual output is only produced when using the

`--plot`

flag

## Example¶

**Tabular ouput****Visual output**