Output

Pydamage generates both a tabular and a visual output.

The tabular output is a comma-separated file (.csv) with the following columns, for each analysed reference:

  • reference: name of the reference genome/contig

  • pred_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’

To select contigs/references with damage, you will most likely want to look at two columns: pred_accuracy > 0.9 and qvalue <= 0.05

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

pydamage_plot