rnakinet 0.1.22

Package for predicting 5EU in nanopore reads and predicting RNA halflives

RNAkinet

RNAkinet is a project dedicated to detecting 5eu-modified reads directly from the raw nanopore sequencing signal. Furthermore, it offers tools to calculate transcript halflives.

Usage

Installation

pip install rnakinet

Predict 5EU in your fast5 files

rnakinet-inference --path <path_to_folder_containing_fast5s> --output <predictions_name.csv>

This creates a csv file with columns read_id - the read id, 5eu_mod_score - the raw prediction score from 0 to 1, 5eu_modified_prediction - Boolean column, True if the read is predicted to be modified by 5EU, False otherwise

Nvidia GPU is required to run this command

Example

rnakinet-inference --path data/experiment/fast5_folder --output preds.csv

Calculate transcript halflives

rnakinet-predict-halflives --transcriptome-bam <path_to_transcriptome_alignment.bam> --predictions <predictions_name.csv> --tl <experiment_tl> --output <halflives_name.csv>

The --tl parameter is the duration for which the cells were exposed to 5EU in hours

The --predictions parameter is the output file of the 5EU prediction step described above

This creates a csv file with columns transcript - the transcript identifier from your BAM file, reads - the amount of reads available for the given transcript, percentage_modified - the percentage of reads of the given transcript that were predicted to contain 5EU, pred_t5 - the predicted halflife of the given transcript

Example

rnakinet-predict-halflives --transcriptome-bam alignments/experiment/transcriptome_alignment.bam --predictions preds.csv --tl 2.0 --output halflives.csv

Note that the calculated halflives pred_t5 are the most reliable for transcripts with at least 200 reads available