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 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 recommended to run this command. If you want to run inference on a CPU-only machine, use the --use-cpu option. This will substantially increase runtime.

Example

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

Selecting flow-cell chemistry

RNAkinet has been extensively tested on flow-cells with the R9 chemistry. Experimental support is offered for R10. You can specify the flow-cell chemistry with the --kit option.

rnakinet-inference --path data/experiment/fast5_folder --kit r10 --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 high read count. The following plots show correlation of halflives computed from RNAkinet predictions with experimentaly measured halflives [1] as we increase read count requirement. We recommend users to acknowledge this and put more confidence in halflife predictions for transcripts with high read count, and less confidence for transcripts with low read count.

[1] Eisen,T.J., Eichhorn,S.W., Subtelny,A.O., Lin,K.S., McGeary,S.E., Gupta,S. and Bartel,D.P. (2020) The Dynamics of Cytoplasmic mRNA Metabolism. Mol. Cell, 77, 786-799.e10.

Cite

Vlastimil Martinek, Jessica Martin, Cedric Belair, Matthew J Payea, Sulochan Malla, Panagiotis Alexiou, Manolis Maragkakis, Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics, NAR Genomics and Bioinformatics, Volume 6, Issue 3, September 2024, lqae116, https://doi.org/10.1093/nargab/lqae116