LaBranchoR uses a LSTM network built with keras to predict the position of RNA splicing branchpoints relative to a three prime splice site. Precisely evaluating LaBranchoR was challenging due to pervasive noise in the experimental data, but as we show in our paper, we estimate that LaBranchoR correcty predicts a branchpoint for over 90% of 3'ss.

Paggi J.M., Bejerano, G. A sequence-based, deep learning model accurately predicts RNA splicing branchpoints. bioRxiv 185868 (2017). DOI:10.1101/185868

See our website linked above to download branchpoint predictions for introns in gencode v19 (hg19) or view LaBranchoR predicted branchpoints in the UCSC genome browser.

If having to run the model yourself would stop you from using LaBranchoR, please open an issue requesting the desired predictions or contact the authors via email.

All of the code and model weights needed to run LaBranchoR are available in the 'labranchor' directory. Running LaBranchoR requires keras and numpy to be installed.

The script labranchor.py makes predictions for a fasta file of sequences upstream of 3'ss. It can be invoked with

python labrachor.py weights 'top-bed'/'top'/'all' fasta_file output

weights: The path to the h5 weights file (labranchor/2layer.h5)

'top-bed'/'top'/'all': top-bed: produces a bed file of predicted branchpoints. Assumes fasta names are chrom:3'ss_coord:strand (ex. chr1:1000:+) top: reports the shift of the top scoring branchpoint from the associated 3'ssfor each fasta entry all: reports a comma seperated list of branchpoint probabilities corresponding to positions -70 to -1 from each 3'ss

fasta_file: Path to a fasta file of sequences upstream of 3'ss. Input sequences are required to be 70 base pairs and should not contain characters other than 'A', 'C', 'G', 'T', or 'N'. Any Ns will be considered A's during prediction.

output: Path to the output file. See the above options for formatting.

The script create_fasta.py can be used to create fasta files suitable for branchpoint prediction for all introns in given gtf file.

It can be invoked with:

python create_fasta.py genome gtf output

genome: A path to a genome fasta file consistent with the gtf file.

gtf: The path to the gtf file you wish to predict branchpoints in.

output: The path to the output fasta file.

Model training: notebooks/train_model.ipynb

Model performance: notebooks/performance_*

Cases where LaBranchoR disagrees with experimental data: notebooks/disagreement_*

Genome-wide properties and overlap with pathogenic variants: notebooks/landscape_*

Properties of C and no -2 U branchpoints: notebooks/landscape_C_and_noT.ipynb

Enrichments of ExAC variants: notebooks/ExAC_variant_enrichments.ipynb

Generation of ISM supplmentary data: notebooks/supp_data.ipynb

Exploration of nucleotide importances: notebooks/importance.ipynb

Analysis of secondary structure near branchpoints: notebooks/secondary_*