SpliceNouveau is a python algorithm to help users generate vectors containing introns and splice sites. At its core, it is an 'in silico directed evolution' algorithm: based on a set of user-defined sequences (which may be amino acid and/or nucleotide sequences) and constraints (splice-site strengths and types), it attempts to identify synonymous and non-coding mutations which result in the SpliceAI splicing predictions matching those requested by the user.
SpliceNouveau can generate several different types of splicing events:
- Cassette exons
- Alternative 3' splice sites
- Alternative 5' splice sites
- Intron retention
In each case, it attempts to alter the sequence to set the SpliceAI predictions at the defined splice sites to the user-requested level, while minimizing the presence of off-target splice sites which could cause mis-splicing.
In theory, SpliceNouveau can be used to create constitutively-spliced vectors. However, if the user specifies an enrichment of certain motifs that bind a given splicing regulator (or are known to influence splicing in, e.g., a tissue-specific manner) near a given splice site, then SpliceNouveau can help generate alternative spliced vectors. We have used this algorithm extensively to generate vectors which undergo alternative splicing in response to loss of TDP-43 nuclear function.
SpliceNouveau is a Python algorithm designed to help users generate vectors containing introns and splice sites. It is an 'in silico directed evolution' algorithm that identifies synonymous and non-coding mutations to match the SpliceAI splicing predictions requested by the user.
- Python >=3.7
- Required Python packages:
numpy,pandas,gzip,random,argparse,os,sys,keras,tensorflow,spliceai
- Clone the repository or download the source code.
- Install the required Python packages using pip:
pip install numpy pandas gzip
- Install SpliceAI by following the instructions on the SpliceAI github page. Note that although a CUDA-enabled GPU significantly increases performance, it is feasible to run SpliceAI/SpliceNouveau using a CPU.
The SpliceNouveau tool is executed from the command line with various arguments. Here's an example usage:
python3 SpliceNouveau.py --initial_cds ATGGCGAGAACAATGGTTGCTATGGTGTCCAAAGGTGAGGCAGTCATAAAG... \
--initial_intron1 GTAAGNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNTGTGTGTGTG... \
--ce_start 43 \
--ce_end 159 \
--ce_mut_chance 1 \
--five_utr CGGCCGCTTCTTGGTGCCAGCTTATCAT... \
--three_utr TGATAAACAAATGGTAAGGAAGGGCACAT... \
--ignore_end 470 \
--output mscar/aars1_inspired_closer_aim_0p5.csv \
--target_cryptic_donor 0.5 \
--target_cryptic_acc 0.5 \
-a 5 \
--intron1_mut_chance 0.5 \
--intron2_mut_chance 0.5 \
-n 2000 \
--cds_mut_end_trim 569
This example command specifies the initial CDS, intron sequences, cryptic exon start and end positions, UTR sequences, splice site target scores, and other parameters for the algorithm.
For a complete list of available arguments and their descriptions, run:
python3 SpliceNouveau.py -h
Note that the 'fitness scores' given to each sequence (for historic reasons) are 2 - sum(deviations from desired splicing). In the paper we ignore this 2 value offset to avoid confusion. Thus, a high-performing sequence is expected to have a score of close to 2 (not zero).
The SpliceNouveau tool generates two output files:
<output_filename>.csv: Contains the attempt number, score, sequence, cryptic exon length, and frameshift information for each attempt.<output_filename>.predictions.csv: Contains the attempt number, position, donor probability, and acceptor probability for each position in the sequence.
If the --track_splice_scores option is used, an additional file <output_filename>.tracked_scores.csv is generated, which tracks the splice scores for each iteration.