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ketringjoni / SuPreMo

Pipeline for generating reference and perturbed sequences for input into predictive models.

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Sequence Mutator for Predictive Models (SuPreMo) is a pipeline for generating reference and perturbed sequences for input into predictive models that is scalable, flexible, and comprehensive. SuPreMo-Akita applies the tool to an existing sequence-to-profile model, Akita, and generates scores that measure disruption to genome folding.

SuPreMo: Get sequences for predictive models

  • SuPreMo incorporates variants one at a time into the human reference genome and generates reference and alternate sequences for each perturbation under each provided augmentation parameter.
  • The sequences are accompanied by the relative position of the perturbation for each sequence.
  • The perturbation is by default centered in the sequence, if possible, unless the user provides a shifting parameter.

SuPreMo-Akita: Get 3D genome folding disruption scores using the Akita model

  • SuPreMo-Akita generates disruption scores by comparing contact frequency maps predicted from the reference and alternate sequences.
  • The maps are accompanied by the relative position of the perturbation and the start coordinate that the predicted maps correspond to.
  • The perturbation is by default centered in the map, if possible, unless the user provides a shifting parameter.
  • Optionally, the predicted maps or disruption score tracks along those maps can also be outputted.

Installation

Download repo

git clone https://github.com/ketringjoni/SuPreMo.git

Install SuPreMo or SuPreMo-Akita

For SuPreMo:

  1. Create and activate a conda environment using the given yml file (this should typically be done in the conda "base" environment):
cd SuPreMo/
conda env create -f supremo_env.yml

Make sure this runs with no errors.

conda activate supremo_env
  1. Test that all SuPreMo requirements are properly installed:
python scripts/test_install_SuPreMo.py

This should print: SuPreMo packges successfully imported.

You may also need to install the following: gcc, g++ and libz-dev.

For SuPreMo-Akita:

  1. Create conda environment with tensorflow and check for its proper installation (this should typically be done in the conda "base" environment):
conda create -n supremo_akita_env python=3.10 numpy scipy pandas jupyter tensorflow

You might be asked to confirm installation with a 'y'.

conda activate supremo_akita_env
python -c "import tensorflow"

This should not result in any errors (warnings are ok).

  1. Install basenji with no dependencies and set environmental variables:
cd ~/
git clone https://github.com/calico/basenji.git
cd basenji/
python setup.py develop --no-deps
export BASENJIDIR=~/basenji
export PATH=$BASENJIDIR/bin:$PATH
export PYTHONPATH=$BASENJIDIR/bin:$PYTHONPATH

If downloading the Basenji repo in a directory other than home (~), adjust the path in the export command accordingly.

  1. Install dependencies:
pip install astropy tensorflow-io-gcs-filesystem patsy libclang cooltools Cython biopython pathlib pysam natsort intervaltree pybedtools pybigwig qnorm seaborn statsmodels tabulate jax wrapt==1.14

If pip installation does not work in your system, please troubleshoot the installation of all of the above pacakges.

  1. Test that all SuPreMo-Akita requirements are properly installed:
cd path_to_SuPreMo/
python scripts/test_install_SuPreMo-Akita.py

This should print: SuPreMo packges successfully imported. SuPreMo-Akita packges successfully imported.

For running walkthroughs/tutorials, Jupyter and Matplotlib are also required.

Download genome fasta file (optional*)

wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/<genome>.fa.gz -P path_to_SuPreMo/data/
gunzip path_to_SuPreMo/data/<genome>.fa.gz

Currently accepted genomes are hg19 and hg38.

*If you choose to opt out of this, you will need to specify the path to an existing fasta file using the --fa parameter.

Use

Input format

Suggested input types

  • VCF file
  • TXT file
    • Columns required for simple variants: CHROM, POS, REF, ALT
    • Columns required for structural variants: CHROM, POS, REF, ALT, END, SVTYPE (SV type), SVLEN (SV length)

Additional input types

  • BED-like file
    • Columns required for simple variants (exclude column names): chrom, pos, end, ref, alt
    • Columns required for structural variants(exclude column names): chrom, pos, end, ref, alt, SV type, SV length
  • TSV file

Optional additional input only for SuPreMo-Akita

  • Sequences outputted from SuPreMo in fasta format.

Running SuPreMo

Both SuPreMo and SuPreMo-Akita are implemented under the same python script SuPreMo.py, used as follows:

python scripts/SuPreMo.py INPUT <args>

Required arguments

  • INPUT: Input file with perturbations. File name must end with one of the following suffixes: vcf, txt, bed, tsv. Can be gzipped. Coordinates should be (1-based and left-open), except for SNPs which are fully closed.
  • At least one of the following:
    • --get_seq: Get reference and mutated sequences.
    • --get_Akita_scores: Get disruption scores using Akita.

Optional arguments

  • Overall arguments
    • --help: Print help page and exit.
    • --fa FASTA_FILE: Path to human fasta file. Default is data/{genome}.fa, where genome is hg38 or hg19.
    • --file FILE_PREFIX: Prefix for output files.
    • --dir DIRECTORY: Path for output files.
    • --limit LENGTH: Limit for SV length. Default: 2/3 of sequence length.
    • --nrows N_ROWS: Number of rows to read at a time from input to save memory. Default: 1000.
    • --genome: Genome to be used: hg19 or hg38 (default).
  • get_seq arguments
    • --seq_len LENGTH: Sequence length. Default: 1048576.
    • --shift_by SHIFT: Values to shift sequence window by.
    • --revcomp: Take the reverse complement of the sequence. Options: no_revcomp,add_revcomp,only_revcomp
  • get_Akita_scores arguments
    • --scores SCORES: Scores to be used to calculate 3D genome folding disruption. Options: mse, corr, ssi, scc, ins, di, dec, tri, and pca, from Gunsalus and McArthur et al.
    • --augment: Get scores for augmented predictions (mean and median scores from predictions with shifts and reverse complement).
    • --get_maps: Get predicted contact frequency maps.
    • --get_tracks: Get disruption score tracks.

If multiple inputs are given for an argument, they should be space-separated.

For more details on how to use arguments, refer to help page printed at the top of SuPreMo.py.

Output format

SuPreMo output

  • Sequences
    • Fasta file
    • Sequence name format: {variant index1}{shift2}{reverse complement3}{sequence index4}{variant relative position5}
    • There are 2-3 entries per prediction (2 for non-BND variants and 3 for BND variants).

SuPreMo-Akita output

  • 3D genome disruption scores
    • Tab-delimited text file
    • The first column name: var_index1,
    • The rest of the column names: {method6}{shift2}{reverse complement3}
    • For variants with multiple alternate alleles, the score columns will contain a score for each allele, separated by a comma.
  • Contact frequency maps
    • Numpy file
    • Dictionary item name format: {variant index1}{shift2}{reverse complement3}
    • There is 1 entry per prediction. Each entry contains the following: 2 (3 for chromosomal rearrangements) arrays that correspond to the upper right triangle of the predicted contact frequency maps, the relative variant position in the map, and the first coordinate of the sequence that the map corresponds to.
  • Disruption score tracks
    • Numpy file
    • Dictionary item name format: {variant index1}{method6}{shift2}_{reverse complement3}
    • There is 1 entry per prediction: a 448x1 array.

Superscript descriptions:

  1. Input row number. (For sequences, this is followed by _N for each allele of variants with multiple alternate alleles);
  2. Integer that window is shifted by;
  3. 'revcomp' present only if reverse complement of sequence was taken;
  4. Index for sequences generated for that variant (0-1 for non-BND reference and alternate sequences and 0-2 for BND left and right reference sequence and alternate sequence);
  5. Relative position of variant in sequence (list of two for non-BND variant positions in reference and alternate sequence and an integer for BND breakend position in reference and alternate sequences).
  6. Method used to calculate disruption score;

Tutorials

To note

  • SuPreMo currently only works with hg38 and hg19
  • Coordinates can be 0- or 1- based:
    • Input files: 1-based, left-open (same as 0-based, right-open)
    • Fasta file: 1-based (0-based once read into python)
    • Chromosome lengths: 1-based, fully closed
    • Centromere coordinates: 0-based, right-open

Test sets

We have generated two categories of test sets:

  1. Test sets for edge cases

These are sets of 347 variants that are meant to include all edge cases that SuPreMo should handle and to ensure that SuPreMo is working appropriately with only expected errors and warnings appearing. These were run using the following commands and the outputs are saved in test_data/test_set_edge_cases/

python scripts/SuPreMo.py test_data/test_set_edge_cases/test_set_edge_SV.bed \
                            --dir test_data/test_set_edge_cases \
                            --file test_set_edge_SV \
                            --shift_by -10000 0 10000 \
                            --get_Akita_scores
python scripts/SuPreMo.py test_data/test_set_edge_cases/test_set_edge_simple.bed \
                            --dir test_data/test_set_edge_cases \
                            --file test_set_edge_simple \
                            --shift_by -10000 0 10000 \
                            --revcomp add_revcomp \
                            --get_Akita_scores
  1. Test set for sequences

This is a set of 10 variants for which sequences were generated manually and used as ground truth to ensure that SuPreMo generates reference and mutated sequences accurately. The output sequences for this set from SuPreMo and from the manual curation are not included in this repo due to size, but were tested to be exactly the same.

For any questions and/or feedback, please reach out to katie.gjoni at ucsf dot edu

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Pipeline for generating reference and perturbed sequences for input into predictive models.

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