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This repo contains code for detecting variants from next-generation sequencing data (BAM / CRAM files) via generative haplotype prediction (see our preprint). Our model uses a deep transformer network to 'generate' haplotypes in the same manner as a modern Large Language Model (LLM), but instead of word tokens, our model generates DNA sequence k-mers for both haplotypes. With this approach, there's not a need for any sophisticated statistical procedures - no HMMs, no de Bruijn graphs, or decisions about mapping quality quality cutoffs, read counts, allele frequencies, etc. The approach allows for true end-to-end deep learning for variant detection.

An earlier version of this tool, called Jovian, was made available in 2022 (see preprint for details). Jovian used a similar encoder architecture, but did not use autoregressive decoding, and had overall lower performance. The current version which uses autoregressive decoders to generate haplotypes is called Jenever.

You'll need a linux / unix compatible system (MacOS is fine) with python >= 3.10 and pip installed.

To install jenever, clone this repository, navigate to the repository directory, and enter:

pip install  .

on the command line. All dependencies should be automatically installed.

It's a good idea to install in a separate conda environment or python virtualenv if possible, but not required unless there are dependency conflicts.

Model weights are stored using git lfs, under the models/ directory. If you don't have git lfs installed, the weights files will appear as small stub files with references to the actual weights objects. If you install git lfs after you've already cloned the repo, run

git lfs fetch

to actually download the weights.

Calling variants requires an alignment file in bam / cram format, a model file, a list of regions to examine in BED format, and a fasta reference sequence. A basical calling command looks like:

jenever call -r <reference genome fasta> 
  --threads <number of threads to use> 
  -m /path/to/model
  --bed /path/to/BED file 
  --bam /BAM or CRAM file
  -c /path/to/classifier.model
  -v output.vcf

The above command will call germline variants in the regions defined by the BED file and write them as a standard VCF file. Runtimes are long and a GPU is required for tolerable performance when more than a few small regions are being called. In general performance is somewhere near 15MB (megabases) per hour, depending on how many regions trigger the generation procedure, the number of threads and batch size, and the GPU speed.

Training requires converting pileups (regions of BAM files) into tensors. Because that process is very slow it makes sense to just do it once and save the tensors to disk so they can be used in multiple training runs. This is called pregen (for pre-generation of training data). The pregenerated training tensors and 'labels' (true alt sequences, stored a k-mer indices) are stored in a single directory. To create pregenerated training data, run

./main.py pregen --threads <thread count> 
  -c <conf.yaml> 
  -d /path/to/output/directory

Depending on how may BAMs and how many labeled instances there are, this can take a really long time.

The configuration file conf.yaml must have a path to the reference genome, the number of examples to choose from each region type, and a list of BAMs + labels, like this:

reference: /path/to/reference/fasta

vals_per_class:
    'snv': 5000   # keys here should match labels in fourth column of BED files provided below    
    'deletion': 500
    'insertion': 500
    'mnv': 1000

data:
  - bam: /path/to/a/bam
    vcf: /path/to/truth/variants.vcf
    bed: /path/to/regions/to/sample.bed

  - bam: /path/to/another/bam
    vcf: /path/to/another/vcf
    bed: /path/to/another/regions/bed
  .... more bams/ labels ...

The BED files must be four-column BED files where the fourth column is the region label. The label scheme is completely flexible and up to the user, but in general should match the values in 'vals_per_class' from the configuration file.

To train a new model, run a command similar to

dnaseq2seq/main.py train -c training_conf.yaml --run-name my_new_run

It's possible to continue training from a checkpoint by providing an input model with the

 --input-model <path to checkpoint model> 

In order to generate well-calibrated quality scores, it's necessary to train a small classifier that learns to predict true and false positive variants. To do this, you must create a configuration file that links VCFs with examples of true and false positive variants to BAM files. The configuration file specifies one or more samples, each of which has a BAM/CRAM file and one or more sets of true positive and false positive variants. Like this:

sample1:
  bam: /path/to/sample1.cram
  fps:
  - false_positive_calls.vcf
  - some_falsepositives.vcf
  tps:
  - true_positives.vcf

sample2:
  bam: /path/to/sample2.bam
  fps:
  - sample2_falsepositives.vcf
  tps:
  - sample2_truepositives.vcf

To generate the classifier, run the dnaseq2seq/builddclf.py tool with the train argument and the path to the configuration file as an option.