You can take a look at the config files which point to data already stored within the Docker image. If you want to run SignalAlign using your own data, you just need to place your data and config into a directory and set the config up so that it points to the data correctly once you mount the local directory to the docker image.

1. docker run -v /complete/path/to/signalAlign/tests/test_Docker/:/data adbailey4/train_sa_models run --config /data/trainModels-config.json
2. docker run -v /complete/path/to/signalAlign/tests/test_Docker/:/data adbailey4/signalalign run --config /data/runSignalAlign-config.json

• git-2.17.1, gcc-5, g++-5, make-4.1, zlib-1:1.2.11.dfsg-0ubuntu2, libbz2-1.0.6-8.1ubuntu0.2, liblzma-5.2.2-1.3, numpy-1.16.4, setuptools-41.0.1, python-3.5, wget-1.19.4-1ubuntu2.2
• Note: If you have newer versions of any of these installed and it does not work please file an issue.
• Needed in the path
  • samtools
  • bwa
• If for some reason your server does not have pip3 associated with python3.5 you can make your own local pip
  • wget https://bootstrap.pypa.io/get-pip.py
  • python3 get-pip.py --user

1. Recursively clone this repo git clone --recursive https://github.com/UCSC-nanopore-cgl/signalAlign.git
2. Make project
   make
3. Install all required python packages.
   pip install -e . --user
4. Add bin to path
   export PATH=$PATH:$PWD/bin
5. Test install
   make test

Nanopore sequencing is based on the principal of isolating a nanopore in a membrane separating buffered salt solutions, then applying a voltage across the membrane and monitoring the ionic current through the nanopore. The Oxford Nanopore Technologies (ONT) MinION sequences DNA by recording the ionic current as DNA strands are enzymatically guided through the nanopore. SignalAlign will align the ionic current from the MinION to a reference sequence using a trainable hidden Markov model (HMM). The emissions model for the HMM can either be the table of parametric normal distributions provided by ONT or a hierarchical Dirichlet process (HDP) mixture of normal distributions. The HDP models enable mapping of methylated bases to your reference sequence. Instructions for usage including building/training HDP models can be found in the manual.