/SpliceAI-lookup

web app for checking SpliceAI scores

Primary LanguagePythonMIT LicenseMIT

SpliceAI Lookup API (Local)

Description:

This is a fork from the original SpliceAI-lookup repository, which has been customized to create and implement a local version for our clinical laboratory. Minimal modifications have been made to build and run a Docker container on our server. You can find a copy of this container on Docker Hub.

Additional Files:

  • Dockerfile: The Dockerfile used for building the container.
  • requirements_for_Docker.txt: This file contains different Python library versions required to make the application work in our environment. The original SpliceAI-lookup is designed to work with CentOS Python 3.6.
  • TestingDocker.py: A Python script for testing the container. It uses our control-group variants, which are specified in the spliceai_control.csv file.

Modifications:

  • Some modifications have been made to the Index.html file.
  • The reference files have also undergone minor modifications to include transcripts used in the laboratory. Please note that these reference files are located outside the container.

To run this in BIOINF-2

docker run -v /home/manuel.dominguez/references_files/:/Users/jocotton/Desktop/references_files

The original README.md file below

SpliceAI Lookup API (local)

This is a local version implemented in our laboratory. Minimal modifications has been performed to build and a Docker container in our server.

A copy of this container can be found at

This API serves as the backend for spliceailookup.broadinstitute.org.

The source code for the server and web UI is available @ github.com/broadinstitute/SpliceAI-lookup and is maintained by the TGG.

For more details on SpliceAI, see Jaganathan et al, Cell 2019 in press. and github.com/Illumina/SpliceAI.

As of February, 2023, this server also computes Pangolin scores.

API Examples:

/spliceai/?hg=38&distance=50&variant=chr8-140300616-T-G

Get SpliceAI scores for the given variant.

  • variant (required) a variant in the format "chrom-pos-ref-alt"
  • hg (required) can be 37 or 38
  • distance (optional) distance parameter of SpliceAI model (default: 50)
  • mask (optional) can be 0 which means raw scores or 1 which means masked scores (default: 0). Splicing changes corresponding to strengthening annotated splice sites and weakening unannotated splice sites are typically much less pathogenic than weakening annotated splice sites and strengthening unannotated splice sites. When this parameter is = 1 (masked), the delta scores of such splicing changes are set to 0. SpliceAI developers recommend using raw (0) for alternative splicing analysis and masked (1) for variant interpretation.

/pangolin/?hg=38&distance=50&variant=chr8-140300616-T-G

Get Pangolin scores for the given variant.

  • variant (required) a variant in the format "chrom-pos-ref-alt"
  • hg (required) can be 37 or 38
  • distance (optional) distance parameter of SpliceAI model (default: 50)
  • mask (optional) can be 0 which means raw scores or 1 which means masked scores (default: 0). Splicing changes corresponding to strengthening annotated splice sites and weakening unannotated splice sites are typically much less pathogenic than weakening annotated splice sites and strengthening unannotated splice sites. When this parameter is = 1 (masked), the delta scores of such splicing changes are set to 0. SpliceAI developers recommend using raw (0) for alternative splicing analysis and masked (1) for variant interpretation.

Local Install

The steps below describe how to install a SpliceAI API server on your local infrastructure. The details will vary depending on your OS, etc. If you run into issues, please submit them to the issue tracker.

  1. Install pytorch as described in the Pangolin installation docs
  2. Install and start a redis server. It's used to cache previously computed API server responses so that they don't have to be computed again.
  3. Download reference fasta files: hg19.fa and hg38.fa
  4. Download annotation files into your local ./annotations directory.
  5. Optionally download pre-computed scores .vcf.gz and .vcf.gz.tbi files from Illumina Basespace
  6. Start a SpliceAI API server on localhost port 8080. To modify server options, edit the start_local_server.sh script:
$ git clone git@github.com:broadinstitute/SpliceAI-lookup.git  # clone this repo  
$ cd SpliceAI-lookup  
$ python3 -m pip install -r requirements.txt  # install python dependencies  
$ ./start_local_server.sh

The server uses ~1.5 Gb RAM per server thread.