Make personal protein databases using next generation sequencing data.

Installation on a Unix/linux distribution (including OS X) is straightforward and enumerated below. To simplify this further (and offer a solution that should work on most platforms), we have included a docker file that will setup a docker container with all necessary dependencies installed.

First, install the community edition docker application for your system.

Next, clone the github repo, which has the latest docker image for genpro and use the docker image to build a container with all dependencies (and GenPro installed).

git clone https://github.com/wingolab-org/GenPro.git
cd GenPro
docker build -t genpro .

There are a few options for running GenPro using the docker container. The simplest way is to login to the container using the command below. For this to be useful, we need to allow the docker container to write to the host system. This is done with the -v option, as in -v /your/machine:/docker/container. You will need to set the host directory accordingly and note that in the example below it is set to the working directory where you launch the genpro docker container.

docker run -it -v $(pwd):/GenProData genpro bash

The approach is:

1. Install local::lib.
2. Install App::cpanminus.
3. Clone the GenPro repository.
4. Install the prebuilt GenPro package in the repository.

Install local::lib by downloading the latest tarball and unpack it. See the bootstrapping section of the documentation.

Example:

curl -O https://cpan.metacpan.org/authors/id/H/HA/HAARG/local-lib-2.000019.tar.gz
tar xzvf local-lib-2.000019.tar.gz
cd local-lib-2.000019
perl Makefile.PL --bootstrap
make test && make install

Install App::cpanminus.

curl -L http://cpanmin.us | perl - App::cpanminus

Install GenPro with cpanm, which will automatically install any uninstalled dependencies.

git clone https://github.com/wingolab/GenPro.git
cd GenPro
cpanm GenPro.tar.gz

An alternative approach is clone the repository, unpack the GenPro.tar.gz tarball and install it manually. This will likely require using sudo, depending on how Perl is setup.

git clone https://github.com/wingolab/GenPro.git
cd GenPro
tar xzvf GenPro.tar.gz
cd GenPro
perl Makefile.PL
make test
make install    

GenPro_download_ucsc_data.pl -a -d hg38 -g hg38

This will perform a dry-run download of hg38 (genome and annotated gene coordinates). It relies on rsync being installed, which should be present on unix, linux, and OS X by default. In the example, the data will be downloaded into hg38 directory, which may be created if it did not already exist. GenPro_download_ucsc_data.pl will download knownGenes track and the genome of the organism by default. By default, GenPro_download_ucsc_data.pl is set to a dry-run (i.e., no download). Use the --act switch to "act", i.e., download the data. Take care when using it since it since it will download from a remote server.

• Use GenPro_create_db.pl.
• There are helper scripts sh/runall_create_db.sh that work with SGE to build all chromosomes on a cluster with SGE. For example:

qsub -v USER -v PATH -cwd -t 1-26 runall_create_db.sh <genome>

• Alternatively, iterate over all the chromosomes. For example:

for ((i=1;i<27;i++)); do
  GenPro_create_db.pl -g hg38 -c $i \
    --genedir hg38/gene             \
    --geneset knownGene             \
    -f hg38/chr -o hg38/idx;
done;

• GenPro_make_refprotdb.pl uses the indexed binary annotation and creates all proteins for a given chromosome.
• A helper script runnall_make_refprot.sh automates this for SGE. For example,

qsub -v USER -v PATH -cwd -t 1-26 runnall_make_refprot.sh \
  <genome>              \
  <binary genome index> \
  <output dir>

• This is a 2-step process using GenPro_make_perprotdb1.pl and GenPro_make_perprotdb2.pl.
• GenPro_make_perprotdb1.pl creates a per chromosome database of all relevant (i.e., nonsense/missense variants) for each sample in the snp file.
• To convert vcf to snp you will need bcftools installed. Use the helper script, sh/vcfToSnp.sh, to perform the conversion.
• GenPro_make_perprotdb2.pl creates a finished personal protein database for each sample. -It provides two outputs:
  1. A json-encoded file that enumerates the variant protein information
  2. A fasta file with all full-length reference and variant proteins, which may be used as input for a proteomics search program.
• Creating the final db is on a per sample basis, but it can take quite a while. It depends on how many proteins have multiple variants. It is especially slow for proteins with >10 variants. For example, if you have a protein with 20 substitutions, there are 20! permutations. By design, all 20! proteins will be considered and only the proteins that contribute unique peptides will be retained. Any protein with more than 20 sites will have all variants inserted into the reference protein without performing any permutation.