cerebra 1.1.2

finds mutants in your scRNA-seq experiment

cerebra

What is cerebra?

This tool allows you to quickly extract meaningful variant information from a DNA or RNA sequencing experiment. If you're interested in learning what variants are present in your DNA/RNA samples, variant callers such as GATK HaplotypeCaller can be used to generate variant calling format (VCF) files following a sequencing experiment. A VCF file looks like this:

##source=HaplotypeCaller
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT
chr1 631391 . C T 72.28 . AC=2;AF=1.00;AN=2;DP=2;ExcessHet=3.0103;FS=0.000;MLEAC=1;MLEAF=0.500;MQ=NaN;QD=25.36;SOR=2.303 GT:AD:DP:GQ:PL 1/1:0,2:2:6:84,6,0

Note that only a single VCF record is displayed here. A sequencing run can generate on the order of 10⁸ unique VCF records. Only a small portion of these VCF records are likely to be relevant to the researcher. Thus drawing conclusions from VCF files remains a substantial challenge.

cerebra provides a fast and intuitive framework for summarizing VCF records across samples. It is comprised of three modules that do the following:

    1) remove germline variants from samples of interest        
    2) count the total number of variants in a given sample, on a per-gene basis           
    3) report peptide-level variants for each sample                 

cerebra gets its name from the eponymous X-men character, who had the ability to detect mutant individuals among the general public.

If you're working with tumor data, it might be a good idea to limit the variant search space to only known cancer variants. Therefore cerebra implements an optional method for restricting to variants also found in the COSMIC database.

This tool was developed for, but is certainly not limited to, single-cell RNA sequencing data. cerebra is free software available under the MIT license.

What makes cerebra different from traditional VCF parsers?

Python libraries exist (i.e. PyVCF and vcfpy) for extracting information from VCF files. Another is GATK VariantsToTable, which produces a file that looks like this:

CHROM    POS ID      QUAL    AC
 1        10  .       50      1
 1        20  rs10    99      10

This table contains only genomic (i.e. DNA-level) coordinates. Often the next questions are: what are the genes associated with these variants, and what are the peptide-level effects of these variants? cerebra queries a reference genome (.fa) and annotation (.gtf) to match each DNA-level variant with its associated gene, and its probable peptide-level variant. cerebra produces the following outfile:

$ python
> import json
> f = open(/path/to/cerebra/output.json)
> data = json.load(f)
> print(json.dumps(data, indent=4))

{
    "CCN1": {
        "A16_B000563": [],
        "A1_B001546": [],
        "A1_B002531": [
            "ENSP00000398736.2:p.(Glu189=)"
        ],
        "A1_B002570": [],
        "A2_B002558": [],
        "A3_B000561": [
            "ENSP00000398736.2:p.(Arg209Trp)",
            "ENSP00000398736.2:p.(Ile90Val)"
        ],
        "A3_B000568": [],
        "A3_B001544": [
            "ENSP00000398736.2:p.(Ala82Thr)"
        ],
        "A3_B002090": [],
        "A3_B002531": [
            "ENSP00000398736.2:p.(Pro217Ser)"
        ]
    }
}

Here CCN1 is a gene name while A16_B000563, A1_B001546, A1_B002531,... are RNA-seq sample IDs. cerebra reports variants to every gene in the genome, for every sample in a given experiment. The ENSP* numbers refer to Ensembl translation IDs -- unique identifiers that correspond to exactly one polypeptide in the Ensembl database. The strings enclosed in parenthesis refer to the amino-acid level variants reported in that particular sample. For example the string Arg209Trp indicates that position 209 of this particular polypeptide should contain an Arg, but the experimental sample instead contains a Trp. cerebra adheres to HGVS sequence variant nomenclature in reporting amino-acid variants.

Features

germline-filter

If the research project is centered around a "tumor/pathogenic vs control" question, then germline-filter is the proper starting point. This module removes germline variants that are common between the control and the experimental tissue so as to not bias the results by including non-pathogenic variants. The user provides a very simple metadata file that indicates which experimental samples correspond to which control samples. The output of germline-filter is a set of trimmed-down VCFs.

count-variants

This module reports the raw variant counts for every gene across every sample. The output is a CSV file that contains counts for each sample versus every gene in the genome. If working with cancer variants, the user has the option of limiting the search space to variants also found in the COSMIC database.

find-peptide-variants

This module reports the peptide-level consequence of genomic variants. VCF records are converted to peptide-level variants, and then ENSEMBL protein IDs, in acordance to the HGVS sequence variant nomenclature. The output is a heirarchically ordered text file (CSV or JSON) that reports the the Ensemble protein ID and the gene associated with each variant, for each experimental sample. The user again has the option of limiting the search space to variants also found in the COSMIC database.

Installation

There are four different methods available to install cerebra.

With Docker

docker pull lincolnharris/cerebra
docker run -it lincolnharris/cerebra

warning: this image will take up ~1Gb of space.

With traditional git clone and the python standard library venv module

git clone https://github.com/czbiohub/cerebra.git
cd cerebra
python3 -m venv cerebra-dev
source cerebra-dev/bin/activate
pip3 install -e . 

With traditional git clone and conda

git clone https://github.com/czbiohub/cerebra.git
cd cerebra
conda create -n cerebra python=3.7
conda activate cerebra
pip3 install -e . 

From PyPi (system-wide installation)

pip install cerebra

# OR, if you dont have root privileges
pip install --user cerebra

cerebra depends on some (fairly standard) packages and libraries. If youre having trouble installing, it might be a good idea to make sure you have all of the requisite dependendices installed first (note: if installing with Docker you can skip this step).

MacOS Dependencies:

sudo pip install setuptools
brew update
brew install openssl
brew install zlib

Linux Dependencies:

sudo apt-get install autoconf automake make gcc perl zlib1g-dev libbz2-dev liblzma-dev libcurl4-gnutls-dev libssl-dev

As of present cerebra is not installable on Windows. cerebra depends on the pysam library (or rather, pysam is a dependency-of-a-dependency) and currently this library is only available on Unix-like systems.

(Quickstart) Usage

$ cerebra should return usage information

Usage: cerebra  <command>

  a tool for fast and accurate summarizing of variant calling format (VCF)
  files

Options:
  -h, --help  Show this message and exit.

Commands:
  germline-filter    filter out common SNPs/indels between control/germline samples and samples of interest
  count-variants    count total number of variants in each sample, and report on a per-gene basis
  find-peptide-variants  report peptide-level SNPs and indels in each sample, and associated coverage

Note that the -h command will display usage information for each of the three commands.

An example workflow might look like this:

Step 1:

cerebra germline-filter --processes 2 --control_path /path/to/control/vcfs --experimental_path /path/to/experimental/vcfs --metadata /path/to/metadata/file --outdir /path/to/filtered/vcfs

Step 2:

cerebra count-variants --processes 2 --cosmicdb /optional/path/to/cosmic/database --refgenome /path/to/genome/annotation --outfile /path/to/output/file /path/to/filtered/vcfs/*

Step 3:

cerebra find-peptide-variants --processes 2 --cosmicdb /optional/path/to/cosmic/database --annotation /path/to/genome/annotation --genomefa /path/to/genome/fasta --report_coverage 1 --output /path/to/output/file /path/to/filtered/vcfs/*

For advanced usage information, see USAGE.md.

Authors

This work was produced by Lincoln Harris, Rohan Vanheusden, Olga Botvinnik and Spyros Darmanis of the Chan Zuckerberg Biohub. For questions please contact [e-mail]

Contributing

We welcome any bug reports, feature requests or other contributions. Please submit a well documented report on our issue tracker. For substantial changes please fork this repo and submit a pull request for review.

See CONTRIBUTING.md for additional details.

You can find official releases here.