CAVA 1.3.4.1

CAVA (Clinical Annotation of VAriants)

• CAVA README
  • 1 INTRODUCTION
  • 2 PUBLICATION
  • 3 DEPENDENCIES
  • 4 INSTALLATION ON LINUX OR MAC
  • 5 RUNNING CAVA
  • 6 LICENCE

CAVA README

1 INTRODUCTION

CAVA (Clinical Annotation of VAriants) is a lightweight, fast, flexible and easy-to-use Next Generation Sequencing (NGS) variant annotation tool. It implements a clinical sequencing nomenclature (CSN), a fixed variant annotation consistent with the principles of the Human Genome Variation Society (HGVS) guidelines, optimised for automated clinical variant annotation of NGS data.

Since 2017, CAVA has been maintained by a group of bioinformaticians involved in both research and clinical genomics, adding several key functionalities, enhancements, and general support along the way.

2 PUBLICATION

If you use CAVA, please cite:

Márton Münz, Elise Ruark, Anthony Renwick, Emma Ramsay, Matthew Clarke, Shazia Mahamdallie, Victoria Cloke, Sheila Seal, Ann Strydom, Gerton Lunter, Nazneen Rahman. CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinical setting. Genome Medicine 7:76, doi:10.1186/s13073-015-0195-6 (2015).

Maybe some day, we'll get around to publishing what we've done to rescue this abandoned project.

3 DEPENDENCIES

To install and run CAVA you will need the following dependencies installed:

• Python 3
• GCC and GNU make
• virtualenv

It just makes sense to keep things in a virtualenv. Here's how you do it if you are unfamiliar.

pip install virtualenv
virtualenv cava
source cava/bin/activate

At this point, your terminal should change to let you know you are in a virtual environment.

4 INSTALLATION ON LINUX OR MAC

pip install cava

# - or -
git clone [e-mail]:Steven-N-Hart/CAVA.git
# optional to checkout release
# e.g. git checkout v.1.2.4
python setup.py install

5 RUNNING CAVA

Before using CAVA, you will need to create a database of transcripts for which to base your annotations from. Details can be found in this README. In short, we reccomend using MANE transcripts, so to get started, you would simply:

# Download GTF files for either RefSeq or ENSEMBLE
wget -O data/ENST.gtf.gz ftp://ftp.ncbi.nlm.nih.gov/refseq/MANE/MANE_human/release_0.91/MANE.GRCh38.v0.91.select_ensembl_genomic.gtf.gz and
wget -O data/RefSeq.gtf.gz ftp://ftp.ncbi.nlm.nih.gov/refseq/MANE/MANE_human/release_0.91/MANE.GRCh38.v0.91.select_refseq_genomic.gtf.gz

# Separate into ENST and NM Transcripts
zcat data/ENST.gtf.gz |cut -f9|cut -f4 -d' '|grep ENST|sed 's/;//;s/\"//g'|sort -u > data/ENST.txt
zcat data/RefSeq.gtf.gz |cut -f9|cut -f4 -d' '|grep "NM_"|sed 's/;//;s/\"//g'|sort -u > data/RefSeq.txt

# Look at the options and configure appropriately
python3 bin/MANE.py -h

CAVA can be run with the following simple command:

python3 bin/CAVA.py -c config.txt -i input.vcf -o output

It requires three command line arguments: the name of the configuration file (-c), the name of the input file (-i) and the prefix of the output file name (-o).

6 LICENCE

CAVA is released under MIT licence (see the LICENCE file).