meta-sparse 0.1.6

SPARSE indexes reference genomes in public databases into hierarchical clusters and uses it to predict origins of metagenomic reads.

Strain Prediction and Analysis using Representative SEquences (SPARSE)

SPARSE indexes >100,000 reference genomes in public databases in to hierarchical clusters and uses it to predict origins of metagenomic reads.

Installation

SPARSE runs on Unix and requires Python version 2.7 (Python 3.x supports are under development)

System modules (Ubuntu 16.04) :

• pip
• gfortran
• llvm
• libncurses5-dev
• cmake
• xvfb-run (for malt, optional)

3rd-party software:

• samtools (>=1.2)
• mash (>=1.1.1)
• bowtie2 (>=2.3.2)
• malt (>=0.4.0) (optional)

See requirements.txt for python module dependencies.

Installation via PIP

pip install meta-sparse

Installation from source codes (Ubuntu)

sudo apt-get update
sudo apt-get install gfortran llvm libncurses5-dev cmake python-pip samtools bowtie2
git clone https://github.com/zheminzhou/SPARSE
cd SPARSE/EM && make
pip install -r requirements.txt 

Updating SPARSE

To update SPARSE, move to installation directory and pull the latest version:

cd SPARSE
git pull

Quick Start

See http://sparse.readthedocs.io/en/latest/ for full documentation.

1. Download reference database

We provide a pre-compiled database based on RefSeq (dated 19.05.2018) to download at http://enterobase.warwick.ac.uk/sparse/refseq_20180519.tar.gz . The database can be downloaded and unpacked by running:

 curl -o refseq_20180519.tar.gz http://enterobase.warwick.ac.uk/sparse/refseq_20180519.tar.gz
 tar -vxzf refseq_20180519.tar.gz

This pre-compiled database is about 350GB and contains four default mapping databases, which can be specified in the next step: representative, subpopulation, Virus, Eukaryota.

To update the database or build a costum database, please refer to the full documentation.

2. Predict read origins

This following command will map and evaluate all reads in both fastq-files against the specified mapping databases.

python SPARSE.py predict --dbname refseq_20180519 --mapDB representative,subpopulation,Virus,Eukaryota --r1 read1.fq.gz --r2 read2.fq.gz --workspace <workspace_name>

For single-end reads, only --r1 needs to be specified. All output files are stored in the respective workspace.

3. Create a report

python SPARSE.py report <workspace_name>

The report will be stored in <workspace_name>/profile.txt

4. Extract reference specific reads

The following command extracts all reads specific to the provided reference ids, which can be found in the output of step 2.

python SPARSE.py extract --dbname refseq_20171014 --workspace <workspace_name> --ref_id <comma delimited indices>

Citation

SPARSE has not been formally published yet. If you use SPARSE please cite the preprint https://www.biorxiv.org/content/early/2017/11/07/215707

Zhemin Zhou, Nina Luhmann, Nabil-Fareed Alikhan, Christopher Quince, Mark Achtman, 'Accurate Reconstruction of Microbial Strains Using Representative Reference Genomes' bioRxiv 215707; doi: https://doi.org/10.1101/215707