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

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# 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.

[![Build Status](https://travis-ci.org/zheminzhou/SPARSE.svg?branch=master)](https://travis-ci.org/zheminzhou/SPARSE) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Docs Status](https://readthedocs.org/projects/sparse/badge/)](http://sparse.readthedocs.io/en/latest/)

## Installation

SPARSE runs on Unix and requires Python >= version 2.7

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](requirements.txt) for python module dependencies.

### Installation (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 14.10.2017) to download at http://enterobase.warwick.ac.uk/sparse/

Please download the complete folder refseq_20171014/ and do not change its internal folder structure. The database can be unpacked by running: ` cd refseq_20171014 && sh untar.bash ` This pre-compiled database 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.

  1. 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_20171014 --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

  1. 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 SSR --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

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