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

Project description

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 License: GPL v3 Docs Status

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 [Suggested]

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

You can update to latest version using PIP:

pip install --upgrade meta-sparse

If you installed SPARSE from github, 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.

  1. Predict read origins

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

sparse 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.

  1. Create a report
sparse 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.

sparse extract --dbname refseq_20171014 --workspace <workspace_name> --ref_id <comma delimited indices>

Citation

SPARSE is published as a conference proceeding in "Research in Computational Molecular Biology".

Zhemin Zhou, Nina Luhmann, Nabil-Fareed Alikhan, Christopher Quince, Mark Achtman, 'Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes' RECOMB 2018: Research in Computational Molecular Biology pp 225-240. doi: https://doi.org/10.1007/978-3-319-89929-9_15

A preprint version of the manuscript is also accessible in bioRxiv: https://doi.org/10.1101/215707

Project details


Download files

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Filename, size & hash SHA256 hash help File type Python version Upload date
meta-sparse-0.1.11.tar.gz (27.6 MB) Copy SHA256 hash SHA256 Source None Jul 18, 2018

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