Name: MaRS
Owner: Centers for Disease Control and Prevention Surveillance Strategy
Description: null
Created: 2017-12-06 16:46:57.0
Updated: 2018-04-30 14:30:56.0
Pushed: 2018-05-09 17:17:53.0
Homepage: https://cdcgov.github.io/MaRS/
Size: 700153
Language: Java
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The emergence of resistance to all currently available antimalarial drugs in multiple regions of the world represents a current global public health challenge. In order to monitor and address this situation, faster and more effective surveillance tools are required to track and monitor the emergence and evolution of drug resistance in malaria. The Malaria Resistance Surveillance (MaRS) project aims to address this challenge by collating and mapping genetic polymorphisms associated with drug resistance in malaria around the world. The project achieves this by employing a targeted amplicon deep sequencing (TADS) approach Lab Protocol to detect single nucleotide polymorphisms on all major malaria drug resistance genes associated genes in samples sourced from travelers returning to the US from overseas, as well as samples actively collected in collaboration with partners from other countries.
Data for this project can be found at the following link NCBI BioProject. Collaborators are encouraged to submit their own data using this NCBI BioProject
The Malaria Resistance Surveillance or MaRS analysis pipline, is an attempt at standardizing the workflow for identifying both known and new polymorhisms in P.falciparum genes associated with drug resistance.
If you end up using MaRS in your workflow, please cite this study:
-Generation Sequencing and Bioinformatics Protocol for Malaria Drug Resistance Marker Surveillance.
ndzic E, Ravishankar S, Kelley J, Patel D, Plucinski M, Schmedes S, Ljolje D, Clemons B,
son-Antenucci S, Arguin PM, Lucchi NW, Vannberg F, Udhayakumar V.
microb Agents Chemother. 2018 Mar 27;62(4). pii: e02474-17. doi: 10.1128/AAC.02474-17. Print 2018 Apr.
Clone the master branch of this repository.
clone https://github.com/CDCgov/MaRS.git
MaRS requires python3 to be installed with pip available. Please make sure this is available on the system. To avoid clashes with system version of required python modules, we recommend using a virtualenv Run the following command to install virtualenv, if you already have virtualenv installed
on3 -m pip install virtualenv
ualenv mars_env # Setup mars virtual environment
ce mars_env/bin/activate # Activate virtual environment
If successfully activated, you should see now (mars_venv) in front of your terminal username.
MaRS uses many python modules Run the following command to install the dependencies
install pyvcf pysam matplotlib seaborn pandas numpy xlrd openpyxl
list --format=columns
Follow the directory structure listed below and use the run script included with the bundle to run your first analysis.
un.sh <path to experiment folder> <path to output folder>
For example if you have stored your fastq files in `fq/folder and you want to store the results in the folder ``local/```. You can run the following command from the MaRS directory.
un.sh fq/ local/
For each sample
CleanedFastq: Folder containing adapter trimmed and quality filtered Fastq files
output.sam : Contains reads aligned to reference genome
output_sorted.bam : Sorted BAM containing aligned reads
output_sorted_RG.bam : Read group added BAM file
outout_fixmate.bam : Final BAM file, with mate information corrected
Sample-name_variants_samtools_annotated.vcf : Annotated variant calls from Samtools
Sample-name_variants_gatk_annotated.vcf : Annotated variant calls from GATK HaplotypeCaller
Sample-name_variants_merge_annotated.vcf : Merged and annotated variant calls from GATK HaplotypeCaller and Samtools
For the study
Study_variants.xlsx : Summary table of all known variants that confer drug resistance, for all samples in the study
Study_depth.xlsx : Summary of depth of coverage for codon correponding to variants that confer drug resistance
Study_al_freq.xlsx : Summary of allele frequency of variants that confer drug resistance
Study_novel_exonic_variants.xlsx : Summary of all novel variants found in exonic regions, for all samples in the study
Study_novel_intronic_variants.xlsx : Summary of all novel variants found in intronic regions, for all samples in the study
Study_novel_var_af.xlsx : Summary of allele frequency of novel variants for all samples
Study_novel_var_depth.xlsx : Summary of depth of coverage for codon correponding to novel variants for all samples
voi_af_heatmap.jpg : Heatmap showing the allele frequency across all samples for variants known to confer drug resistance
voi_dp_heatmap.jpg : Heatmap showing the codon coverage across all samples for variants known to confer drug resistance
voi_frequency.jpg : Count plot showing frequency variants conferring drug resistance across all the samples in the study
This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.
The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.
This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.
This soruce code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details.
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