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Scripts and tools used to develop one-step real-time RT-LAMP assays for specific detection of dengue virus.
##Associated publication
Development of four one-step real-time RT-LAMP assays for specific detection of dengue virus serotypes. Lopez-Jimena B, Bekaert M, Bakheit M, Frischmann S, Patel P, Sakuntabhai A, Lambrechts L, Fall C, Faye O, Sall A and Weidmann M. PLOS Negl. Trop. Dis.. 12(7): e0180625
This repository host both the scripts and tools used by this study and the raw results generated at the time. Feel free to adapt the scripts and tools, but remember to cite their authors!
To look at our scripts and raw results, browse through this repository. If you want to reproduce our results you will need to clone this repository, build the docker, and the run all the scripts. If you want to use our data for our own research, fork this repository and cite the authors.
All required files and tools run in a self-contained docker image.
git clone https://github.com/pseudogene/lamp-denv.git
cd lamp-denv
docker build --rm=true --file=Dockerfile -t denv-docker .
To import and export the results of your analyse you need to link a folder to the docker. It this example your data will be store in results (current filesystem) which will be seem as been /mnt from within the docker by using -v <USERFOLDER>:/mnt.
mkdir ~/results
docker run -i -t -v ~/results:/mnt --name dengue denv-docker /bin/bash
1 - Collect NCBI genomes and automatically aligned them with GramAlign v3.0
./collect_genomevirus.pl -f denv1.fasta -d 1 -y 2004 -b 2014
./collect_genomevirus.pl -f denv2.fasta -d 2 -y 2004 -b 2014
./collect_genomevirus.pl -f denv3.fasta -d 3 -y 2004 -b 2014
./collect_genomevirus.pl -f denv4.fasta -d 4 -y 2004 -b 2014
where:
-f is the output file where the sequences will be saved.
-d is the dengue virus serotypes: 1, 2, 3 or 4.
-y or -a (after) is the lower limit for the year.
-b (before) is the upper limit for the year.
2 - Run R/adegenet on the 4 strains to generate PCA and phylogeny
Rscript denv.R --no-save --slave -q
cp -r denv?.* /mnt
3 - In /mnt (from within the docker) or results (from outside) you now have all the results files including PCA, phylogeny and TSV files.
Edit the TSV (Tabulation Separated Values) files by adding two new columns (columns 5 and 6) with the genotype/group numbers you manually identified. The fifth column been a detailed subgoups (to be used with the loose parameter sets), the six column been the less fragmented (to be used with the standard parameter sets)
e.g.
PC1 PC2 PC3
sequence_1 -14.75 -7.31 1.42
sequence_2 -14.16 9.83 1.00
becomes:
PC1 PC2 PC3 group group
sequence_1 -14.75 -7.31 1.42 1 1
sequence_2 -14.16 9.83 1.00 2 2
**4 **- Import your data. Copy the modified TSV in /mnt (from within the docker) or results (from outside)
dos2unix -c mac *.tsv
dos2unix *.tsv
cp *.tsv /dengue
5 - Retrieve the subgroups/genotypes from the TSV file and run LAVA-DNA on each genotype and each combination.
./class_sequences.pl -a denv1.fasta.align.fa -l denv1.pca.edited.tsv -c > denv1.log
./class_sequences.pl -a denv2.fasta.align.fa -l denv2.pca.edited.tsv -c -e > denv2.loose.log
./class_sequences.pl -a denv3.fasta.align.fa -l denv3.pca.edited.tsv -c > denv3.log
./class_sequences.pl -a denv4.fasta.align.fa -l denv4.pca.edited.tsv -c -e > denv4.loose.log
where:
-a is the alignment generated by GramAlign (fasta-align format).
-l is the list of subgroups/genotypes (TSV file).
-c allows to test combination of genotypes and not only each genotypes.
-e allows for the loose parameter set rather than standard by LAVA-DNA.
6 - Then evaluate the best minimal set of primer and map each primer set
cd denv1
../map_lamp.pl -svg -a denv1.fasta.align.fa \
-p denv1.fasta.align.fa_1.primers \
-p denv1.fasta.align.fa_11.primers \
-p denv1.fasta.align.fa_19_20_8.primers \
-p denv1.fasta.align.fa_13.primers \
> denv1.svg
cd ..
where:
-svg or -png will generate a SVG or PNG image.
-a is the alignment generated by GramAlign (fasta-align format).
-p are lists of primer sets generated by ./class_sequences.pl.
If you have any problems with or questions about the scripts, please contact us through a GitHub issue. Any issue related to the scientific results themselves must be done directly with the authors.
You are invited to contribute new features, fixes, or updates, large or small; we are always thrilled to receive pull requests, and do our best to process them as fast as we can.
This code is distributed under the GNU GPL license v3. The documentation, raw data and work are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.