Nanopore sequencing provides rapid and reliable insight into microbial profiles of Intensive Care Units
Guilherme Marcelino Viana de Siqueira, Felipe Marcelo Pereira-dos-Santos, Rafael Silva-Rocha, María-Eugenia Guazzaroni
In this repository we provide a custom pipeline for taxonomic assignment of long-read 16S sequencing. We have submitted this work in Biorxiv!!
In Python, import the library provided in minion_analysis.py with the following command:
import minion_analysis
Next, provide paths in which demultiplexed sequencing files (.FASTQ) might be found. Assuming that we have our demultiplexed files for two sequencing runs in the subdirectories Experiment1 and Experiment2, within a directory experiment_data in the user's Documents directory, the command would look like:
path = '/home/user/Documents/experiment_data'
lib = ['Experiment1', 'Experiment2']
The next steps automate the pipeline guppy_barcoder (for removing barcodes) -> NanoFilt -> Minimap2. The minimap2 step depends on the file refseq_16S.fa, that can be found in this repository as a .zip file
HC_analysis = minion_analysis.Analysis(path, lib)
HC_analysis.merge_files(cpus = 4)
HC_analysis.guppy_barcoder(cpus = 2)
HC_analysis.nanofilt(q = 7, cpus=2, just_filt = False)
HC_analysis.minimap(refseqpath = path, nanofilt_q = 7, cpus = 2)
After processing the files, obtaining the read count for each resulting alignment files (.paf)can be done with the alignment-parser.R script.
Rscript --vanilla ./alignment-parser.R [path] [lib(s)]
For this, the nametable.tsv file (that relates NCBI accession numbers and species names) should be located in a directory within the working environment.
UPDATE July 21st 2021: We now provide an example for our downstream analysis in R. Data and code can be found in the directory analysis-example. Please, feel free to contact us at viana.guilherme@usp.br for any questions or suggestions.