Package: sequana-nanomerge
This is is thenanomergepipeline from theSequanaprojectOverview:merge fastq files generated by Nanopore run and generates raw data QC.Input:individual fastq files generated by nanopore demultiplexingOutput:merged fastq files for each barcode (or unique sample)Status:productionCitation:Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI doi:10.21105/joss.00352InstallationYou can install the packages using pip:pip install sequana_nanomerge --upgradeAn optional requirements is pycoQC, which can be install with conda/mamba using e.g.:conda install pycoQCyou will also need graphviz installed.Usagesequana_nanomerge --helpIf you data is barcoded, they are usually in sub-directories barcoded/barcodeXY so you will need to use a pattern
(–input-pattern) such as*/*.gz:sequana_nanomerge --input-directory DATAPATH/barcoded --samplesheet samplesheet.csv
    --summary summary.txt --input-pattern '*/*fastq.gz'otherwise all fastq files are in DATAPATH/ so the input pattern can just be*.fastq.gz:sequana_nanomerge --input-directory DATAPATH --samplesheet samplesheet.csv
    --summary summary.txt --input-pattern '*fastq.gz'The –summary is optional and takes as input the output of albacore/guppy demultiplexing. usually a file called sequencing_summary.txtNote that the different between the two is the extra*/before the*.fastq.gzpattern since barcoded files are in individual subdirectories.In both bases, the command creates a directory with the pipeline and configuration file. You will then need to execute the pipeline:cd nanomerge
sh nanomerge.sh  # for a local runThis launch a snakemake pipeline. If you are familiar with snakemake, you can
retrieve the pipeline itself and its configuration files and then execute the pipeline yourself with specific parameters:snakemake -s nanomerge.rules -c config.yaml --cores 4 --stats stats.txtOr usesequanixinterface.Concerning the sample sheet, whether your data is barcoded or not, it should be a CSV filebarcode,project,sample
barcode01,main,A
barcode02,main,B
barcode03,main,CFor a non-barcoded run, you must provide a file where the barcode column can be set (empty):barcode,project,sample
,main,Aor just removed:project,sample
main,AUsage with apptainer:With apptainer, initiate the working directory as follows:sequana_nanomerge --use-apptainerImages are downloaded in the working directory but you can store then in a directory globally (e.g.):sequana_nanomerge --use-apptainer --apptainer-prefix ~/.sequana/apptainersand then:cd nanomerge
sh nanomerge.shif you decide to use snakemake manually, do not forget to add apptainer options:snakemake -s nanomerge.rules -c config.yaml --cores 4 --stats stats.txt --use-apptainer --apptainer-prefix ~/.sequana/apptainers --apptainer-args "-B /home:/home"RequirementsThis pipelines requires the following executable(s), which is optional:pycoQCdotDetailsThis pipeline runsnanomergein parallel on the input fastq files (paired or not).
A brief sequana summary report is also produced.Rules and configuration detailsHere is thelatest documented configuration fileto be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.ChangelogVersionDescription1.5.0refactoring to use Click1.4.0sub sampling was biased in v1.3.0. Using stratified sampling to
correcly sample large file. Also set a –promethion option that
auomatically sub sample 10% of the dataadd summary table1.3.0handle large promethium run by using a sub sample of the
sequencing summary file (–sample of pycoQC still loads the entire
file in memory)1.2.0handle large promethium run by using find+cat instead of just
cat to cope with very large number of input files.1.1.0add subsample option and set to 1,000,000 reads to handle large
runs such as promethion1.0.1CSV can now handle sample or samplename column name in samplesheet.Fix the pyco file paths, update requirements and doc1.0.0Stable release ready for production0.0.1First release.