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Bernardo Aguzzoli Heberle authored
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README.md

DCNL_NANOPORE_PIPELINE

NextFlow pipeline used by the Developmental Cognitive Neuroscience Lab (DCNL) to process Oxford Nanopore (ONT) DNA methylation data

Getting Started

1) Have a functioning version of Nextflow in your Path.

  • Information on how to install NextFlow can be found here.

2) Have a functioning version of Singularity on your Path.

  • Information on how to install Singularity cna be found here

3) Clone this github repo using the command below

git clone https://github.com/bernardo-heberle/DCNL_NANOPORE_PIPELINE

4) Make sure you have all the sequencing files and reference genomes/assemblies files and annotation files you will need to run the pipeline.

  • ".fast5" or ".pod5" files.

  • refecence/assembly ".fa" file specific to your organism of interest.

5) Set NXF_SINGULARITY_CACHEDIR environment variable to your desired directory:

Substitute /<your>/<desired>/<path>/ in the codeblock below for the path to the directory you would like to store your singularity images in. Make sure the directory exists before executing the pipeline.

echo "" >> ~/.bash_profile && echo 'NXF_SINGULARITY_CACHEDIR="/<your>/<desired>/<path>/"' >> ~/.bash_profile && echo 'export NXF_SINGULARITY_CACHEDIR' >> ~/.bash_profile && . ~/.bash_profile

Pipeline parameters:

Many of the parameters for this step are based on dorado basecaller, see their documentation to understand it better.

--basecall_path               <path to base directory containing all fast5 and/or pod5 files you want to basecall.
                              It will automatically separate samples based on naming conventions and 
                              directory structure. example: /sequencing_run/">

--basecall_speed              <"fast", "hac", "sup". Default = "hac">

--basecall_mods               <Comma separated list of base modifications you want to basecall. See dorado docs 
                              for more information. Example: "5mCG_5hmCG,5mC_5hmC,6mA". Default: "False">

--basecall_compute            <"gpu", "cpu". Default: "gpu". Allows users to choose to basecall with 
                              CPU or GPU. CPU basecalling is super slow and should only be used for small
                              test datasets when GPUs are not available. Also, use --basecall_speed "fast"
                              when basecalling with CPU to make it less slow. Default: "gpu">

--basecall_config             <configuration name for basecalling setting. This is not necessary since dorado 
                              is able to automatically determine the appropriate configuration. When set to "None"
                              the basecaller will automatically pick the basecall configuration.
                              Example: "dna_r9.4.1_e8_hac@v3.3". Default: "None">

--basecall_trim              <"all", "primers", "adapters", "none". Default: "None">

--qscore_thresh              <Mean quality score threshold for basecalled reads to be considered passing. Default: 9>


--demux                       <"True", "False". Whether you want the data to be demultiplexed
                              setting it to "True" will perform demultiplexing. Default: "False">

--trim_barcodes               <"True", "False". Only relevant is --demux is set to "True". 
                              if set to "True" barcodes will be trimmed during demultiplexing
                              and will not be present in output "fastq" files. Default: "False">

--gpu_devices                 <Which gpu devices to use for basecalling. Only relevant when
                              parameter "--basecall_compute" is set to "gpu". Use the 
                              "nvidia-smi" command to see available gpu devices and their
                              current usage. Default: "all". Alternative: "0". 
                              Second Alternative: "0,1,2".

--prefix                      <Will add a prefix to the beggining of your filenames, good
                               when wanting to keep track of batches of data.
                               Example: "Batch_1". Default value is "None" which does not add any prefixes>

--out_dir                    <Name of output directory. Output files/directories will be output to
                              "./results/<out_dir>/" in the directory you submitted the pipeline from.
                              Default: "output_directory">

Submission example:

nextflow ../workflow/main.nf --basecall_path "../../data/test_data/" \
        --basecall_speed "hac" \
        --ref "../references/mouse_reference.fa" \
        --gpu_devices "all" \
        --basecall_mods "5mC_5hmC" \
        --basecall_config "False" \
        --basecall_trim "none" \
        --basecall_compute "gpu" \
        --basecall_demux "False" \
        --out_dir "test_basecall_gpu_no_demux_mouse"

Pipeline output directory description:

  1. fast5_to_pod5 - One directory per sample. Only exists for sample that had any fast5 files converted into pod5 files for more efficient basecalling with Dorado.

  2. basecalling_output - Dorado basecalling output. One fastq file per sample and one sequencing summary file per sample. Reads for the same run will be separated into different fastq files based on barcode when demultiplexing is enabled. `