areebapatel / rapid-cns2 Goto Github PK

This pipeline analyses CNS tumour data generated through Nanopore adaptive sequencing using ReadFish or adaptive sampling on MinKNOW. It requires FAST5 files or FASTQ file generated from the sequencing run as input.

Sequencing

Sequencing using ReadFish on a MinION device is recommended on a workstation/notebook powered by a NVIDIA RTX2080 GPU.

Instructions (from ReadFish)

1. Install ReadFish

# Make a virtual environment
python3 -m venv readfish
. ./readfish/bin/activate
pip install --upgrade pip

# Install our ReadFish Software
pip install git+https://github.com/LooseLab/read_until_api_v2@master
pip install git+https://github.com/LooseLab/readfish@master

2. Download and edit the reference field in read_until.toml
3. Load the flowcell and start sequencing on MinKNOW
4. Open terminal and start guppy_basecall_server

guppy_basecall_server --config dna_r9.4.1_450bps_fast.cfg --port 5555 --device "cuda:0" --qscore_filtering

3. Open new tab in terminal and run ReadFish

readfish targets --device <YOUR_DEVICE_ID> \
              --experiment-name "RU Test basecall and map" \
              --toml <PATH_TO_TOML> \
              --log-file ru_test.log

You should get outputs as

2020-02-24 16:45:35,677 ru.ru_gen 7R/0.03526s
2020-02-24 16:45:35,865 ru.ru_gen 3R/0.02302s
2020-02-24 16:45:35,965 ru.ru_gen 4R/0.02249s

If these times > 0.4s, targeting is not working as expected.

RAPID-CNS² analysis

1. If you do not have an existing conda installation, follow these steps. If you have conda preinstalled, skip to step 2.

• Download the appropriate Miniconda installer here
• Open terminal, go to directory containing the Miniconda file. Enter bash Miniconda3-latest-Linux-x86_64.sh

2. Install snakemake

conda install -n base -c conda-forge mamba
conda activate base
mamba create -c conda-forge -c bioconda -n snakemake snakemake
conda activate snakemake

3. Install CNVpytor

git clone https://github.com/abyzovlab/CNVpytor.git
cd CNVpytor
pip install .

4. Download GPU version of guppy here (Registration required)

• Unpack the tar file (xxx is the version e.g. 4.2.2) tar -xf ont-guppy_xxx_linux<64 or aarch64>.tar.gz

5. Download singularity images for PEPPER-Margin-DeepVariant singularity pull docker://kishwars/pepper_deepvariant:r0.4.

6. Download ANNOVAR here

7. Download Rapid-CNS² Git repo git clone https://github.com/areebapatel/Rapid_CNS2.git

8. Edit rapid_cns_snake.yaml to specify paths

9. Run pipeline as

snakemake --use-conda --config FAST5_dir=<path to FAST5 files> outdir=<path to output directory for library> <output file eg. (sample)_cnvpytor_100k.png>

Requirements

• Reference genome hg19 and hg38 FASTA
• ANNOVAR database.

annotate_variation.pl -buildver hg19 -downdb -webfrom annovar refGene humandb/

annotate_variation.pl -buildver hg19 -downdb cytoBand humandb/

annotate_variation.pl -buildver hg19 -downdb -webfrom annovar exac03 humandb/

annotate_variation.pl -buildver hg19 -downdb -webfrom annovar avsnp147 humandb/

annotate_variation.pl -buildver hg19 -downdb -webfrom annovar dbnsfp30a humandb/

Citation

If you use this pipeline, please cite:

Patel, A., Dogan, H., Payne, A. et al. Rapid-CNS²: rapid comprehensive adaptive nanopore-sequencing of CNS tumors, a proof-of-concept study. Acta Neuropathol (2022). https://doi.org/10.1007/s00401-022-02415-6