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Guppy similarity

Descriptions

This directory archives the pipeline for sequence identity and QV score similarity between Guppy versions.

We use the downsampled dataset HG002 sequenced with R9.4.1 flowcell (https://labs.epi2me.io/gm24385_2021.05) to evaluate the sequence identity and QV score similarity.

Execution environments

  1. To recreate the Python virtual environment for sequence identity, run the following command.
conda env create -f mappy.yaml;
  1. To recreate the Python virtual environment for QV similarity analysis, just use the longbow environment.
conda env create -f ont-longbow.yaml;

Data

Please download the HG002_R9_guppy_similarity.tar.gz file we shared through ScienceDB link to the ../data directory and decompress it before running the following pipelines. The files contained the basecalled FASTQ of HG002 data.

The data is shared through https://www.scidb.cn/en/detail?dataSetId=b9eca82475a64772a67ec9b7dac2beb3, specifically the HG002_R9_guppy_similarity.tar.gz file

You can follow the instruction in here to download the data we shared.

mkdir -p ../data;
cd ../data;

## Download the FASTQ file through ScienceDB

## decompression
tar -zxvf HG002_R9_guppy_similarity.tar.gz;

Pipelines

  1. Evaluate the sequence identity between different Guppy versions
bash HG002_R9_seq_identity.sh
  1. Evaluate the Bhattacharyya coefficient between different Guppy versions
bash HG002_R9_QV_sim.sh > ../results/HG002_R9_qv_sim.txt

Repeat our results

To repeat our results, please install the aforementioned conda environment first and run

bash ./run_all.sh;