Practical-Analysis-of-Nanopore-Sequencing-Data-Using-Third-Generation-Sequencing

Introduction:

With the decreasing cost of third-generation sequencing (TGS), many researchers and enthusiasts are becoming interested in analyzing TGS data. This project aims to provide a comprehensive guide to TGS data analysis, allowing users to quickly get hands-on experience with the entire TGS workflow.

#!/bin/bash

Step 1: Quality Control (Option 1: nanoQC)

mkdir nanoQC nanoQC -o nanoQC -f input_data_path.fastq.gz NanoStat --fastq input_data_path.sra.fastq.gz --outdir statreports

Step 2: Filtering (Option 1: Nanofilt)

gunzip -c input_data_path.fastq.gz | NanoFilt -q 7 -l 1000 --headcrop 50 --tailcrop 50 | gzip > clean.NanoFilt.fastq.gz

Step 3: Alignment (Option 1: minimap2)

minimap2 -d ref.mmi ref.fa minimap2 -a ref.mmi reads.fq > alignment.sam

Step 4: Sorting

samtools sort -@ 8 -o bam -o s0137.sorted.bam s1037.sam samtools index s0137.sorted.bam samtools faidx ref.fa

Step 5: Assembly (Option 1: Canu)

canu -p output_prefix -d output_dir genomeSize=5g maxThreads=96 -nanopore-raw input_data_path > canu.log

Step 6: Assembly Quality Assessment (Quast)

quast.py -r ref.fa canu.fa miniasm.fa wtdbg2.cns.fa smartdenovo.fa -o quast

Step 7: Assembly Result Polishing (Option 1: Medaka)

medaka_consensus -i raw_reads.fastq.gz -d assembly.fasta -o medaka_result -m r941_min_high_g360 -v medaka.vcf -t 24 > medaka.log

Step 8: Assembly Result Polishing (Option 2: Pilon)

bwa index pilon.fasta bwa mem pilon.fasta input_data_path.fastq.gz | samtools view -Sb - > input_data_path.bam samtools sort -o input_data_path.sorted.bam input_data_path.bam samtools index input_data_path.sorted.bam java -Xmx32G -jar pilon.jar --genome pilon.fasta --bam input_data_path.sorted.bam --output pilon_polished.fasta --threads 24 --fix all --verbose