This repository contains 1 shell script and 6 Python scripts designed to extract statistics from raw nanopore adaptive sampling data.
Script Usage
python target_nontarget_statistics.py [all_adaptive.fastq] [adaptive-target.sam] [adaptive-nontarget.sam]
Input:
Raw sequence file, SAM alignment files for target/non-target sequences.
Output:
Total sequences/bases, uniquely matched sequences/bases to target/non-target, and dual-matched sequences/bases.
python match_read_number_with_time.py [sequencing_summary.txt] [adaptive-target.sam] [control-target.sam]
Input:
Nanopore sequencing summary file, SAM alignment files for adaptive/control groups.
Output:
A 5-column table with sequencing duration, sequence counts, and base counts for both groups.
python unblock_reads_matched_number.py [all_adaptive.fastq] [sequencing_summary.txt] [adaptive-target.sam]
Input:
Raw sequence file, nanopore sequencing summary file, SAM alignment file.
Output:
Statistics (counts/average/median read lengths) for accepted/rejected sequences matching/non-matching targets.
python read_category_statistics.py [all_adaptive.fastq] [sequencing_summary.txt]
Input:
Raw sequence file, nanopore sequencing summary file.
Output:
Table categorizing sequences (columns: counts/bases, average/median/N50 lengths).
python zymogut_species_read_with_time.py [all_adaptive.fastq] [adaptive-target.sam] [adaptive-nontarget.sam] [sequencing_summary.txt]
Input:
Raw sequence file, SAM alignment files for target/non-target sequences, nanopore sequencing summary file.
Output:
Time-series table of species-specific sequence counts.
python zymogut_species_base_with_time.py [all_adaptive.fastq] [adaptive-target.sam] [adaptive-nontarget.sam] [sequencing_summary.txt]
Input:
Raw sequence file, SAM alignment files for target/non-target sequences, nanopore sequencing summary file.
Output:
Time-series table of species-specific base counts.
sh ReadBouncer_running.sh
Monitors and auto-restarts ReadBouncer every 30 seconds if inactive.