README.md

Analysis of RB-TNSeq Data

Allows mapping of transposon library, as well as barcode counting

Installation:

• Create and activate conda environment

git clone ...
mamba env create -f tnseq_environment.yaml
conda activate tnseq2
tnseq2 --help

Run tnseq2 maplib on test data

1. Display help information

tnseq2 maplib --help

2. Run maplib Required inputs: library fastq, genome fasta Optional inputs: annotation gff For test dataset have to set l=0, because only a few reads are analyzed Output files: maplib_demo.barcode_map.annotated: final library map with annoations maplib_demo.barcode_map: final library map without annotations maplib_demo.blastn: blast output for each barcode: host sequence maplib_demo.fasta: fasta files of barcodes and host sequences (>barcode\nhostsequence) maplib_demo.output.bed: bedtools intersection of gff and barcode locations maplib_demo.temp.bed: need to clean this up after completion tnseq2_mapping.log: log file, will only have errors in it

tnseq2 maplib -f tests/test_files/library_13_1_1.fq -r tests/test_files/library_13_1_2.fq -a tests/test_files/ref/Salmonella_genome+plasmids.gff -g tests/test_files/ref/Salmonella_genome_FQ312003.1_SL1344.fasta --name maplib_demo -o tests/test_data -l 0

3. Run test suit

pytest tests/unit/test_mapping.py -v

Run tnseq2 count on test data

1. Display

tnseq2 count --help

2. Run on test data Required Inputs: Optional Inputs: mapping file Output Files: count_demo_counts_mapped: barcodes and counts, for barcodes found in the mapping file count_demo_counts_unmapped: barcodes and counts for barcodes not found in the mapping file If not mapping file is provided, all barcode and counts will be in the count_demo_counts_mapped

tnseq2 count -f tests/test_files/dnaid2023_12_test.fasta -n count_demo -o tests/test_data -m tests/test_files/ref/library_13_1.barcode_map.annotated.csv

tnseq2 count -f tests/test_files/L -n WISH_count_demo -o tests/test_data -tn GGAGGTTCACAATGTGGGAGGTCA:40:0:after

tnseq2 merge

Input: directory with counts for different samples Output: 1 csv files with with all the sample counts to be used for the analysis

tnseq2 analyze

Input: sample counts csv, expreimental design table, control tags Output: log2 FC for each gene according to experimental design, z-scores/pval relative to control tags.

Steps:

0. (optional) Demultiplexing
1. Mapping
2. Barcode Counting
3. Analysis

Installation

conda env create -f tnseq_environment.yaml
# mamba env create -f tnseq_environment.yaml
conda activate tnseq2
pip install -e . 

Quick Start

Demultiplex

Map

Count

What you need:

Analysis

UNDER CONSTRUCTION