Pipeline for BarSeq analysis

Pipeline for analysis of Tn-BarSeq data. The pipeline is based on the scripts of Morgan Price's Feba repository. The user is referred to the Feba repository or the original Tn-BarSeq publications for further information.

Usage

Prerequisites

• Linux environment with bash, perl installed
• a DNA alignment software, the default here is blat. It can be downloaded here and should placed in feba/bin/
• Perl scripts from Morgan Price's Feba repository, A. Arkin lab, Berkeley (see feba/bin/)
• Gzipped fastq sequencing data as obtained from Illumina runs (see data/example/fastq)
• a TnSeq 'pool file' with barcode-to-genome mappings
• a 'metadata' file describing the input files

Retrieving data from Illumina basespace via command line (optional)

Data in form of *.fastq files can be manually downloaded from the basespace website on MacOS or Windows. For Linux systems, only the command line option is available via Illumina's basespace client bs-cp. Files are in Illumina's proprietary format. Execute the following line in a terminal and replace <your-run-ID> with the number you will find in the URL of your browser. For example, log in to basespace, navigate to runs, select a sequencing run and copy the ID you find in the URL: https://basespace.illumina.com/run/200872678/details.

bs-cp -v https://basespace.illumina.com/Run/<your-run-ID> /your/target/directory/

The data must then be converted to *.fastq (plain text) files using Illumina's bcl2fastq tool. It is recommended to run it with option --no-lane-splitting in order to obtain one file per sample, instead of several files subdivided by lane. If it complains about indices being too similar to demultiplex, the option --barcode-mismatches 0 can be added.

cd /your/target/directory/
bcl2fastq --no-lane-splitting

The gzipped *.fastq.gz files will be stored in ./Data/Intensities/BaseCalls/. To merge replicates of the same sample into a new *.fastq.gz file, run the following script. The script merges files matching a certain pattern into a single new file. Input and output folder can be specified with optional parameters (the default is current directory ./). New file names are truncated to the part preceding the variable pattern (all characters trailing the pattern are ignored).

• input_dir - input directory
• output_dir - - output directory
• file_ext - file extension of the target files (default: fastq.gz)
• pattern - all files matching this pattern will be merged (default: _L00[1-4]_)

source/merge_fastq_files.sh --input_dir data/fastq/ --output_dir data/fastq/ --pattern _R.

Input files

Before starting, a pool file, a metadata file, and gzipped fastq files must be prepared.

Pool file

The pool file describes TnSeq mappings, and is obtained as output from the TnSeq pipeline. It has the following structure. Note that the column for gene IDs is currently named old_locus_tag, this can be customized if needed (see section "Calculate fitness"). The default location for pool files is ref/.

barcode rcbarcode  nTot     n scaffold strand    pos   begin     end gene_strand desc  old_locus_tag new_locu…
CAGAAG… CCCCGCCC…     1     1 NC_0083… +      1.02e6 1014705 1015328 -           gene  H16_B0896     H16_RS23…  
CTGTTG… ACCAACCC…     1     1 NC_0083… +      3.12e6 3118049 3119266 +           gene  H16_A2889     H16_RS14…  
AGCCGC… GTCCCCCT…     1     1 NC_0083… -      3.44e6 3442926 3443798 -           gene  H16_A3183     H16_RS15…  
CGTCAT… CCACCGCT…     1     1 NC_0083… +      3.46e6 3464096 3464620 -           gene  H16_A3206     H16_RS34…  
CAGCAG… CGCCGAAC…     2     2 NC_0083… -      2.50e6 2495842 2499936 -           gene  H16_B2203     H16_RS29…  
...

Metadata file

The tab-separated metadata.tsv file contains sample descriptions for the fastq files. The file_names must match the names of the supplied files (for our example data, the files in /data/example/fastq/). The group and reference_group columns specify which comparisons between sample groups should be made (e.g. group 2 vs reference 1).

file_name                  ID condition replicate date        time group reference_group
01_cond1gen0_1.fastq.gz     1 generic           1 2020-12-05     0     1               1
02_cond1gen0_2.fastq.gz     2 generic           2 2020-12-05     0     1               1
03_cond1gen8_1.fastq.gz     3 generic           1 2020-12-08     8     2               1
04_cond1gen8_2.fastq.gz     4 generic           2 2020-12-08     8     2               1

Running the analysis

Step 1: Extract per-sample barcode counts

This script is a wrapper for the MultiCodes.pl script from FEBA. It identifies reads containing barcodes and summarizes barcode counts in .codes and .counts files. It takes the following optional arguments:

• input_dir - path to fastq files (default ./)
• output_dir (default ./)
• pattern - the file name pattern to look for (default .fastq.gz)

To run the script with the example data, execute the following line in the terminal:

source/run_MultiCodes.sh --input_dir data/example/fastq --output_dir data/example/counts

Step 2: Combine BarSeq data and genome mappings

This script is a wrapper for the combineBarSeq.pl script from FEBA. It maps barcodes to genomic positions and summarizes results in a single output table (.poolcount) and a short report (.colsum). It takes the following optional arguments:

• input_dir - path to .counts and .codes files (default ./)
• output_dir (default ./)
• poolfile - path to the pool file (default ./ref/poolfile.tsv)

source/run_combineBarSeq.sh --input_dir data/example/counts --output_dir data/example/results

Step 3: Calculate gene fitness

This script calculates gene fitness using the method described in Wetmore 2015. The script takes the following arguments. All files are saved to the output_dir folder.

• result - path to the .poolcount file from previous step (default: ./data/example/results/result.poolcount)
• poolfile - path to the pool file (default ./ref/poolfile.tsv)
• gene_id - name of the column containing gene IDs in the pool file
• metadata - path to the metadata file (default: ./data/example/fastq/metadata.tsv)
• output_dir (default ./)
• deseq - use Deseq2 instead of method from Wetmore et. al. (0/1, default: 0)

source/calculate_gene_fitness.sh --result data/example/results/result.poolcount \
  --poolfile ref/poolfile.tsv \
  --gene_id old_locus_tag \
  --metadata data/example/fastq/metadata.tsv \
  --output_dir data/example/results/

Expected output are result tables in memory-efficient .Rdata format and summary plots in .png and .pdf format. The two tables are fitness.Rdata for all strains (barcodes), including data per gene (columns strains_per_gene, norm_gene_fitness, t, significant), and fitness_gene.Rdata for gene fitness data only (columns counts and n0 are summed over all strains [barcodes] for each gene, column log2FC is log2(Counts/n0)). Note: The output when using --deseq 1 is similar to the default but slightly different. DESeq2 summarizes abundance of replicates to mean log2FC, fitness, and p-values per condition. Columns replicate and n0 are not contained in the output and strain_fitness/norm_gene_fitness is calculated over all time points.

The columns have the following contents:

Column	Description
barcode	Barcode sequence (not in fitness_gene table)
locusId	Locus ID (gene name)
scaffold	Name of DNA molecule
date	Sample date batch; Variable connecting samples to t0 samples
time	Timepoint
ID	Sample ID
condition	Growth condition
replicate	Replicate ID
counts	Read count for strain (barcode) in sample (summed per gene in fitness_gene table)
n0	Read count in corresponding t0 samples
strains_per_gene	Number of strains (barcodes) for the current locusId
strain_fitness	Strain (barcode) fitness (not in fitness_gene table); f_s on p.12 in Wetmore 2015
norm_gene_fitness	Normalized gene fitness; (iii) on p.13 in Wetmore 2015
t	t-like test statistic; calculated on p.13 in Wetmore 2015
significant	Significant gene if |t| > 4; stated on p.3 in Wetmore 2015
log2FC	log2(Counts/n0), only in fitness_gene table

Example of graphical summary

Reads per gene (sum of all barcodes)	Barcodes per gene

Reads per barcode (by sample)	PCA of samples

Authors

Johannes Asplund-Samuelsson (johannes.aspsam@gmail.com)

Qi Chen, KTH (qiche@kth.se)

Michael Jahn, KTH (michael.jahn@scilifelab.se)