nf-core/captnseq

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Introduction

nf-core/captnseq [provisional name] is a bioinformatics pipeline for bacterial and plasmid TIS/Tn-seq (transposon insertion sequencing) data. It takes paired-end short reads from a transposon-insertion library, maps them to a reference genome, calls the insertion sites, and uses the per-gene insertion density to classify every gene as essential, ambiguous or non-essential.

The pipeline is aimed at High-Throughput Transposon Mutagenesis (HTTM) experiments on large mutant collections. The current version covers the standard Tn-seq processing mode, from raw reads to gene essentiality. Genome-wide digenic interaction calling (synthetic-lethal and alleviating gene pairs) is the planned extension and is not part of the pipeline yet.

You provide a samplesheet of paired-end FASTQ files, a reference genome FASTA and a gene annotation in BED format. Everything else runs with defaults that are tuned for Tn5-style transposon libraries, and each step can be adjusted with the parameters below.

Pipeline summary

1. Raw read QC (FastQC)
2. Adapter trimming of both mates (Cutadapt), default adapter ACTGTCTCTTATACACATCT
3. Quality trimming and length filtering (fastp), with a sliding-window trim from the 3’ end. Reads that fail, and orphaned mates, are kept in separate folders
4. Alignment to the reference genome (BWA-MEM); the BWA index is built from --genome_fasta
5. Mapping-quality filtering, name-sorting, fixmate and coordinate-sorting (samtools). A sample with fewer than --min_mapped_reads mapped reads stops the run instead of producing silently poor results
6. Duplicate marking and removal (samtools markdup), plus alignment statistics (samtools flagstat)
7. Insertion-site calling from the deduplicated reads (bin/sam2sites.py): one record per insertion site, written as BED and as normalised or raw, stranded or unstranded bedGraph tracks.
8. Assignment of insertion sites to genes (bedtools intersect and bin/sites2genes.py). The informative part of each gene can be restricted, so that insertions near the start or end of a gene can be ignored
9. Essentiality calling (bin/tradis_essentiality_wscore_fixed.py, a Python port of the TRADIS essentiality script): genes are classified as essential, ambiguous or non-essential, with QC and changepoint plots for each sample
10. Run-level summaries: mapped reads, insertion-site counts and per-contig read counts for all samples
11. Aggregated QC report (MultiQC)

Usage

Input files

Samplesheet. Create a comma-separated file with one row per sample (samplesheet.csv):

sample,fastq_1,fastq_2
mutant_pool_1,pool1_R1.fastq.gz,pool1_R2.fastq.gz
mutant_pool_2,pool2_R1.fastq.gz,pool2_R2.fastq.gz

Reference genome. --genome_fasta is the FASTA file of the genome the library was built in. It can contain several contigs, for example a chromosome and plasmids, and reads are counted per contig.

Gene annotation. --genome_features is a tab-separated, headerless BED6 file with one line per gene or feature to score: chromosome, start, end, name, score, strand. The chromosome names must match the contig names in the FASTA file.

chr1    37      1816    geneA   900     -
chr1    2291    3482    geneB   900     +

Running the pipeline

nextflow run nf-core/captnseq \
   -profile docker \
   --input samplesheet.csv \
   --genome_fasta genome.fasta \
   --genome_features genes.bed \
   --outdir results

Replace docker with singularity, apptainer, conda or the profile of your institution. To try the pipeline on the small bundled dataset first, use -profile test,docker --outdir results.

Key parameters

The complete list is in the parameter documentation. For more details and further functionality, please refer to the usage documentation.

Pipeline output

The results directory contains one folder per stage. Every file name starts with the sample name.

In the essentiality tables, each row is a gene with its length, the number of insertion sites (ins_count), the number of reads (read_count) and the insertion index (ins_index, insertions per base). The classification uses the two thresholds reported in the same table, F_threshold and N_threshold.

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/captnseq was originally written by Simon Jeanneau.

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on the Slack #captnseq channel (you can join with this invite).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.