PathogenTrack is an unsupervised computational software that uses unmapped single-cell RNAseq reads to characterize intracellular pathogens at the single-cell level. It is a python-based script that can be used to identify and quantify intracellular pathogenic viruses and bacteria reads at the single-cell level. PathogenTrack has been tested on various scRNA-seq datasets derived from simulated and real datasets and performed robustly. The detailes are described in our paper Decoding Intracellular Pathogens of scRNA-seq experiments with PathogenTrack and SCKIT.

PathogenTrack has been tested on Linux platform with CentOS 7 operation system. The RAM is 120 GB, with 40 computational threads.

1 . Installing Miniconda on Linux Platform. For details, please refer to Miniconda Installation.

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

2 . Installing PathogenTrack.

conda env create -f environment.yml

Users can install the dependencies manually. The dependencies and test versions are listed below.

Download the Human GRCh38 genome and genome annotation file, and then decompress them:

wget ftp:https://ftp.ensembl.org/pub/release-101/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz
gzip -d Homo_sapiens.GRCh38.dna.toplevel.fa.gz
wget ftp:https://ftp.ensembl.org/pub/release-101/gtf/homo_sapiens/Homo_sapiens.GRCh38.101.gtf.gz
gzip -d Homo_sapiens.GRCh38.101.gtf.gz

Build STAR Index with the following command:

STAR --runThreadN 16 --runMode genomeGenerate --genomeDir ./ \
     --genomeFastaFiles ./Homo_sapiens.GRCh38.dna.toplevel.fa \
     --sjdbGTFfile ./Homo_sapiens.GRCh38.101.gtf \
     --sjdbOverhang 100

wget ftp:https://ftp.ccb.jhu.edu/pub/data/kraken2_dbs/minikraken_8GB_202003.tgz
tar zxf minikraken_8GB_202003.tgz

Before running this tutorial, you should run cellranger or alevin to get the single cells' gene expression matrix. Here, we take the simulated 10X sequencing data as an example:

First, we use cellranger to get scRNA-seq expression matrix and valid barcodes:

cellranger count --id cellranger_out --transcriptom /path/to/cellranger_database/

Then we run PathogenTrack to identify and quantify pathogen expression at the single-cell level:

conda activate PathogenTrack
python PathogenTrack.py count --project_id PathogenTrack_out --pattern CCCCCCCCCCCCCCCCNNNNNNNNNN \
                              --min_reads 10 --confidence 0.11 --star_index ~/database/STAR_index/ \
                              --kraken_db ~/database/minikraken_8GB_20200312/ --barcode barcodes.tsv \
                              --read1 simulation_S1_L001_R1_001.fastq.gz \
                              --read2 simulation_S1_L001_R2_001.fastq.gz 

IMPORTANT: The Read 1 in the example is made up of 16 bp CB and 10 bp UMI, so the --pattern is CCCCCCCCCCCCCCCCNNNNNNNNNN (16C and 10N). Users must adjust the pattern with their own Read 1 accordingly.

Note: It may take 4-6 hours to complete one sample, and it depends on the performance of computational resources and the size of the raw single-cell data.

For questions and suggestions about the pipeline or the code, please contact [email protected] and [email protected]. We will try our best to provide support, address new issues, and keep improving this software.