SeqSero2

Salmonella serotype prediction from genome sequencing data.

Online version: http://www.denglab.info/SeqSero2

Introduction

SeqSero2 is a pipeline for Salmonella serotype prediction from raw sequencing reads or genome assemblies

Dependencies

SeqSero2 has three workflows:

(A) Allele micro-assembly (default). This workflow takes raw reads as input and performs targeted assembly of serotype determinant alleles. Assembled alleles are used to predict serotype and flag potential inter-serotype contamination in sequencing data (i.e., presence of reads from multiple serotypes due to, for example, cross or carryover contamination during sequencing).

Allele micro-assembly workflow depends on:

1. Python 3;

2. Biopython 1.73;

3. Burrows-Wheeler Aligner v0.7.12;

4. Samtools v1.8;

5. NCBI BLAST v2.2.28+;

6. SRA Toolkit v2.8.0;

7. SPAdes v3.15.5;

8. Bedtools v2.17.0;

9. SalmID v0.11.

(B) Raw reads k-mer. This workflow takes raw reads as input and performs rapid serotype prediction based on unique k-mers of serotype determinants.

Raw reads k-mer workflow (originally SeqSeroK) depends on:

1. Python 3;
2. SRA Toolkit (optional, just used to fastq-dump sra files);

(C) Genome assembly k-mer. This workflow takes genome assemblies as input and the rest of the workflow largely overlaps with the raw reads k-mer workflow

Installation

Conda

To install the latest SeqSero2 Conda package (recommended):

conda install -c bioconda seqsero2=1.3.1

Git

To install the SeqSero2 git repository locally:

git clone https://github.com/denglab/SeqSero2.git
cd SeqSero2
python3 -m pip install --user .

Other options

Third party SeqSero2 installations (may not be the latest version of SeqSero2):
https://github.com/B-UMMI/docker-images/tree/master/seqsero2
#13

Executing the code

Make sure all SeqSero2 and its dependency executables are added to your path (e.g. to ~/.bashrc). Then type SeqSero2_package.py to get detailed instructions.

Usage: SeqSero2_package.py 

-m <string> (which workflow to apply, 'a'(raw reads allele micro-assembly), 'k'(raw reads and genome assembly k-mer), default=a)

-t <string> (input data type, '1' for interleaved paired-end reads, '2' for separated paired-end reads, '3' for single reads, '4' for genome assembly, '5' for nanopore reads (fasta/fastq))

-i <file> (/path/to/input/file)

-p <int> (number of threads for allele mode, if p >4, only 4 threads will be used for assembly since the amount of extracted reads is small, default=1) 

-b <string> (algorithms for bwa mapping for allele mode; 'mem' for mem, 'sam' for samse/sampe; default=mem; optional; for now we only optimized for default "mem" mode)

-d <string> (output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number)

-c <flag> (if '-c' was flagged, SeqSero2 will only output serotype prediction without the directory containing log files)

-n <string> (optional, to specify a sample name in the report output)

-s <flag> (if '-s' was flagged, SeqSero2 will not output header in SeqSero_result.tsv)
	    
--check <flag> (use '--check' flag to check the required dependencies)

-v, --version (show program's version number and exit)

Examples

Allele mode:

# Allele workflow ("-m a", default), for separated paired-end raw reads ("-t 2"), use 10 threads in mapping and assembly ("-p 10")
SeqSero2_package.py -p 10 -t 2 -i R1.fastq.gz R2.fastq.gz

K-mer mode:

# Raw reads k-mer ("-m k"), for separated paired-end raw reads ("-t 2")
SeqSero2_package.py -m k -t 2 -i R1.fastq.gz R2.fastq.gz

# Genome assembly k-mer ("-t 4", genome assemblies only predicted by the k-mer workflow, "-m k")
SeqSero2_package.py -m k -t 4 -i assembly.fasta

Output

Upon executing the command, a directory named 'SeqSero_result_Time_your_run' will be created. Your result will be stored in 'SeqSero_result.txt' in that directory. And the assembled alleles can also be found in the directory if using "-m a" (allele mode).

Citation

Zhang S, Den-Bakker HC, Li S, Dinsmore BA, Lane C, Lauer AC, Fields PI, Deng X. SeqSero2: rapid and improved Salmonella serotype determination using whole genome sequencing data. Appl Environ Microbiology. 2019 Sep; 85(23):e01746-19. PMID: 31540993

Zhang S, Yin Y, Jones MB, Zhang Z, Deatherage Kaiser BL, Dinsmore BA, Fitzgerald C, Fields PI, Deng X.
Salmonella serotype determination utilizing high-throughput genome sequencing data.
J Clin Microbiol. 2015 May;53(5):1685-92. PMID: 25762776