jpayne@7: # SeqSero2 v1.0.0
jpayne@7: Salmonella serotype prediction from genome sequencing data
jpayne@1: 
jpayne@1: # Introduction 
jpayne@7: SeqSero2 is a pipeline for Salmonella serotype prediction from raw sequencing reads or genome assemblies
jpayne@1: 
jpayne@1: # Dependencies 
jpayne@7: SeqSero has three workflows:
jpayne@1: 
jpayne@7: (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). 
jpayne@1: 
jpayne@7: Allele micro-assembly workflow depends on:
jpayne@1: 
jpayne@7: 1. Python 3;
jpayne@7: 
jpayne@7: 2. [Burrows-Wheeler Aligner v0.7.12](http://sourceforge.net/projects/bio-bwa/files/);
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jpayne@7: 3. [Samtools v1.8](http://sourceforge.net/projects/samtools/files/samtools/);
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jpayne@7: 4. [NCBI BLAST v2.2.28+](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download);
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jpayne@7: 5. [SRA Toolkit v2.8.0](http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software);
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jpayne@7: 6. [SPAdes v3.9.0](http://bioinf.spbau.ru/spades);
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jpayne@7: 7. [Bedtools v2.17.0](http://bedtools.readthedocs.io/en/latest/);
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jpayne@7: 8. [SalmID v0.11](https://github.com/hcdenbakker/SalmID).
jpayne@7: 
jpayne@7: 
jpayne@7: (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. 
jpayne@7: 
jpayne@7: Raw reads k-mer workflow (originally SeqSeroK) depends on:
jpayne@1: 
jpayne@1: 1. Python 3;
jpayne@1: 2. [SRA Toolkit](http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software) (optional, just used to fastq-dump sra files);
jpayne@1: 
jpayne@1: 
jpayne@7: (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
jpayne@1: 
jpayne@1: 
jpayne@1: # Executing the code 
jpayne@1: 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.
jpayne@1: 
jpayne@1:     Usage: SeqSero2_package.py 
jpayne@1: 
jpayne@7:     -m <string> (which workflow to apply, 'a'(raw reads allele micro-assembly), 'k'(raw reads and genome assembly k-mer), default=a)
jpayne@1: 
jpayne@1:     -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 fasta, '6'for nanopore fastq)
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jpayne@1:     -i <file> (/path/to/input/file)
jpayne@1: 
jpayne@1:     -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) 
jpayne@1: 
jpayne@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)
jpayne@1:  
jpayne@1:     -d <string> (output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number)
jpayne@1: 	
jpayne@7: 	-c <flag> (if '-c' was flagged, SeqSero2 will only output serotype prediction without the directory containing log files)
jpayne@1: 	
jpayne@1: 
jpayne@1: # Examples
jpayne@7: Allele mode:
jpayne@7: 
jpayne@7:     # Allele workflow ("-m a", default), for separated paired-end raw reads ("-t 2"), use 10 threads in mapping and assembly ("-p 10")
jpayne@7:     SeqSero2_package.py -p 10 -t 2 -i R1.fastq.gz R2.fastq.gz
jpayne@7: 	
jpayne@1: K-mer mode:
jpayne@1: 
jpayne@7:     # Raw reads k-mer ("-m k"), for separated paired-end raw reads ("-t 2")
jpayne@7:     SeqSero2_package.py -m k -t 2 -i R1.fastq.gz R2.fastq.gz
jpayne@1: 
jpayne@7:     # Genome assembly k-mer ("-t 4", genome assemblies only predicted by the k-mer workflow, "-m k")
jpayne@7:     SeqSero2_package.py -m k -t 4 -i assembly.fasta
jpayne@1: 	
jpayne@1: # Output 
jpayne@1: 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).
jpayne@1: 
jpayne@1: 
jpayne@1: # Citation
jpayne@1: Zhang S, Yin Y, Jones MB, Zhang Z, Deatherage Kaiser BL, Dinsmore BA, Fitzgerald C, Fields PI, Deng X.  
jpayne@1: Salmonella serotype determination utilizing high-throughput genome sequencing data.  
jpayne@1: **J Clin Microbiol.** 2015 May;53(5):1685-92.[PMID:25762776](http://jcm.asm.org/content/early/2015/03/05/JCM.00323-15)