New upstream version 1.0.2

include LICENSE

include README.md

include MANIFEST.in

include version.py

include setup.py

include seqsero2_db/antigens.pickle

include seqsero2_db/H_and_O_and_specific_genes.fasta

include seqsero2_db/invA_mers_dict

include seqsero2_db/special.pickle

include bin/deinterleave_fastq.sh

# SeqSero2 alpha-test version

Salmonella serotyping from genome sequencing data

# SeqSero2 v1.0.1

Salmonella serotype prediction from genome sequencing data.

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

# Introduction 

SeqSero2 is a pipeline for Salmonella serotype determination from raw sequencing reads or genome assemblies. This is a alpha test version. A web app will be available soon.

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

# Dependencies 

SeqSero has two modes:

SeqSero has three workflows:

(A) k-mer based mode (default), which applies unique k-mers of serotype determinant alleles to determine Salmonella serotypes in a fast speed. Special thanks to Dr. Hendrik Den Bakker for his significant contribution to this mode, details can be found in [SeqSeroK](https://github.com/hcdenbakker/SeqSeroK) and [SalmID](https://github.com/hcdenbakker/SalmID).

(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). 

K-mer mode is a independant pipeline, it only requires:

Allele micro-assembly workflow depends on:

1. Python 3;

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);

2. [Burrows-Wheeler Aligner v0.7.12](http://sourceforge.net/projects/bio-bwa/files/);

(B) allele based mode (if users want to extract serotype determinant alleles), which applies a hybrid approach of reads-mapping and micro-assembly.

3. [Samtools v1.8](http://sourceforge.net/projects/samtools/files/samtools/);

Allele mode depends on:

4. [NCBI BLAST v2.2.28+](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download);

1. Python 3; 

5. [SRA Toolkit v2.8.0](http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software);

2. [Burrows-Wheeler Aligner](http://sourceforge.net/projects/bio-bwa/files/); 

6. [SPAdes v3.9.0](http://bioinf.spbau.ru/spades);

3. [Samtools](http://sourceforge.net/projects/samtools/files/samtools/);

7. [Bedtools v2.17.0](http://bedtools.readthedocs.io/en/latest/);

4. [NCBI BLAST](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download);

8. [SalmID v0.11](https://github.com/hcdenbakker/SalmID).

5. [SRA Toolkit](http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software);

6. [SPAdes](http://bioinf.spbau.ru/spades);

(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](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);

7. [Bedtools](http://bedtools.readthedocs.io/en/latest/);

8. [SalmID](https://github.com/hcdenbakker/SalmID).

(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

# Executing the code 

    Usage: SeqSero2_package.py 

    -m <string> (which mode to apply, 'k'(kmer mode), 'a'(allele mode), default=k)

    -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 fasta, '6'for nanopore fastq)

    -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 use clean mode and only output serotyping prediction without the directory containing log files)

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

    --check <flag> (use '--check' flag to check the required dependencies)

# Examples

K-mer mode:

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

    # K-mer (default), for separated paired-end raw reads ("-t 2")

	SeqSero2_package.py -t 2 -i R1.fastq.gz R2.fastq.gz

	# K-mer (default), for assemblies ("-t 4", assembly only predcited by K-mer mode)

	SeqSero2_package.py -t 4 -i assembly.fasta

# Examples

Allele mode:

    # Allele mode ("-m a"), for separated paired-end raw reads ("-t 2"), use 10 threads in mapping and assembly ("-p 10")

	SeqSero2_package.py -m a -p 10 -t 2 -i R1.fastq.gz R2.fastq.gz

    # 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).