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1 # SeqSero2 alpha-test version 1 # SeqSero2 v1.0.0
2 Salmonella serotyping from genome sequencing data 2 Salmonella serotype prediction from genome sequencing data
3
4 # Introduction
5 SeqSero2 is a pipeline for Salmonella serotype prediction from raw sequencing reads or genome assemblies
6
7 # Dependencies
8 SeqSero has three workflows:
9
10 (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).
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12 Allele micro-assembly workflow depends on:
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14 1. Python 3;
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16 2. [Burrows-Wheeler Aligner v0.7.12](http://sourceforge.net/projects/bio-bwa/files/);
17
18 3. [Samtools v1.8](http://sourceforge.net/projects/samtools/files/samtools/);
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20 4. [NCBI BLAST v2.2.28+](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download);
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22 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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24 6. [SPAdes v3.9.0](http://bioinf.spbau.ru/spades);
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26 7. [Bedtools v2.17.0](http://bedtools.readthedocs.io/en/latest/);
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28 8. [SalmID v0.11](https://github.com/hcdenbakker/SalmID).
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4 30
5 # Introduction 31 (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.
6 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.
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8 33 Raw reads k-mer workflow (originally SeqSeroK) depends on:
9 # Dependencies
10 SeqSero has two modes:
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12
13 (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).
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15 K-mer mode is a independant pipeline, it only requires:
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17 1. Python 3; 35 1. Python 3;
18 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); 36 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);
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20 38
21 (B) allele based mode (if users want to extract serotype determinant alleles), which applies a hybrid approach of reads-mapping and micro-assembly. 39 (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
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23 Allele mode depends on:
24
25 1. Python 3;
26
27 2. [Burrows-Wheeler Aligner](http://sourceforge.net/projects/bio-bwa/files/);
28
29 3. [Samtools](http://sourceforge.net/projects/samtools/files/samtools/);
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31 4. [NCBI BLAST](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download);
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33 5. [SRA Toolkit](http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software);
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35 6. [SPAdes](http://bioinf.spbau.ru/spades);
36
37 7. [Bedtools](http://bedtools.readthedocs.io/en/latest/);
38
39 8. [SalmID](https://github.com/hcdenbakker/SalmID).
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41 41
42 # Executing the code 42 # Executing the code
43 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. 43 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.
44 44
45 Usage: SeqSero2_package.py 45 Usage: SeqSero2_package.py
46 46
47 -m <string> (which mode to apply, 'k'(kmer mode), 'a'(allele mode), default=k) 47 -m <string> (which workflow to apply, 'a'(raw reads allele micro-assembly), 'k'(raw reads and genome assembly k-mer), default=a)
48 48
49 -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) 49 -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)
50 50
51 -i <file> (/path/to/input/file) 51 -i <file> (/path/to/input/file)
52 52
54 54
55 -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) 55 -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)
56 56
57 -d <string> (output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number) 57 -d <string> (output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number)
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59 -c <flag> (if '-c' was flagged, SeqSero2 will use clean mode and only output serotyping prediction without the directory containing log files) 59 -c <flag> (if '-c' was flagged, SeqSero2 will only output serotype prediction without the directory containing log files)
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61 61
62 # Examples 62 # Examples
63 Allele mode:
64
65 # Allele workflow ("-m a", default), for separated paired-end raw reads ("-t 2"), use 10 threads in mapping and assembly ("-p 10")
66 SeqSero2_package.py -p 10 -t 2 -i R1.fastq.gz R2.fastq.gz
67
63 K-mer mode: 68 K-mer mode:
64 69
65 # K-mer (default), for separated paired-end raw reads ("-t 2") 70 # Raw reads k-mer ("-m k"), for separated paired-end raw reads ("-t 2")
66 SeqSero2_package.py -t 2 -i R1.fastq.gz R2.fastq.gz 71 SeqSero2_package.py -m k -t 2 -i R1.fastq.gz R2.fastq.gz
67
68 # K-mer (default), for assemblies ("-t 4", assembly only predcited by K-mer mode)
69 SeqSero2_package.py -t 4 -i assembly.fasta
70 72
71 Allele mode: 73 # Genome assembly k-mer ("-t 4", genome assemblies only predicted by the k-mer workflow, "-m k")
72 74 SeqSero2_package.py -m k -t 4 -i assembly.fasta
73 # Allele mode ("-m a"), for separated paired-end raw reads ("-t 2"), use 10 threads in mapping and assembly ("-p 10")
74 SeqSero2_package.py -m a -p 10 -t 2 -i R1.fastq.gz R2.fastq.gz
75
76 75
77 # Output 76 # Output
78 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). 77 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).
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