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1 # SeqSero2 v1.1.1 |
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2 Salmonella serotype prediction from genome sequencing data. |
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4 Online version: http://www.denglab.info/SeqSero2 |
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6 # Introduction |
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7 SeqSero2 is a pipeline for Salmonella serotype prediction from raw sequencing reads or genome assemblies |
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9 # Dependencies |
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10 SeqSero2 has three workflows: |
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11 |
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12 (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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14 Allele micro-assembly workflow depends on: |
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16 1. Python 3; |
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18 2. Biopython 1.73; |
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20 3. [Burrows-Wheeler Aligner v0.7.12](http://sourceforge.net/projects/bio-bwa/files/); |
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22 4. [Samtools v1.8](http://sourceforge.net/projects/samtools/files/samtools/); |
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24 5. [NCBI BLAST v2.2.28+](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download); |
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26 6. [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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28 7. [SPAdes v3.9.0](http://bioinf.spbau.ru/spades); |
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30 8. [Bedtools v2.17.0](http://bedtools.readthedocs.io/en/latest/); |
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32 9. [SalmID v0.11](https://github.com/hcdenbakker/SalmID). |
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33 |
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34 |
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35 (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. |
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37 Raw reads k-mer workflow (originally SeqSeroK) depends on: |
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39 1. Python 3; |
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40 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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41 |
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42 |
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43 (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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45 # Installation |
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46 ### Conda |
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47 To install the latest SeqSero2 Conda package (recommended): |
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48 ``` |
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49 conda install -c bioconda seqsero2=1.1.1 |
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50 ``` |
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51 ### Git |
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52 To install the SeqSero2 git repository locally: |
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53 ``` |
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54 git clone https://github.com/denglab/SeqSero2.git |
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55 cd SeqSero2 |
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56 python3 -m pip install --user . |
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57 ``` |
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58 ### Other options |
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59 Third party SeqSero2 installations (may not be the latest version of SeqSero2): \ |
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60 https://github.com/B-UMMI/docker-images/tree/master/seqsero2 \ |
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61 https://github.com/denglab/SeqSero2/issues/13 |
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63 |
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64 # Executing the code |
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65 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. |
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66 |
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67 Usage: SeqSero2_package.py |
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69 -m <string> (which workflow to apply, 'a'(raw reads allele micro-assembly), 'k'(raw reads and genome assembly k-mer), default=a) |
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70 |
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71 -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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73 -i <file> (/path/to/input/file) |
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75 -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) |
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77 -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) |
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79 -d <string> (output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number) |
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81 -c <flag> (if '-c' was flagged, SeqSero2 will only output serotype prediction without the directory containing log files) |
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83 -n <string> (optional, to specify a sample name in the report output) |
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85 -s <flag> (if '-s' was flagged, SeqSero2 will not output header in SeqSero_result.tsv) |
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86 |
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87 --check <flag> (use '--check' flag to check the required dependencies) |
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89 -v, --version (show program's version number and exit) |
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90 |
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92 # Examples |
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93 Allele mode: |
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94 |
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95 # Allele workflow ("-m a", default), for separated paired-end raw reads ("-t 2"), use 10 threads in mapping and assembly ("-p 10") |
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96 SeqSero2_package.py -p 10 -t 2 -i R1.fastq.gz R2.fastq.gz |
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98 K-mer mode: |
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99 |
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100 # Raw reads k-mer ("-m k"), for separated paired-end raw reads ("-t 2") |
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101 SeqSero2_package.py -m k -t 2 -i R1.fastq.gz R2.fastq.gz |
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102 |
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103 # Genome assembly k-mer ("-t 4", genome assemblies only predicted by the k-mer workflow, "-m k") |
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104 SeqSero2_package.py -m k -t 4 -i assembly.fasta |
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105 |
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106 # Output |
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107 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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108 |
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109 |
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110 # Citation |
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111 Zhang S, Den-Bakker HC, Li S, Dinsmore BA, Lane C, Lauer AC, Fields PI, Deng X. |
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112 SeqSero2: rapid and improved Salmonella serotype determination using whole genome sequencing data. |
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113 **Appl Environ Microbiology. 2019 Sep; 85(23):e01746-19.** [PMID: 31540993](https://aem.asm.org/content/early/2019/09/17/AEM.01746-19.long) |
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114 |
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115 Zhang S, Yin Y, Jones MB, Zhang Z, Deatherage Kaiser BL, Dinsmore BA, Fitzgerald C, Fields PI, Deng X. |
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116 Salmonella serotype determination utilizing high-throughput genome sequencing data. |
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117 **J Clin Microbiol. 2015 May;53(5):1685-92.** [PMID: 25762776](http://jcm.asm.org/content/early/2015/03/05/JCM.00323-15) |