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author cstrittmatter
date Mon, 27 Apr 2020 01:11:53 -0400
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1 Metadata-Version: 1.1
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2 Name: SeqSero2
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3 Version: 1.1.0
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4 Summary: Salmonella serotyping
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5 Home-page: https://github.com/denglab/SeqSero2/
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6 Author: Shaokang Zhang, Hendrik C Den-Bakker and Xiangyu Deng
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7 Author-email: zskzsk@uga.edu, Hendrik.DenBakker@uga.edu, xdeng@uga.edu
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8 License: GPLv2
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9 Description: # SeqSero2 v1.1.0
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10 Salmonella serotype prediction from genome sequencing data.
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11
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12 Online version: http://www.denglab.info/SeqSero2
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13
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14 # Introduction
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15 SeqSero2 is a pipeline for Salmonella serotype prediction from raw sequencing reads or genome assemblies
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16
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17 # Dependencies
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18 SeqSero2 has three workflows:
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19
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20 (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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21
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22 Allele micro-assembly workflow depends on:
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23
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24 1. Python 3;
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25
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26 2. Biopython 1.73;
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28 3. [Burrows-Wheeler Aligner v0.7.12](http://sourceforge.net/projects/bio-bwa/files/);
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29
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30 4. [Samtools v1.8](http://sourceforge.net/projects/samtools/files/samtools/);
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31
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32 5. [NCBI BLAST v2.2.28+](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download);
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33
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34 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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35
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36 7. [SPAdes v3.9.0](http://bioinf.spbau.ru/spades);
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37
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38 8. [Bedtools v2.17.0](http://bedtools.readthedocs.io/en/latest/);
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39
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40 9. [SalmID v0.11](https://github.com/hcdenbakker/SalmID).
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42
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43 (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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44
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45 Raw reads k-mer workflow (originally SeqSeroK) depends on:
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46
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47 1. Python 3;
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48 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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51 (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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52
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53 # Installation
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54 ### Conda
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55 To install the latest SeqSero2 Conda package (recommended):
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56 ```
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57 conda install -c bioconda seqsero2=1.1.0
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58 ```
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59 ### Git
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60 To install the SeqSero2 git repository locally:
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61 ```
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62 git clone https://github.com/denglab/SeqSero2.git
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63 cd SeqSero2
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64 python3 -m pip install --user .
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65 ```
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66 ### Other options
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67 Third party SeqSero2 installations (may not be the latest version of SeqSero2): \
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68 https://github.com/B-UMMI/docker-images/tree/master/seqsero2 \
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69 https://github.com/denglab/SeqSero2/issues/13
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70
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71
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72 # Executing the code
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73 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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74
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75 Usage: SeqSero2_package.py
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76
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77 -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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78
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79 -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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80
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81 -i <file> (/path/to/input/file)
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82
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83 -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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84
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85 -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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86
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87 -d <string> (output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number)
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88
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89 -c <flag> (if '-c' was flagged, SeqSero2 will only output serotype prediction without the directory containing log files)
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90
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91 -n <string> (optional, to specify a sample name in the report output)
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92
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93 -s <flag> (if '-s' was flagged, SeqSero2 will not output header in SeqSero_result.tsv)
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94
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95 --check <flag> (use '--check' flag to check the required dependencies)
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96
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97 -v, --version (show program's version number and exit)
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98
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99
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100 # Examples
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101 Allele mode:
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102
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103 # 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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104 SeqSero2_package.py -p 10 -t 2 -i R1.fastq.gz R2.fastq.gz
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105
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106 K-mer mode:
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107
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108 # Raw reads k-mer ("-m k"), for separated paired-end raw reads ("-t 2")
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109 SeqSero2_package.py -m k -t 2 -i R1.fastq.gz R2.fastq.gz
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110
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111 # Genome assembly k-mer ("-t 4", genome assemblies only predicted by the k-mer workflow, "-m k")
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112 SeqSero2_package.py -m k -t 4 -i assembly.fasta
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113
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114 # Output
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115 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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116
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117
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118 # Citation
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119 Zhang S, Den-Bakker HC, Li S, Dinsmore BA, Lane C, Lauer AC, Fields PI, Deng X.
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120 SeqSero2: rapid and improved Salmonella serotype determination using whole genome sequencing data.
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121 **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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122
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123 Zhang S, Yin Y, Jones MB, Zhang Z, Deatherage Kaiser BL, Dinsmore BA, Fitzgerald C, Fields PI, Deng X.
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124 Salmonella serotype determination utilizing high-throughput genome sequencing data.
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125 **J Clin Microbiol. 2015 May;53(5):1685-92.** [PMID: 25762776](http://jcm.asm.org/content/early/2015/03/05/JCM.00323-15)
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126
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127 Keywords: Salmonella serotyping bioinformatics WGS
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128 Platform: UNKNOWN
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129 Classifier: Development Status :: 3 - Alpha
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130 Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
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131 Classifier: Programming Language :: Python :: 3
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132 Classifier: Topic :: Text Processing :: Linguistic