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-# SeqSero2S
-
-Salmonella serotype prediction from genome sequencing data.
-
-Online version: http://www.denglab.info/SeqSero2
-
-# Updates since SS2 v1.2.1
-1. Convert the sequences of the following alleles to their reverse complement sequences in the SeqSero2 database.
-```
--fliC_b_Wien_CDC_b,d,j__1488\
--fliC_d_from-II-48:d:z6_SRR1168371__1521\
--fliC_a_Salmonella.enterica_from-cdc-Stk2184_other.a__1488
--fliC_l,v_from-Nchanga_SRR1153349__1503
--fliC_l,z13,z28_Salmonella.enterica_from-CDC_2011K-0215_l,v__1506
--fljB_1,7_Salmonella.enterica_from-cdc_Stk1415_1__1521
--fljB_1,5_from-cdc_Stk2184_1__1521
--fljB_1,5_from-Infantis-micro-assembly_SRR1106258_1__1521
--fljB_z6_from-II-48:d:z6_SRR1168371__1503
-```
-2. Delete the following alleles from the SeqSero2 database because of the existence of mutations.
-```
--fliC_y_Bareillystr_AOZP01000027_other.y__1508
--fliC_d_Muenchenstr_ARYW01000085_b,d,j__1496
--fliC_d_Muenchenstr_ARYX01000110_b,d,j__1488
--fliC_g,m_Enteritidisstr_ALHD01000038_g,m__1507
--fljB_1,2_Newportstr_AYDZ01000021_1__1510
-```
-2. Add a fliC 1,5,7 allele and a fliC 1,2,7 allele into the SeqSero2 database.
-```
--fliC_1,5,7_Salmonella.enterica_from-cdc-Stk1778_1,5,7_1521
--fliC_1,2,7_Salmonella.enterica_from-cdc-Stk2293_1,2,7_1521
-```
-3. Delete the O54 allele
-```
--O-54_wbbF__1380
-```
-4. Fixed the bug that caused the misidentification of O9 and O2 by the micro-assembly workflow
-5. Update serotype names based on the simplified KWS scheme
-6. Remove the 9,46,27 allele
-```
--O-9,46,27_partial_wzy__1019
-```
-7. Remove two fljB_1,2 allels
-```
--fljB_1,2_from-Brazzaville_SRR2058145_1__1521
--fljB_1,2_Salmonella.enterica_1,4,5,12:i:1,2,7_AY353272_1__1521
-```
-8. Run 7-gene MLST analysis using stringMLST/mlst
-
-# Introduction 
-SeqSero2S is a pipeline for Salmonella serotype prediction from raw sequencing reads or genome assemblies
-
-# Dependencies 
-SeqSero2S has three workflows:
-
-(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). 
-
-Allele micro-assembly workflow depends on:
-
-1. Python 3;
-2. Biopython 1.73;
-3. [Burrows-Wheeler Aligner v0.7.12](http://sourceforge.net/projects/bio-bwa/files/);
-4. [Samtools v1.8](http://sourceforge.net/projects/samtools/files/samtools/);
-5. [NCBI BLAST v2.2.28+](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download);
-6. [SRA Toolkit v2.8.0](http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software);
-7. [SPAdes v3.9.0](http://bioinf.spbau.ru/spades);
-8. [Bedtools v2.17.0](http://bedtools.readthedocs.io/en/latest/);
-9. [SalmID v0.11](https://github.com/hcdenbakker/SalmID);
-10. [stringMLST v0.6.3](https://github.com/jordanlab/stringMLST);
-
-(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);
-3. [mlst v2.22.1](https://github.com/tseemann/mlst).
-
-
-(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
-
-# Installation
-### Git
-Install mlst and stringMLST first
-```
-conda install mlst
-```
-```
-pip install stringMLST
-```
-To install the SeqSero2S git repository locally:
-```
-git clone https://github.com/LSTUGA/SeqSero2S.git
-cd SeqSero2S
-python3 -m pip install --user .
-```
-
-# Executing the code 
-Make sure all SeqSero2S and its dependency executables are added to your path (e.g. to ~/.bashrc). Then type SeqSero2S.py to get detailed instructions.
-
-    Usage: SeqSero2S.py 
-
-    -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 reads (fasta/fastq))
-
-    -i <file> (/path/to/input/file)
-
-    -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) 
-
-    -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)
- 
-    -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, SeqSero2S will only output serotype prediction without the directory containing log files)
-    
-    -n <string> (optional, to specify a sample name in the report output)
-    
-    -s <flag> (if '-s' was flagged, SeqSero2S will not output header in SeqSero_result.tsv)
-		    
-    --check <flag> (use '--check' flag to check the required dependencies)
-    
-    -v, --version (show program's version number and exit)
-	
-
-# Examples
-Allele mode:
-
-    # Allele workflow ("-m a", default), for separated paired-end raw reads ("-t 2"), use 10 threads in mapping and assembly ("-p 10")
-    SeqSero2S.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")
-    SeqSero2S.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")
-    SeqSero2S.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).
-
-
-# Citation
-Zhang S, Den-Bakker HC, Li S, Dinsmore BA, Lane C, Lauer AC, Fields PI, Deng X. 
-SeqSero2: rapid and improved Salmonella serotype determination using whole genome sequencing data.
-**Appl Environ Microbiology. 2019 Sep; 85(23):e01746-19.** [PMID: 31540993](https://aem.asm.org/content/early/2019/09/17/AEM.01746-19.long) 
-
-Zhang S, Yin Y, Jones MB, Zhang Z, Deatherage Kaiser BL, Dinsmore BA, Fitzgerald C, Fields PI, Deng X.  
-Salmonella serotype determination utilizing high-throughput genome sequencing data.  
-**J Clin Microbiol. 2015 May;53(5):1685-92.** [PMID: 25762776](http://jcm.asm.org/content/early/2015/03/05/JCM.00323-15)