annotate build/lib/SeqSero2.egg-info/PKG-INFO @ 13:e3b74e412f40 draft

planemo upload commit 70dc513aa7d7ac6785847dfd86323687613b6b68-dirty

Metadata-Version: 1.1
Name: SeqSero2
Version: 1.1.1
Summary: Salmonella serotyping
Home-page: https://github.com/denglab/SeqSero2/
Author: Shaokang Zhang, Hendrik C Den-Bakker and Xiangyu Deng
Author-email: [e-mail], [e-mail], [e-mail]
License: GPLv2
Description: # SeqSero2 v1.1.1
        Salmonella serotype prediction from genome sequencing data.
        
        Online version: http://www.denglab.info/SeqSero2
        
        # Introduction 
        SeqSero2 is a pipeline for Salmonella serotype prediction from raw sequencing reads or genome assemblies
        
        # Dependencies 
        SeqSero2 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).
        
        
        (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);
        
        
        (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
        ### Conda
        To install the latest SeqSero2 Conda package (recommended):  
        ```
        conda install -c bioconda seqsero2=1.1.1
        ```
        ### Git
        To install the SeqSero2 git repository locally:
        ```
        git clone https://github.com/denglab/SeqSero2.git
        cd SeqSero2
        python3 -m pip install --user .
        ```
        ### Other options
        Third party SeqSero2 installations (may not be the latest version of SeqSero2): \
        https://github.com/B-UMMI/docker-images/tree/master/seqsero2 \
        https://github.com/denglab/SeqSero2/issues/13
        
        
        # Executing the code 
        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.
        
            Usage: SeqSero2_package.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 fasta, '6'for nanopore 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, SeqSero2 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, SeqSero2 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")
            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).
        
        
        # 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)
        
Keywords: Salmonella serotyping bioinformatics WGS
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Text Processing :: Linguistic