Metadata-Version: 2.1
Name: metmap
Version: 1.0.4
Summary: A tool for generating DNA MTase motif testing sequences
Home-page: https://github.com/cfblaeb/metmap
Author: Lasse Ebdrup Pedersen
Author-email: laeb@biosustain.dtu.dk
License: UNKNOWN
Description: # metmap
        DNA methyltransferase binding motif plasmid assembler
        
        ## Overall purpose:
        * To identify the motifs of multiple DNA methyltransferases (DNA MTase) simultaneously.
        
        ### Quick start
        * This script requires python 3.6 or newer.
        * Install using "pip install metmap"
        * This will put a script named "run_metmap.py" in your pythons bin folder.
          * Linux: You should just be able to type "run_metmap.py" from terminal
          * Windows: The script will be placed in your python folders "Scripts" subfolder and you can run it with "python path\to\python\Scripts\run_metmap.py" from command line
          * Mac: Who knows..probably works like linux?
        
        ### Test
        You can download the tests/test_data.txt and run  
        ```run_metmap.py test_data.txt```  
        This should generate a cas1.fa and cas1.gb file.
        
        ## Overview:
        * You have an organism with multiple identified DNA MTases and you wish to know their individual motifs.
        * You use NGS to obtain motifs of all methylated DNA sites.
          * Some of those motifs will contain ambiguous bases and some will not
        * You submit those motifs to this program
        * This program then stitches these motifs together in random order
        * Motifs may contain ambiguous bases per the IUPAC nucleotide code
          * We can't synthesize lots of ambiguous bases, so we "de-ambigulate" them before putting them into the final construct.
          * De-ambigulation happens according to 1 of 2 rules:
            * Rule 1:
              * Pick L random variants of the motif. E.g. Motif ATGNNTTA have a total of 16 possible actual sequences. If L<16 then the program will random pick L variants (without duplicates). If L>16 then each possible variant will be picked at least L/16 times and some will be picked 1 more than that.
            * Rule 2:
              * Make K copies of each completely "de-ambigulated" variant: E.g. the sequence "SATC" will then be treated as 2 sequences: "GATC" and "CTAC" that will each appear in K copies.
        * We put M N's between each motif
        * And the program will output P versions of these cassettes
        * You then clone this cassette into a plasmid with 1 DNA MTase in each plasmid.
        * You then transform this library into an organism that doesnt natively methylate DNA.
        * Grow, Harvest, Sequence plasmids.
        * ?
        * Profit!  
        
        
        ## Motif file format
        * The motifs should be stored in a standard text file
        * One motif per line, then a comma then a 1 or a 2 to indicate whether either rule 1 or 2 should be used for this motif
          Example:  
          ATGCATGCATGC, 1  
          STGCAGTCATCGTTK, 1  
          ATCNNNNAAA, 2  
          CGTAGCANNNATCGATGC, 2  
          
        
        ### IUPAC nucleotide code:
        |code | nucs|
        |:---:|:---:|
        |R|A or G|
        |Y|	C or T  
        |S|	G or C  
        |W|	A or T  
        |K|	G or T  
        |M|	A or C  
        |B|	C or G or T  
        |D|	A or G or T  
        |H|	A or C or T  
        |V|	A or C or G  
        |N|	any base  
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: Free for non-commercial use
Requires-Python: >=3.6
Description-Content-Type: text/markdown
