README 1.92 KB
aRNhAck 1.0

REQUIRED PYTHON LIBRARIES: 
    Bio-Python
    networkx

OPTIONAL DEPENDANCIES:
    Infernal: If the WT sequence in the mutate-and-map experiment is not already aligned in the MSA, 
        the argument '-i' can be used to leverage infernal to produce the alignment http://eddylab.org/infernal/

OUTPUT:
    each line contains two numbers (a, b) such that 
            a: is the disruptive mutation
            b: A position satisfying the input parameters in relation to 'a'

usage: arnhack.py [-h] -r RDAT_PATH -s MSA_PATH -d DELTA -g GAMMA -z ZETA [-i]

required arguments:
  -r RDAT_PATH, --rdat_path RDAT_PATH
                        Path to mutate-and-map 'rdat' file
  -s MSA_PATH, --msa_path MSA_PATH
                        Path to multiple sequence alignment 'stockholm' file
  -d DELTA, --shape_disruption_percentile DELTA
                        The percentile cutoff of the SHAPE profile disruption
  -g GAMMA, --gamma_distance GAMMA
                        Minimal distance in the graph to be considered for the
                        mutations
  -z ZETA, --npmi_percentile ZETA
                        The percentile cutoff of the NPMI

optional arguments: 
  -h, --help            show this help message and exit
  -i, --infernal        Uses infernal to align the wt to the provided MSA The
                        resulting alignment will be saved in the file
                        "rdat_path_msa_path"


As an example, the files provided in NAR_2016_DATA can be used:

    python arnhack.py -r ../NAR_2016_DATA/Data/5SRRNA_SHP_0002.rdat -s ../NAR_2016_DATA/Data/5SRRNA_SHP_0002_RF00001.stockholm.txt -d 98 -g 22 -z 90
 
Output:
    83 48
    83 34
    83 45
    116 42
    116 43
    116 37

Which mean that mutations 83 (resp. 116) is in the 98th percentile of SHAPE profile disruption and that mutations 48, 34 and 45 (resp. 42, 43 and 37) are at a distance of at least 22 and are each in the 90th percentile of NPMI values.