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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.