Commit 5af8294b authored by Roman Sarrazin-Gendron's avatar Roman Sarrazin-Gendron
Browse files

Final paper supplementary version

parent 969ac9ad
...@@ -13,6 +13,7 @@ All scripts from this package can be found in src/pgmpy, updated from [github]( ...@@ -13,6 +13,7 @@ All scripts from this package can be found in src/pgmpy, updated from [github](
* regex * regex
* GhostScript * GhostScript
* BioPython * BioPython
* wrapt
## Software requirements ## Software requirements
...@@ -22,8 +23,12 @@ All scripts from this package can be found in src/pgmpy, updated from [github]( ...@@ -22,8 +23,12 @@ All scripts from this package can be found in src/pgmpy, updated from [github](
# Installation # Installation
All the code and files required for the downloadable version of the software can be found in the git repository : http://jwgitlab.cs.mcgill.ca/sarrazin/rnabayespairing.git
We also have a webserver on which you can annotate sequences for module signal with our two main datasets: http://bayespairing.cs.mcgill.ca/
* Clone the git repository and execute the following commands: * Clone the git repository and execute the following commands:
* cd bayespairing * cd rnabayespairing
* pip install . * pip install .
This will install the dependencies and allow you to use some of the scripts outside of the directory. This will install the dependencies and allow you to use some of the scripts outside of the directory.
...@@ -34,6 +39,9 @@ Three python scripts found in `/src` can be used to run `BayesPairing`. ...@@ -34,6 +39,9 @@ Three python scripts found in `/src` can be used to run `BayesPairing`.
# Predicting module likelihood from sequence # Predicting module likelihood from sequence
We provide a basic overview of the main functions of the software with the following examples, which use dummy sequences. For biologically relevant applications, please consult the python notebooks in the *test* directory
The main script for predicting the likelihood of a module in an input sequence is `parse_sequences.py`. The main script for predicting the likelihood of a module in an input sequence is `parse_sequences.py`.
For manual: For manual:
...@@ -62,9 +70,9 @@ The following arguments can be used to call it. only -seq is required. ...@@ -62,9 +70,9 @@ The following arguments can be used to call it. only -seq is required.
-m M number of folded candidates, must be larger than n -m M number of folded candidates, must be larger than n
-n N number of outputted candidates -n N number of outputted candidates
-seq SEQ sequences to parse, FASTA file -seq SEQ sequences to parse, FASTA file
-ss SS facultative secondary structures, FASTA file -ss SS facultative secondary structures in the commandline as a string. If your input comes within a fasta file, write "infile"
-d D Dataset, as a pickle. Default will be the dataset -d D Dataset, as a pickle. Default will be the dataset
presented in the paper presented in the paper, rna3dmotif.
-mod MOD [MOD ...] If you only want to parse specific modules, list them. -mod MOD [MOD ...] If you only want to parse specific modules, list them.
ex -mod 1 4 12 ex -mod 1 4 12
-p P Number of times the Bayes Net should be sampled -p P Number of times the Bayes Net should be sampled
...@@ -72,8 +80,9 @@ The following arguments can be used to call it. only -seq is required. ...@@ -72,8 +80,9 @@ The following arguments can be used to call it. only -seq is required.
Default:25 Default:25
-w W Window Length [50 to 300]. Default:200 -w W Window Length [50 to 300]. Default:200
-s S Step size between windows [10 to 200]. Default:100 -s S Step size between windows [10 to 200]. Default:100
-k K weight of the secondary structure analysis [0.8 to 2]. -sw SW weight of the secondary structure analysis [0.8 to 2].
Default:1 Default:1
-o O The name of the output pickle file.
``` ```
To test `parse_sequences.py`, you can try the following command, which will run 4 modules from the default dataset on two sequences in the example fasta file : To test `parse_sequences.py`, you can try the following command, which will run 4 modules from the default dataset on two sequences in the example fasta file :
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