@@ -6,13 +6,13 @@ We use the Rfam mappings between 3D structures and known Rfam families, using th
Future versions might compute a real MSA-based clusering directly with Rfamseq ncRNA sequences, like ProteinNet does with protein sequences, but this requires a tool similar to jackHMMER in the Infernal software suite, which is not available yet.
This script prepares the dataset from available public data in PDB and Rfam.
It requires solid hardware to run. (Tested on a server with 24 cores and 48GB of RAM.)
It requires solid hardware to run. (Tested on a server with 32 cores and 48GB of RAM.)
# Dependencies
You need to install Infernal, DSSR, and SINA before running this.
I moved to python3.8.1. Unfortunately, python3.6 is no longer supported, because of changes in the multiprocessing and Threading packages. Untested with Python 3.7.*.
Packages numpy, pandas, matplotlib, requests, psutil, biopython, and sqlalchemy are required.
Packages numpy, pandas, matplotlib, requests, psutil, biopython, sqlalchemy and tqdm are required.
Before use, please set the two variables `path_to_3D_data` and `path_to_seq_data` (around line 30 of RNAnet.py) to two folders where you want to store RNA 3D structures and RNA sequences. A few gigabytes will be produced.
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@@ -34,7 +34,7 @@ Now, compute the features:
Then, compute the labels:
* Run DSSR on every chain to get eta' and theta' pseudotorsions
* Run DSSR on every chain to get a variety of descriptors per position, describing secondary and tertiary structure
* This also permits to identify missing residues and compute a mask for every chain.