GSAE 0.2

A small example package

Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings

Visual Description

Installation

pip install GSAE

NOTE: You will also need to install PyTorch Geometric. Instructions for doing so can be found here

Training the models

To train the GSAE:

python train_gsae.py

To train the GAE or GVAE models:

python train_gnn.py

The scattering inversion network if found ins

gsae/models/inv_scattering.py

Data Processing Workflow

1. Loading data from RNAfold

---

From RNAfold, we get a file like the following

rnafold_output.txt

which inside looks like

GGCGUUUUCGCCUUUUGGCGAUUUUUAUCGCC -14.20  10.00
(.((...(((((....))))).......)).)  -5.50
(.(((..(((((....))))).....).)).)  -4.20
((.....(((((....))))).........))  -5.90
((.((..(((((....))))).....))..))  -5.60
((.(...(((((....))))).......).))  -6.40
((.(.(.(((((....))))).....).).))  -4.20
((.((..(((((....))))).....).).))  -5.10
(((....(((((....))))).(...)..)))  -4.30
(((....(((((....)))))(....)..)))  -5.30

RNAfold output files used in the paper are included in

data/raw_data/

We can use rnafold2arrays.py in gsae/data_processing to convert this text file to

• a csv file containing adjacency matrices for each fold (adjmats_<datestamp>.csv)
• a csv file containing the energy scalar for each structure (energies_<datestamp>.csv)
• a text file with the rna sequence (sequence_<datestamp>.txt)

rnafold2arrays.py usage:

usage: rnafold2arrays.py [-h] --data DATA --outname OUTNAME

optional arguments:
-h, --help         show this help message and exit
--data DATA        RNAfold txt file output to be converted
--outname OUTNAME  base name for the outputs

sample usage:

> python rnafold2arrays.py --data seq4_rnafold_out.txt --outname seq4

Which will produce the following files

seq4_adjmat_2020-03-04-03.csv
seq4_energies_2020_03-04-03.csv
seq4_sequence_2020-03-04-03.txt

If you would like to skip this step, you can also download the processed files from this box link. The file is named processed_data.tar.gz

2. Converting adjacency data to scattering coefficients

---

Once we have the adjacency matrices of the structures we're interested in, we can convert them using scattering transforms to a new, more informative representation

Here we will use diracs centered at each node (i.e. the identity matrix) as our graph signals.

To convert them, we will use adj2scatcoeffs.py

usage: adj2scatcoeffs.py [-h] --data DATA --outname OUTNAME [--pcs PCS]

optional arguments:
-h, --help         show this help message and exit
--data DATA        file (npy or csv) with adjacency matrices
--outname OUTNAME  base name for output
--pcs PCS          how many principle components to use (if 0, then use raw scattering coefficients)

sample usage:

> python adj2scatcoeffs.py --data seq4_adjmat_2020-03-04-03.csv --outname seq_4

If you would like to skip this step, you can also download the processed files from this box link. The file is named scattering_coeffs.tar.gz

3. Create Splits

---

Now that we've generated all the data our model will use, we can now create the train/test splits

To convert them, we will use create_splits.py

usage: create_splits.py [-h] --adjs ADJS --coeffs COEFFS --energies ENERGIES --outname OUTNAME

optional arguments:
-h, --help           show this help message and exit
--adjs ADJS          file with adjacency matrices
--coeffs COEFFS      file with scattering coeffs
--energies ENERGIES  file with energy values
--outname OUTNAME    base name for output

The output set of files can be then stored in a directory which we will later refer to as ROOT_DIR for the reason mentioned below

If you would like to skip this step, you can also download the processed files from this box link. The file is named final_splits.tar.gz

IMPORTANT: Data loading for models

In order to ensure that the training scripts in the model files function correctly, the ROOT_DIR variable at the top of load_splits.py to where the train/test split is located

Data

Data for the 4 sequences used in the paper are located in data/

└── raw_data
    ├── hiv_tar
    │   ├── hiv_tar_sequence.txt
    │   ├── hivtar_100k_subp_n_052020.txt
    ├── hob_seq3
    │   ├── seq3_100k_subp_n_052020.txt
    │   └── seq3_sequence.txt
    ├── hob_seq4
    │   ├── seq4_100k_subp_n_052020.txt
    │   └── seq4_sequence.txt
    └── tebown
        ├── teb_100k_subp_n_052020.txt
        └── tebown_sequence.txt

Citation

@inproceedings{castro2020uncovering,
  title={Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings},
  author={Castro, Egbert and Benz, Andrew and Tong, Alexander and Wolf, Guy and Krishnaswamy, Smita},
  booktitle={2020 IEEE International Conference on Big Data (Big Data)},
  pages={4519--4528},
  year={2020},
  organization={IEEE}
}