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

Visual Description

Update 05/21

Please refer to the lab's official implementation as this repo will no longer be maintained

How to run

First, install dependencies

# clone project   
git clone https://github.com/ec134/GSAE   

# install project   
cd GSAE
pip install -e .   
pip install -r requirements.txt

Workflow

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

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
> ls
seq4_adjmat_2020-03-04-03.csv
seq4_energies_2020_03-04-03.csv
seq4_sequence_2020-03-04-03.txt
rnafold2arrays.py
seq3_rnafold_out.py

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

adj2scatcoeffs.py usage:

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

optional arguments:
-h, --help            show this help message and exit
--data DATA           csv with adjacency matrices
--outname OUTNAME     base name for output
--graph_size GRAPH_SIZE
                        number of nodes in graphs (assume equal size)
--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 seq4 --graph_size 32 --pcs 100

> ls
seq4_scat_coeff_2020-03-04-03_pca_n100.csv
seq4_adjmat_2020-03-04-03.csv
adj2scatcoeffs.py

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

IMPORTANT: Data loading for models

In order to ensure that the training scripts in the model files function correctly, the global variables at the top of load_splits.py must be assigned to whereever you save the outputs of adj2scatcoeffs.py.

Training the models

python gsae/models/gsae_model.py 

arguments:

usage: gsae_model.py [--input_dim INPUT_DIM] [--dataset DATASET] [--bottle_dim BOTTLE_DIM] [--hidden_dim HIDDEN_DIM] [--learning_rate LEARNING_RATE] [--alpha ALPHA] [--beta BETA] [--n_epochs N_EPOCHS] [--batch_size BATCH_SIZE] [--n_gpus N_GPUS] [--save_dir SAVE_DIR]

Citation

@article{castro2020uncovering,
  title={Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings},
  author={Castro, Egbert and Benz, Andrew and Tong, Alexander and Wolf, Guy and Krishnaswamy, Smita},
  journal={arXiv preprint arXiv:2006.06885},
  year={2020}
}