Predicting mRNA Degradation using GNNs and RNNs in the Search for a COVID-19 Vaccine
By: Gilles Vandewiele
Tags: Kaggle, Covid 19, Graph Neural Networks, Rna, Recurrent Neural Network

Predicting mRNA Degradation using GNNs and RNNs in the Search for a COVID-19 Vaccine

The current COVID-19 pandemic requires an effective vaccine in order to win the fight against this virus. Researchers from Stanford University are studying mRNA vaccines as these could quickly provide a candidate solution. One challenge to these types of vaccines is their stability: they tend to spontaneously degrade unless kept under intense refrigeration. In order to find a stable mRNA sequence, the people from Stanford University reached out to the Kaggle community in the OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction competition. The competition launched on 11 September and lasted for only 26 days, which is unusually short for Kaggle due to its urgency. There was a prize-pool of $25,000 for the top three competitors. Bram Steenwinckel, Michele Tinti and I were able to achieve the fourth position, missing out on the money by an inch! In the hope that we could even have the slightest contribution in the search for a COVID-19 vaccine, we are publishing this blog post that discusses our solution.

Our team “FromTheWheel & Dyed & StoneShop” got fourth place! Our team name consists of our family names translated (quite literally) from our native languages (Dutch and Italian) to English. This image is a screenshot of the Kaggle Leaderboard.

Problem statement

The goal of this competition was to predict, for each of the bases (A, C, G or U) in the provided RNA sequences, the reactivity and degradation under certain circumstances such as incubation with or without magnesium in high temperatures or high pH. In summary, we have to predict three continuous values (i.e. regression) for each of the bases in the sequence (i.e. sequence-to-sequence).

The training set consists of 2400 sequences of length 107, the public test set contains 629 sequences of length 107 and the private test set consists of 3005 sequences of length 130. There is thus a difference in the sequence length in the private test data. It is therefore of great importance that the model has built-in invariance to the sequence length. Moreover, the sequences are only partially scored. For the train and public test set, only the first 68 of 107 bases are scored while for the private test set, the first 91 out of 130 bases are scored. An example of the targets for one of the training sequences is provided below:

The time series of target values for one of the training sequences. Image by Author.

We can also display these target values on the RNA sequence graph directly as done for the reactivity in the example below:

One of the possible foldings of a RNA sequence. The node colors depict the reactivity values (red = high, blue = low). Notice that 107–68 nodes are not colored as no targets are specified for the tail of the sequence. Image by Author.

High-level solution overview

By looking at the targets, it is clear that a certain value is correlated with its neighbouring values in the time series. Recurrent Neural Networks are a perfect fit for these kinds of situations, as their built-in memory takes into account the nearby predicted values. We learn a representation for each of the bases in our sequence and pass these through 2 (bi-directional) RNN layers. The representation for each node is learned by using both information of the node itself and relationship or edge information. As can be seen in the introduction, the RNA sequence can be represented by a graph, and relations between certain bases in the sequence are present. We use Graph Layers to aggregate this relationship information. Our final solution consists of a blend of 4 different models, in which we vary the RNN layers (LSTM+LSTM, LSTM+GRU, GRU+LSTM and GRU+GRU). Some details are left out from this high-level overview. One important detail is that we pre-trained the network until the RNN layers as an Auto-Encoder. This allowed us to train on the unlabeled test data as well.

A high-level overview of our solution. Image by Author.

Generating extra information with ARNIE

ARNIE is a software package that wraps around 6 different libraries which can be used to generate extra information for each of the sequences:

For a provided RNA sequence, ARNIE is able to calculate a Base Pairing Probability (BPP) matrix. Based on that BPP, a potential structure can be predicted and from these structures, the loop type can be inferred for each of the bases in the sequence.

We used different libraries in ARNIE to generate extra information. This often required a bit of bash magic in order to get everything installed. We provide a notebook to demonstrate how to generate this information. We experimented with all 6 libraries, but only found CONTRAfold to be the most useful, followed by RNAsoft, RNAstructure and Vienna.

Node features

From our high-level overview, we can see 2 types of inputs are required for our network: node information and edge information. The node information is a NxLxV matrix with N the number of RNA sequences, L the length of the sequences and V the number of features per node. We use V=25 features in total:

One-hot-encoded base (‘A’, ‘G’, ‘C’ or ‘U’) (4 features)

(‘A’, ‘G’, ‘C’ or ‘U’) (4 features) One-hot-encoded loop type (provided: ‘S’: paired Stem, ‘M’: Multiloop, ‘I’: Internal loop, ‘B’: Bulge, ‘H’: Hairpin loop, ‘E’: dangling End or ‘X’: eXternal loop) (7 features)

(provided: ‘S’: paired Stem, ‘M’: Multiloop, ‘I’: Internal loop, ‘B’: Bulge, ‘H’: Hairpin loop, ‘E’: dangling End or ‘X’: eXternal loop) (7 features) one-hot-encoded positional feature (index % 3) (3 features)

feature (index % 3) (3 features) certainty : fraction of packages (3 different ones: Vienna, CONTRAfold and RNAstructure) that predict ( , ) or . (3 features).

: fraction of packages (3 different ones: Vienna, CONTRAfold and RNAstructure) that predict , or (3 features). CapR loop type probabilities (6 features)

probabilities (6 features) BPP sum and number of zeroes (2 features)

Edge features

The edge information is captured in a NxLxLxE matrix, with N the number of RNA sequences, L the length of the sequences and E the number of edge features. In total, we have E=5 features for each edge:

3 different BPPs : the provided ones, generated with RNAsoft (ARNIE) and generated with contrafold (ARNIE)

: the provided ones, generated with RNAsoft (ARNIE) and generated with contrafold (ARNIE) whether there is a base pairing indicated in the structure ( ( and ) )

indicated in the structure ( and ) the distances (manhattan) between bases, normalized by sequence length

The model

Our pipeline was an adaptation from this great notebook by mrmakr. Make sure to give that notebook an upvote! We did make a few modifications to the original notebook:

Corrected the loss function of the classification part of the network.

Changed the loss function of the auto-encoder, which was tailored for binary representations (it was a variant of binary cross-entropy). We simply used Reconstruction Loss/MAE (|y — y_hat|).

Added 2 RNN layers after the Encoder. As was illustrated during the competition (by @tuckerarrants and others), blending a GRU+GRU, LSTM+GRU, GRU+LSTM and LSTM+LSTM model results in a slight boost. We generated models in 10-fold CV for each of these 4 combinations (so 40 models in total) and blended those with uniform weights.

Tuned some hyper-parameters. The epochs of AE, Classification part, the number of units in the GCN layers, …

Adding augmented data, from the notebook made by @its7171.

Training on all the samples but using different sample weights based on their signal_to_noise. Again a shout out to @its7171 for this! Evaluation was only performed on data with SN_filter=1. Because we expected the private LB to do that as well ;)

Things that did not work

We tried many things that did not work:

We got 3D foldings of all the RNA and calculated 3D (euclidean) distances between the RNA bases. We also tried to use angle information (binned and then categorically encoded): this boosted our simple LSTM architecture but deteriorated the performance of the AE+LSTM. There’s definitely some potential in these features, but they require some more thorough experimentation. A dataset to allow for such experimentation can be found here.

Many software packages that give some RNA features were not informative. Some of these include RNAup, Shaker, MXFold, …

All of the spectral representations discussed in this paper.

Richer graph representations (e.g. including amino-acid information)

Entropy features

Atom information

Concluding words

I really enjoyed this competition. I especially liked the fact that it only lasted for a rather short period of time. It was a neck-to-neck race on the public leaderboard for the course of the entire competition. Very stressful, but fun! This kind of “blitz” competitions could perhaps be considered as a separate format by Kaggle?

While we just missed out on the money (by 1 position), we are still very proud of our result! With this result, my two teammates: Bram and Michele have also become Kaggle Competition Masters. So a huge congratulations to them!

We provide the code of our solution on both Kaggle and Github.

As usual, if anything is unclear or requires some more explanation: do not hesitate to leave a comment or contact me!

Keep an eye open on this post, as it may receive some updates in the nearby future!

See you in the next competition!

Gilles, Bram and Michele