README.Rmd

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[logo](logo.svg)

# __DeepG4__: A deep learning approach to predict active G-quadruplexes

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__DeepG4__ is a deep learning model build in keras+tensorflow who aims to predict the probability of DNA sequences to form G-Guadruplexes secondary structures. __DeepG4__ is wrapped in a R package, but can work with any langage that has implemented keras and tensorflow (see below). 

## Abstract

DNA is a complex molecule carrying the instructions an organism needs to develop, live and reproduce. In 1953, Watson and Crick discovered that DNA is composed of two chains forming a double-helix. Later on, other structures of DNA were discovered and shown to play important roles in the cell, in particular G-quadruplex (G4). Following genome sequencing, several bioinformatic algorithms were developed to map G4s in vitro based on a canonical sequence motif, G-richness and G-skewness or alternatively sequence features including k-mers. Here, we propose instead a convolutional neural network (DeepG4) to map active G4s (forming both in vitro and in vivo). DeepG4 is very accurate to predict active G4s, while state-of-the-art algorithms fail. Moreover, DeepG4 identifies key DNA motifs that are predictive of G4 activity. We found that active G4 motifs do not follow a very flexible sequence pattern as current algorithms seek for. Instead, active G4s are determined by numerous specific motifs. Moreover, among those motifs, we identified known transcription factors which could play important roles in G4 activity by either directly contributing to G4 structure themselves or by participating in G4 formation in the vicinity. Lastly, variant analysis suggests that SNPs altering predicted G4 activity could affect transcription and chromatin, e.g. gene expression, H3K4me3 mark and DNA methylation. Thus, DeepG4 paves the way for future studies assessing the impact of known disease-associated variants on DNA secondary structure and provides a mechanistic interpretation of SNP impact on transcription and chromatin.

## Installation

You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("morphos30/DeepG4")
```
## Usage with DeepG4 R package

```r
library(Biostrings)
library(DeepG4)

sequences <- system.file("extdata", "test_G4_data.fa", package = "DeepG4")
sequences <- readDNAStringSet(sequences)

predictions <- DeepG4(sequences)
head(predictions)
```

## Using our model directly with keras in R

Using our model with keras is very simple, the code is very similar, but you have to convert youre sequence in one-hot first. To help you, our function `readDNAStringSet` help you to do it.

```r

library(Biostrings)
library(DeepG4)
library(keras)

sequences <- system.file("extdata", "test_G4_data.fa", package = "DeepG4")
sequences <- readDNAStringSet(sequences)

model <- system.file("extdata", "model.hdf5", package = "DeepG4")
model <- load_model_hdf5(model)

sequences <- DNAToNumerical(sequences)

predictions <- predict(model,sequences)
```

## Using DeepG4 with a new active G4 dataset

If you want to use our model architecture, but retrain with your own dataset, you can do it by running our function `DeepG4` with `retrain = TRUE`

```r

library(Biostrings)
library(DeepG4)
library(rsample)

# Read positive and segative set of sequences 
sequences.pos <- readDNAStringSet("Peaks_BG4_G4seq_HaCaT_GSE76688_hg19_201b.Fa")
sequences.ctrl <- readDNAStringSet("Peaks_BG4_G4seq_HaCaT_GSE76688_hg19_201b_Ctrl_gkmSVM.Fa")
sequences <- c(sequences.pos,sequences.ctrl)
# Generate classes
Y <- c(rep(1,length(sequences.pos)),rep(0,length(sequences.ctrl)))

```

It's a good idea to split your dataset in train/test to evaluate the model performance on the testing dataset.

```r
#  Sample dataset and get test and train dataset
smp_size <- floor(0.70 * length(sequences))
train_ind <- sample(seq_len(length(sequences)), size = smp_size)
x.train <- sequences[train_ind]
x.test <- sequences[-train_ind]
y.train <- Y[train_ind]
y.test <- Y[-train_ind]
```
Then train your model on your training dataset :

```r
training <- DeepG4(x.train,y.train,retrain=TRUE,retrain.path = "DeepG4_retrained.hdf5")
```

You can now evaluate it with your testing dataset :

```r
predictions <- DeepG4(x.test,y.test,model = "DeepG4_retrained.hdf5")
predictions
```