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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
### [__DeepG4__: A deep learning approach to predict cell-type specific active G-quadruplex regions](https://journals.plos.org/ploscompbiol/article/comments?id=10.1371/journal.pcbi.1009308)
The predictions for differents tissues and cancer with DeepG4 is available [here](https://github.com/morphos30/DeepG4ToolsComparison/tree/main/prediction_genome/bed/predictions).
_Vincent Rocher, Matthieu Genais, Elissar Nassereddine and Raphael Mourad_
<!-- badges: start -->
[](https://codecov.io/gh/morphos30/DeepG4?branch=master)
<!-- badges: end -->
**DeepG4** is a deep learning model developed to predict a score of DNA sequences to form active G-Guadruplexes (found both in vitro and in vivo) using __DNA sequences__ and __DNA accessibility__.
**DeepG4** is built in keras+tensorflow and is wrapped in an R package.
## Requirements
DeepG4 was built with `Keras 2.3.1` and `tensorflow 2.1.0`, but it
should work with any version of theses libraries.
### Update 30/05/2022
It seems that our model cannot be properly load so please install keras/tensorflow using the environment file provided :
On a terminal:
`conda env create -f environment.yml`
On R:
``` r
install.packages("keras")
library(keras)
reticulate::use_condaenv("DeepG4")
```
This will provide you with default CPU installations of Keras and TensorFlow python packages (within a virtualenv) that can be used with or without R.
## Installation
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("morphos30/DeepG4")
```
## Basic usage of DeepG4
### With accessibility
Given small regions (bed) and an accessibility file (coverage file from ATAC-seq/DNAse-seq/MNase-seq), you can predict active G4 regions in a __specific cell type__:
```r
library(rtracklayer)
library(BSgenome.Hsapiens.UCSC.hg19)
library(DeepG4)
BED <- system.file("extdata", "test_G4_data.bed", package = "DeepG4")
BED <- import.bed(BED)
ATAC <- system.file("extdata", "Peaks_BG4_G4seq_HaCaT_GSE76688_hg19_201b_Accessibility.bw", package = "DeepG4")
ATAC <- import.bw(ATAC)
Input_DeepG4 <- DeepG4InputFromBED(BED=BED,ATAC = ATAC,GENOME=BSgenome.Hsapiens.UCSC.hg19)
Input_DeepG4
```
```
[[1]]
A DNAStringSet instance of length 100
width seq
[1] 201 GTTCGGGCCTCGGTCGCGCCGCCGGGTCTTGCAGACGCGAATGTAAACAGAAACA...TGACTCCTGGAGCGACCTTCACGAGGGAAAGCGCGCCCCCCGGCACCCACCCCT
[2] 201 TTTCTATAGTTTTCTTTTGTTTCTACCTCATGACTAGATGATTCACTGCTTGAAC...GTCAAATCTGTCCATCTTCACTGCCACCCTTCAGTACCAAATGACCAGTCTCTT
[3] 201 GCTTAAAAGCCTGTAAGAAAGATATAATTTGATAGAACTGGCTAGGATTTGTCAG...CGTCAGGGAGGGGGTGGGGCCTCCACGTGGGAGATCTTGCCTGGAGGTGGTGGA
[4] 201 TCCCACACCCGGTAGATGTAAGGGAAAAACTGCATTACCCAGAAGGCACTGCCCC...GTGTGACGTCATCTCCGTGGGCCGGTTTGGCCCTGAAACAGTGTGGGGCCTAGA
[5] 201 AGTAGCTACAGAGTTCCTGCTCCAGCAACCAGGAGCCTTGAGGCAGCACAAGGAC...ACCACAATGTCTGCCAAGAAAGAGGATGAGTCACCAAGACCCACAGGAAAGAGG
... ... ...
[96] 201 CACATGCCTTCCTTGGGGACGTGTTCACACATGTGGCCCTAGCTGTGAGAGACAG...CATCTCAGAACAGCTGAGCTGGAAGTGGGTGAATAATAATAATAATAATAATAA
[97] 201 TGGTGGTCTTTCTCTACCGGGCCTGGTAGCCAAAGACAAAGGTCATAATCACTTG...CTATGTACTCTTCAAAGTGCCACCTCCTGGCTGCAAGCCAACCAACACAAAACC
[98] 201 TGACCGTAGACCTCGTGCACTTCTGCTGCGGTCGGGGCCGGAGTCTGGGCTGGAG...GCGATCCAGAGCCAAGCGCCCCGCCCCTGCCCGGGCGCGCTCCCTCCTTAGCCC
[99] 201 TTAACGTCATCAGTCGGGAGGACGACAGCTACGCACGCGCGGGGCACCTCCTCTG...GCCACGGTGGAGGCAGCGGCGAGAGGGGGCGGGGACAAGGAGAGGGCACGCACG
[100] 201 GTGTCCGGGTGAGAGACCTGGAGGTGGGGCCTAGGTGTCTACCCGGCCAGGTGCG...TAAGGCTCGGGGCCAGTCGTCGTCCATTCCTTCCTAACACCTCCCTATCCTCCC
[[2]]
[1] 0.000000000 0.016287416 0.033261447 0.069375103 0.018520650 0.010934717 0.036308476 0.315843234 0.037658374
[10] 0.045887551 0.037320211 0.042853401 0.068908093 0.071774485 0.084947561 0.027456211 0.033915868 0.006912598
[19] 0.012604675 0.051405275 0.093813195 0.019288668 0.051228826 0.019520666 0.048686840 0.050116329 0.045801884
[28] 0.033079207 0.035834917 0.056326946 0.096531489 0.064706374 0.026422647 0.016979087 0.008512502 0.021891554
[37] 0.016688682 0.109472225 0.047901838 0.066676075 0.052591085 0.017467983 0.035541899 0.060001992 0.028878783
[46] 0.056284886 0.045126048 0.052469122 0.101620595 0.047741155 0.036925371 0.021645371 0.044472962 0.012457179
[55] 0.020373459 0.109529076 0.039006694 0.047824384 0.028752257 0.015437852 0.069926660 0.022213134 0.019726120
[64] 0.044609840 0.028773493 0.008077349 0.042587371 0.016502886 0.035757895 0.015023933 0.024181422 0.057516040
[73] 0.027492004 0.030316917 0.049878433 0.020105394 0.025934350 0.023845766 0.032338052 0.048007935 0.136436151
[82] 0.060423998 0.034617445 0.051958662 0.064664156 0.034518694 0.020277026 0.042060108 0.055335700 0.051632313
[91] 0.066588875 0.030586623 0.043823259 0.034947155 0.082091662 0.008496193 0.034567766 0.055516400 0.062191534
[100] 0.049011882
```
Then predict using both __DNA__ and __Accessibility__ :
```r
predictions <- DeepG4(X=Input_DeepG4[[1]],X.atac = Input_DeepG4[[2]])
head(predictions)
```
```
[,1]
[1,] 0.8414769
[2,] 0.5075037
[3,] 0.9905243
[4,] 0.9991857
[5,] 0.9387835
[6,] 0.2330312
```
### Without accessbility
You still can predict active G4 regions using only __DNA__ sequences :
```r
library(rtracklayer)
library(BSgenome.Hsapiens.UCSC.hg19)
library(Biostrings)
library(DeepG4)
BED <- system.file("extdata", "test_G4_data.bed", package = "DeepG4")
BED <- import.bed(BED)
sequences <- getSeq(BSgenome.Hsapiens.UCSC.hg19,BED)
predictions <- DeepG4(X=sequences)
head(predictions)
```
```
[,1]
[1,] 0.9478214
[2,] 0.5868858
[3,] 0.9660227
[4,] 0.9093548
[5,] 0.9119551
[6,] 0.2471965
```
## Advanced usage of DeepG4
If you have a large sequence (>201bp up to several Mbp), you can scan the sequence and predict the positions of active G4s within the sequence.
### With accessibility
``` r
library(rtracklayer)
library(BSgenome.Hsapiens.UCSC.hg19)
library(DeepG4)
BED <- system.file("extdata", "promoters_seq_example.bed", package = "DeepG4")
BED <- import.bed(BED)
ATAC <- system.file("extdata", "Peaks_BG4_G4seq_HaCaT_GSE76688_hg19_201b_Accessibility.bw", package = "DeepG4")
ATAC <- import.bw(ATAC)
res <- DeepG4Scan(X = BED,X.ATAC=ATAC,k=20,treshold=0.5,GENOME=BSgenome.Hsapiens.UCSC.hg19)
```
DeepG4Scan function scans each input sequence with a step of `k=20` and outputs for each input sequence the G4 positions (+/- 100bp) and the corresponding DeepG4 probabilities (>= treshold).
``` r
library(dplyr)
res %>% dplyr::select(-seq) %>% group_by(seqnames) %>% dplyr::slice(1:2) %>% head
```
```
# A tibble: 6 x 5
# Groups: seqnames [3]
seqnames start end width score
<fct> <int> <int> <int> <dbl>
1 chr15 63569229 63569429 201 0.690
2 chr15 63569249 63569449 201 0.810
3 chr2 131850345 131850545 201 0.548
4 chr2 131850385 131850585 201 0.671
5 chr5 10562715 10562915 201 0.547
6 chr5 10562735 10562935 201 0.503
```
### Without accessibility
``` r
library(Biostrings)
library(rtracklayer)
library(BSgenome.Hsapiens.UCSC.hg19)
library(DeepG4)
sequences <- import.bed(system.file("extdata", "promoters_seq_example.bed", package = "DeepG4"))
sequences <- getSeq(BSgenome.Hsapiens.UCSC.hg19,sequences)
res <- DeepG4Scan(X = sequences,k=20,treshold=0.5)
```
DeepG4Scan function scans each input sequence with a step of `k=20` and outputs for each input sequence the G4 positions (+/- 100bp) and the corresponding DeepG4 probabilities (>= treshold).
``` r
library(dplyr)
res %>% dplyr::select(-seq) %>% group_by(seqnames) %>% dplyr::slice(1:2) %>% head
```
```
# A tibble: 6 x 5
# Groups: seqnames [3]
seqnames start end width score
<fct> <int> <int> <int> <dbl>
1 chr15 63569229 63569429 201 0.690
2 chr15 63569249 63569449 201 0.810
3 chr2 131850345 131850545 201 0.548
4 chr2 131850385 131850585 201 0.671
5 chr5 10562715 10562915 201 0.547
6 chr5 10562735 10562935 201 0.503
```
## Scan DeepG4 DNA motifs from the input sequences
Using one-hot encoding of DNA, convolution kernels (first layer of DeepG4)
can be interpreted as weighted motifs, similar to position weight matrices (PWMs) used for DNA motifs.
The function ExtractMotifFromModel detects DeepG4 DNA motifs found in the input sequences.
``` r
library(Biostrings)
library(DeepG4)
library(ggseqlogo)
library(cowplot)
sequences <- readDNAStringSet(system.file("extdata", "test_G4_data.fa", package = "DeepG4"))
res <- ExtractMotifFromModel(sequences,top_kernel=4)
p.pcm <- lapply(res,function(x){ggseqlogo(as.matrix(x)) + ggplot2::theme_classic(base_size=14)})
print(plot_grid(plotlist = p.pcm,ncol=2))
```
## 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)
library(rtracklayer)
library(BSgenome.Hsapiens.UCSC.hg19)
ATAC <- system.file("extdata", "Peaks_BG4_G4seq_HaCaT_GSE76688_hg19_201b_Accessibility.bw", package = "DeepG4")
ATAC <- import.bw(ATAC)
# Read positive and segative set of sequences
bed.pos <- import.bed(system.file("extdata", "Peaks_BG4_G4seq_HaCaT_GSE76688_hg19_201b.bed", package = "DeepG4"))
bed.neg <- import.bed(system.file("extdata", "Peaks_BG4_G4seq_HaCaT_GSE76688_hg19_201b_Ctrl_gkmSVM.bed", package = "DeepG4"))
# Generate classes
Y <- c(rep(1,length(bed.pos)),rep(0,length(bed.neg)))
BED <- c(bed.pos,bed.neg)
Input_DeepG4 <- DeepG4InputFromBED(BED=BED,ATAC = ATAC,GENOME=BSgenome.Hsapiens.UCSC.hg19)
```
```r
training <- DeepG4(X=Input_DeepG4[[1]],X.atac=Input_DeepG4[[2]],Y,retrain=TRUE,retrain.path = "DeepG4_retrained.hdf5")
```
You can now take a look on the results :
``` r
library(cowplot)
p_res_train <- cowplot::plot_grid(plotlist = training[2:3])
print(p_res_train)
```
``` r
training[[4]]
```
```
# A tibble: 4 x 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 accuracy binary 0.987
2 kap binary 0.973
3 mn_log_loss binary 0.0525
4 roc_auc binary 0.999
```