DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions

The predictions for differents tissues and cancer with DeepG4 is available here.

Vincent Rocher, Matthieu Genais, Elissar Nassereddine and Raphael Mourad

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:

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 with:

# 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:

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 :

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 :

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

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).

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

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).

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.

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

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

DeepG4

Install / Use

README

DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions

Requirements

Update 30/05/2022

Installation

Basic usage of DeepG4

With accessibility

Without accessbility

Advanced usage of DeepG4

With accessibility

Without accessibility

Scan DeepG4 DNA motifs from the input sequences

Using DeepG4 with a new active G4 dataset