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Code for paper 'Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications'

License: MIT License

Python 1.22% R 0.01% Jupyter Notebook 98.77%

multirm's Introduction

MultiRM: Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occuring RNA modifications

DOI

Prerequisites

  • python: 3.7.6
  • CUDA: 10.1
  • pytorch: 1.2.0

Installation

For command line version, our current release has been tested on Ubuntu 16.04.4 LTS.

For GUI version, please check it on our Web Server.

Cloning the repository and downloading MultiRM

git clone https://github.com/Tsedao/MultiRM.git
cd MultiRM

Demo

Here is a simple demo which using GGGGCCGTGGATACCTGCCTTTTAATTCTTTTTTATTCGCCCATCGGGGCCGCGGATACCTGCTTTTTATTTTTTTTTCCTTAGCCCATCGGGGTATCGGATACCTGCTGATTCCCTTCCCCTCTGAACCCCCAACACTCTGGCCCATCGGGGTGACGGATATCTGCTTTTTAAAAATTTTCTTTTTTTGGCCCATCGGGGCTTCGGATA RNA sequence as input to predict modifications.

Usage:

cd Scripts
python main.py -s [RNA sequence] --top [No. of top-k highlighted sites] --alpha [significant level] --gpu [which gpu to use]

The following options are available:

  • seqs: Input single RNA sequnce in string format. (Minimum length: 51-bp)
  • att_window: Length of sliding window to aggregate attention weights on a single sequence. (default:3; recommended range: 2-6)
  • top: Number of top consecutive nucleotides based on the summation of attention weights. (default:3; recommended range: 2-5)
  • alpha:Significance level. (default:0.05)
  • verbose: Whether to show the detailed predictions or not. (default:False)
  • save: Whether to save the results to file or not. (default:False)
  • save_path: (Optional) Path of desirable directory to store results. (default: current working directory)
  • save_id: (Optional) JOBID for the use of web sever.

Example:

python main.py -s GGGGCCGTGGATACCTGCCTTTTAATTCTTTTTTATTCGCCCATCGGGGCCGCGGATACCTGCTTTTTATTTTTTTTTCCTTAGCCCATCGGGGTATCGGATACCTGCTGATTCCCTTCCCCTCTGAACCCCCAACACTCTGGCCCATCGGGGTGACGGATATCTGCTTTTTAAAAATTTTCTTTTTTTGGCCCATCGGGGCTTCGGATA --top 3 --att_window=3 --alpha=0.1 --gpu=0

Predicting the RNA modification of a singe RNA sequence (Minimum length:51-bp), the result generates as:

Note: MultiRM does not make predictions for the first and
last 25nt of the input sequence.

************************Reporting************************
***************Visualize modification sites**************
************************  1- 50 nt***********************
Origin GGGGCCGTGGATACCTGCCTTTTAATTCTTTTTTATTCGCCCATCGGGGC
Am**** --------------------------------------------------
Cm**** --------------------------------------------------
Gm**** ------------------------------------------------G-
Um**** --------------------------------------------------
m1A*** --------------------------------------------------
m5C*** ---------------------------C----------------------
m5U*** --------------------------------------------------
m6A*** --------------------------------------------------
m6Am** --------------------------------------------------
m7G*** --------------------------------------------------
Psi*** --------------------------------------------------
AtoI** --------------------------------------------------
************************ 51-100 nt***********************
Origin CGCGGATACCTGCTTTTTATTTTTTTTTCCTTAGCCCATCGGGGTATCGG
Am**** --------------------------------------------------
Cm**** C-C---------C---------------CC--------------------
Gm**** -G-G-------G------------------------------------GG
Um**** ---------------TTT-TTTTT--------------------------
m1A*** -----A------------A-------------------------------
m5C*** ---------C--C---------------CC--------------------
m5U*** -----------------T--TTTT---T----------------------
m6A*** --------------------------------------------------
m6Am** --------------------------------------------------
m7G*** --------------------------------------------------
Psi*** --------------------------------------------------
AtoI** --------------------------------------------------
************************101-150 nt***********************
Origin ATACCTGCTGATTCCCTTCCCCTCTGAACCCCCAACACTCTGGCCCATCG
Am**** --------------------------A-----------------------
Cm**** ------------------CCCC-C------CCC-----------------
Gm**** ------G--G-------------------------------G-------G
Um**** ----------------------T------------------------T--
m1A*** --------------------------A-----------------------
m5C*** -------------CCC--CC---C----C------C--------------
m5U*** ----------------------T-T----------------------T--
m6A*** --------------------------------------------------
m6Am** --------------------------------------------------
m7G*** --------------------------------------------------
Psi*** ----------------------T---------------------------
AtoI** --------------------------------------------------
************************151-200 nt***********************
Origin GGGTGACGGATATCTGCTTTTTAAAAATTTTCTTTTTTTGGCCCATCGGG
Am**** -----A---A------------A-A-------------------------
Cm**** ----------------C---------------------------------
Gm**** GGG-G--GG------G----------------------------------
Um**** ---T--------------TTTT-------T--------------------
m1A*** -----A---A-A----------AAAAA-----------------------
m5C*** -------------C--C---------------------------------
m5U*** ---T--------------TTTT-----TTTT-------------------
m6A*** --------------------------------------------------
m6Am** --------------------------------------------------
m7G*** --------------------------------------------------
Psi*** ---T----------------------------------------------
AtoI** -----A---A----------------------------------------
************************201-210 nt***********************
Origin GCTTCGGATA
Am**** ----------
Cm**** ----------
Gm**** ----------
Um**** ----------
m1A*** ----------
m5C*** ----------
m5U*** ----------
m6A*** ----------
m6Am** ----------
m7G*** ----------
Psi*** ----------
AtoI** ----------


*******************Visualize Attention*******************
************************  1- 50 nt***********************
Origin GGGGCCGTGGATACCTGCCTTTTAATTCTTTTTTATTCGCCCATCGGGGC
Am**** --------------------------------------------------
Cm**** ------------------------------------TCGC--ATCGG-GC
Gm**** --------------------------------TTATTCGC-CAT--GGGC
Um**** --------------------------------------------CGGGGC
m1A*** ----------------------------------ATT-------CGG---
m5C*** --------GGA---------------TCT-TTT----CGC---TCG----
m5U*** --------------------------------------------CGGGGC
m6A*** --------------------------------------------------
m6Am** --------------------------------------------------
m7G*** --------------------------------------------------
Psi*** --------------------------------------------------
AtoI** --------------------------------------------------
************************ 51-100 nt***********************
Origin CGCGGATACCTGCTTTTTATTTTTTTTTCCTTAGCCCATCGGGGTATCGG
Am**** --------------------------------------------------
Cm**** CGC----ACCTGC-------------TTCCTTAG------------TCGG
Gm**** CGCG-----CTG---------------------GCCCATCG-----TCGG
Um**** CGCG----------TTTTATTTTTT-TTC----------CGG-----CGG
m1A*** ---GGA-----------TAT--TTT----------CCA------------
m5C*** -------ACCTGC-------------TTCCTTAGCCC-TCGG----TCGG
m5U*** CGCGGATACC-----TTT-TTTTTT-TTC------------------CGG
m6A*** --------------------------------------------------
m6Am** --------------------------------------------------
m7G*** --------------------------------------------------
Psi*** -----------------------------------------------CGG
AtoI** --------------------------------------------------
************************101-150 nt***********************
Origin ATACCTGCTGATTCCCTTCCCCTCTGAACCCCCAACACTCTGGCCCATCG
Am**** --ACC-GCT---------------TGA-----CAA-----TGG------G
Cm**** --------TGATT-CCTTCCCCTCTGAACCCCCAA---------CCA-CG
Gm**** ----CTGCTG-TTC-------CTC-GAACCCCCAACACTCTGGCCC-TCG
Um**** --------TGA----------CTC-GAA-CCCCA-CAC--------ATCG
m1A*** ---CCT------------------TGA-------------TGGCCC----
m5C*** -----TGCTGATTCCCTTCCCCTCT--ACC----ACA----GGC-CATCG
m5U*** --ACC---TGAT---------CTCTGAA-CCCCA-CAC--------ATCG
m6A*** --------------------------------------------------
m6Am** --------------------------------------------------
m7G*** --------------------------------------------------
Psi*** --------TGA----------CTC-------CCA-CAC------------
AtoI** ----------------------------------ACA---TGG-------
************************151-200 nt***********************
Origin GGGTGACGGATATCTGCTTTTTAAAAATTTTCTTTTTTTGGCCCATCGGG
Am**** GGGTGA-GGA-----------TAAAA-TTT--------TGG---------
Cm**** G-------------TGC---------------------------------
Gm**** GGGTGACGG----CTG-----------------TTT--------------
Um**** GGGTGACG-ATA-CTG-TTTTTAA----TTT-------------------
m1A*** ---TGACGGATAT------TTTAAAAATTTTC---TTTTGGCCCAT----
m5C*** ------------TCTGC-TTT-----------------------------
m5U*** GGGT-ACGGATATCTGCTTTTTAA--ATTTT-TTT---------------
m6A*** --------------------------------------------------
m6Am** --------------------------------------------------
m7G*** --------------------------------------------------
Psi*** -GGT----------------------------------------------
AtoI** ---TGA-GGA-------------AAA-TTT--------------------
************************201-210 nt***********************
Origin GCTTCGGATA
Am**** ----------
Cm**** ----------
Gm**** ----------
Um**** ----------
m1A*** ----------
m5C*** ----------
m5U*** ----------
m6A*** ----------
m6Am** ----------
m7G*** ----------
Psi*** ----------
AtoI** ----------

You also can check detailed descriptions by turning verbose into True:

python main.py -s GGGGCCGTGGATACCTGCCTTTTAATTCTTTTTTATTCGCCCATCGGGGCCGCGGATACCTGCTTTTTATTTTTTTTTCCTTAGCCCATCGGGGTATCGGATACCTGCTGATTCCCTTCCCCTCTGAACCCCCAACACTCTGGCCCATCGGGGTGACGGATATCTGCTTTTTAAAAATTTTCTTTTTTTGGCCCATCGGGGCTTCGGATA --top 3 --att_window=3 --alpha=0.1 --gpu=0 --verbose=True

Additionally ,you can save these results by turning save to True.

Train your own MultiRM from scratch

python train.py --mode='train' --use_embedding=True --epochs=10 --lr=0.001 --batch_size=64 --length=51

For reference, please check our training data profie

multirm's People

Contributors

shashivish avatar tsedao avatar

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