About CHEUI
CHEUI (Methylation (CH3) Estimation Using Ionic current) is an RNA modification detection software for Oxford Nanopore direct RNA sequencing data. CHEUI can be used to detect m6A and m5C in individual reads at single-nucleotide resolution from any sample (e.g. single condition), or detect differential m6A or m5C between any two conditions. CHEUI uses a two-stage deep learning method to detect m6A and m5C transcriptome-wide at single-read and single-site resolution in any sequence context (i.e. without any sequence constrains).
CHEUI is open source and freely available under an Academic Public License (see copy of the license in this repository).
- Dependencies
- Outline of CHEUI-solo and CHEUI-diff
- Preprocessing data before running CHEUI
- Install CHEUI
- IMPORTANT
- Detect m6A and m5C modifications in one condition
- Identify differential RNA modifications between two conditions
python=3.7
numpy==1.19.2
pandas==1.3.4
tensorflow-gpu==2.4.1
keras-preprocessing==1.1.2
Before running CHEUI:
- Raw signal data (fast5) should be basecalled using Guppy 4.0.11+ (4.0.11 or later) (https://community.nanoporetech.com/downloads/guppy/)(basecaller model used template_rna_r9.4.1_70bps*)
- Basecalled sequences (fastq) should be aligned to a reference transcriptome using minimap2 and primary, positive strand alignments should be selected, e.g.
minimap2 -ax map-ont -k14 <transcriptome fasta> <read fastq> | samtools view -F 2324 -b > <sorted-bam-file>
samtools index <sorted-bam-file>
- Signal data should be resquiggled to aligned sequences using Nanopolish (https://nanopolish.readthedocs.io/en/latest/), ensuring that events are rescaled, e.g.
nanopolish index -s <sequencing_summary.txt> -d <fast5_folder> <read fastq>
nanopolish eventalign -t 48 \
--reads <read fastq> \
--bam <sorted-bam-file> \
--genome <transcriptome fasta> \
--scale-events --signal-index --samples --print-read-names > nanopolish_out.txt
Installation can be performed manually or using Conda (recommended).
Manual installation:
git clone https://github.com/comprna/CHEUI.git
cd CHEUI/test
Conda installation with manual CUDA installation (recommended):
conda create --name cheui python=3.7 tensorflow-gpu=2.4.1 pandas=1.3.4 -y && conda activate cheui
git clone https://github.com/comprna/CHEUI.git
cd CHEUI/test
Conda installation with integrated CUDA installation (not recommended):
conda create --name cheui python=3.7 tensorflow-gpu=2.4.1 pandas=1.3.4 conda-forge::cudatoolkit-dev -y && conda activate cheui
git clone https://github.com/comprna/CHEUI.git
cd CHEUI/test
Please follow the instructions below carefully.
-
Notice that for detecting m6A or m5C, the nanopolish output files require different preprocessing scripts:
CHEUI_preprocess_m6A.pyfor m6A andCHEUI_preprocess_m5C.pyfor m5C. -
CHEUI model 1 (read level predictions) and model 2 (site level predictions) use different predictive models for m6A and m5C that have to be specified using the --DL_model flag:
for m6A: ```../CHEUI_trained_models/CHEUI_m6A_model1.h5``` and ```../CHEUI_trained_models/CHEUI_m6A_model2.h5``` For m5C: ```../CHEUI_trained_models/CHEUI_m5C_model1.h5``` and ```../CHEUI_trained_models/CHEUI_m5C_model2.h5```
This script takes the output from nanopolish and creates a file containing signals corresponding to 9-mers centered in As and IDs.
../scripts/CHEUI_preprocess_m6A.py --help
required arguments:
-i, --input_nanopolish Nanopolish output file. Nanopolish should be run with the following flags:
nanopolish eventalign --reads <in.fasta>--bam
<in.bam> --genome <genome.fa> --print-read-names--
scale-events --samples > <out.txt>
-m, --kmer_model file containing the expected signal k-mer means
(available at CHEUI/kmer_models/model_kmer.csv)
-o, --out_dir output directory
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
-s <str>, --suffix_name <str>
name to use for output files
-n CPU, --cpu CPU Number of CPUs (threads) to use
Example command of the preprocessing step for m6A:
python3 ../scripts/CHEUI_preprocess_m6A.py -i nanopolish_output_test.txt -m ../kmer_models/model_kmer.csv -o out_A_signals+IDs.p -n 15
The processing of the Nanopolish output for m5C is very similar:
../scripts/CHEUI_preprocess_m5C.py --help
required arguments:
-i, --input_nanopolish Nanopolish output file. Nanopolish should be run with the following flags:
nanopolish eventalign --reads <in.fasta>--bam
<in.bam> --genome <genome.fa> --print-read-names--
scale-events --samples > <out.txt>
-m, --kmer_model file containing the expected signal k-mer means
(available at CHEUI/kmer_models/model_kmer.csv)
-o, --out_dir output directory
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
-s <str>, --suffix_name <str>
name to use for output files
-n CPU, --cpu CPU Number of cores to use
Example command of the preprocessing step for m5C:
python3 ../scripts/CHEUI_preprocess_m5C.py -i nanopolish_output_test.txt -m ../kmer_models/model_kmer.csv -o out_C_signals+IDs.p -n 15
A faster method to run the CHEUI preprocessing step. The C++ version is 2-10x times faster than the python version.
Installation
cd ../scripts/preprocessing_CPP/
./build.sh
Parameters of the program
$ ./CHEUI -h
required arguments:
-i, --input-nanopolish Nanopolish output file. Nanopolish should be run with the following flags:
nanopolish eventalign --reads <in.fasta>--bam
<in.bam> --genome <genome.fa> --print-read-names--
scale-events --samples > <out.txt>
-m, --kmer-model file containing the expected signal k-mer means
(available at CHEUI/kmer_models/model_kmer.csv)
-o, --out-dir output directory
--m6A/--m5C preprocessing type
optional arguments:
-h, --help show this help message and exit
-s <str>, --suffix_name <str>
name to use for output files
-n CPU, --cpu CPU Number of cores to use
-t, --temp-dir temp file directory (default: out dir)
Example command of the preprocessing step for m6A:
./CHEUI -i ../../test/nanopolish_output_test.txt -o ../../test/out_A_signals+IDs.p/ -m ../../kmer_models/model_kmer.csv -n 16 --m6A
Example command of the preprocessing step for m5C:
./CHEUI -i ../../test/nanopolish_output_test.txt -o ../../test/out_C_signals+IDs.p/ -m ../../kmer_models/model_kmer.csv -n 16 --m5C
For large nanopolish file, we recommend to split the file into smaller files and run the preprocessing step, then using the following command to combine the outputs
python3 ../scripts/combine_binary_file.py -i [output binary folder] -o [combined output file name]
CHEUI model 1 takes the output from the previous step of preprocessing signals and calculates with the m6A_model_1 the m6A methylation probability in individual read signals at each individual A nucleotide, and with the m5C_model_1 the m5C methylation probability in individual read signals at each individual C nucleotide.
../scripts/CHEUI_predict_model1.py --help
required arguments:
-i, --signals_input path to the ID+signal file
-m, --DL_model path to the trained (m6A or m5C) model 1
-l LABEL, --label LABEL label of the condition of the sample, e.g. WT_rep1
-o, --file_out Path to the output file
-r, --resume Continue running predictions
optional arguments:
-h, --help show help message and exit
-v, --version show program's version number and exit
Example command for the prediction of m6A in individual reads at each A nucleotide:
python ../scripts/CHEUI_predict_model1.py -i out_A_signals+IDs.p/nanopolish_output_test_signals+IDS.p -m ../CHEUI_trained_models/CHEUI_m6A_model1.h5 -o ./read_level_m6A_predictions.txt -l WT_rep1
Example command for the prediction of m5C in individual reads at each C nucleotide:
python ../scripts/CHEUI_predict_model1.py -i out_C_signals+IDs.p/nanopolish_output_test_signals+IDS.p -m ../CHEUI_trained_models/CHEUI_m5C_model1.h5 -o ./read_level_m5C_predictions.txt -l WT_rep1
The output from model 1 needs to be sorted before using model 2 to predict m6A (or m5C) at transcriptomic sites. We provide below two ways of doing this depending of whether you have one or more replicates:
sort -k1 --parallel=15 ./read_level_m6A_predictions.txt > ./read_level_m6A_predictions_sorted.txt
sort -k1 --parallel=15 ./read_level_m5C_predictions.txt > ./read_level_m5C_predictions_sorted.txt
In case there are N replicates and you want to combine them for model 2:
cat ./read_level_m6A_predictions_1.txt ... ./read_level_m6A_predictions_N.txt >> ./read_level_m6A_predictions_combined.txt
cat ./read_level_m5C_predictions_1.txt ... ./read_level_m5C_predictions_N.txt >> ./read_level_m5C_predictions_combined.txt
then sort these files:
sort -k1 --parallel=15 ./read_level_m6A_predictions_combined.txt > ./read_level_m6A_predictions_sorted.txt
sort -k1 --parallel=15 ./read_level_m5C_predictions_combined.txt > ./read_level_m5C_predictions_sorted.txt
CHEUI model 2 calculates the probability and stoichiometry for m6A (or m5C) at each transcriptomic site: each site from the transcriptome reference used for mapping above:
../scripts/CHEUI_predict_model2.py
-i, --input path to read-level prediction file from CHEUI_predict_model1.py
-m, --DL_model path to pretrained model 2
-c, --cutoff model 2 probability cutoff for printing tested sites. Default value: 0
-d, --double_cutoff Model 1 probability double cutoff used to calculate the
stoichiometry. Default values: 0.3 and 0.7 (<0.3 is considered not-modified,
>0.7 is considered modified, and all other reads are ignored).
-n, --min_reads Minimun number of reads in a site to include in the analysis. Default value: 20
-o, --file_out Path to the output file
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
Example command for the prediction of m6A probability and stoichiometry at every A nucleotide site in the reference transcriptome:
python3 ../scripts/CHEUI_predict_model2.py -i read_level_m6A_predictions_sorted.txt -m ../CHEUI_trained_models/CHEUI_m6A_model2.h5 -o site_level_m6A_predictions.txt
Example command for the prediction of m5C probability and stoichiometry at every C nucleotide site in the reference transcriptome:
python3 ../scripts/CHEUI_predict_model2.py -i read_level_m5C_predictions_sorted.txt -m ../CHEUI_trained_models/CHEUI_m5C_model2.h5 -o site_level_m5C_predictions.txt
Example of the read-level prediction for m6A file generated using ../scripts/CHEUI_predict_model1.py
The file contains 3 columns, the first column contains information about contig/transcriptome_location_k-mer_readID. Second column contains the read levele and k-mer probability of the middle A (in this example) to be methylated, the third column contains the label of the sample:
ENST00000000233.10_1003_TTGTAGATG_3386eb53-8805-4c11-a721-02a23fc73cb4 0.39094510674476624 WT_1
ENST00000000233.10_1007_TTGCAGAAA_87b56740-d8db-4d17-8cd1-aa5019d4750b 0.58213871717453 WT_1
ENST00000000233.10_133_TGTGAAGAA_06685ba0-c2f9-4540-9805-3e1746df432f 0.08152690529823303 WT_1
ENST00000000412.8_2120_GGCGATGAC_18dad7fd-796a-4f1a-a242-27e4c5226234 0.5041788816452026 WT_1
ENST00000000412.8_2120_GGCGATGAC_a760a4ac-597e-4f57-892e-37eb0a6e1c56 0.19357600808143616 WT_1
ENST00000000412.8_2120_GGCGATGAC_397c1862-b29c-4dd1-93a7-753da410535b 0.42834094166755676 WT_1
An example of the site-level prediction file for m6A generated using ../scripts/CHEUI_predict_model2.py
This file is a tab separated file containing; contig, position, site, coverage, stoichiometry of the site and probability of the site being methylated:
contig position site coverage stoichiometry probability
ENST00000000233.10 1003 CTTGAGTAA 648 0.10132158 0.11857438
ENST00000000233.10 1003 CTTGAGTAA 648 0.10132158 0.11857438
ENST00000000233.10 1007 AGTAATAAA 628 0.32236842 0.54757184
ENST00000000233.10 1333 AAGCAGATG 467 0.25602409 0.3631263
ENST00000000412.8 2120 AGAAACCTG 63 0.05 0.038466703
ENST00000000412.8 2126 CTGGACTGA 68 0.92424242 0.9999988
ENST00000000412.8 2130 ACTGATCTT 67 0.05 0.08036163
First use CHEUI_preprocess_m6A to preprocess signals centerd in A for both replicates.
For m6A:
python3 ../scripts/CHEUI_preprocess_m6A.py -i nanopolish_X_output_test.txt -m ../kmer_models/model_kmer.csv -o condition_X_m6A_signals+IDs.p -n 15
python3 ../scripts/CHEUI_preprocess_m6A.py -i nanopolish_Y_output_test.txt -m ../kmer_models/model_kmer.csv -o condition_Y_m6A_signals+IDs.p -n 15
For m5C:
python3 ../scripts/CHEUI_preprocess_m5C.py -i nanopolish_X_output_test.txt -m ../kmer_models/model_kmer.csv -o condition_X_m5C_signals+IDs.p -n 15
python3 ../scripts/CHEUI_preprocess_m5C.py -i nanopolish_Y_output_test.txt -m ../kmer_models/model_kmer.csv -o condition_Y_m5C_signals+IDs.p -n 15
When using ../scripts/CHEUI_predict_model1.py please choose the correct label for each condition. Later the label name will be used for the differential methylation config file.
Run CHEUI_predict_model1 that takes the preprocessed signals and calculates m6A (m5C) probability per individual signal for the two conditions.
Example for m6A for the two conditions (note the use of different labels):
python ../scripts/CHEUI_predict_model1.py -i condition_X_m6A_signals+IDs.p/nanopolish_output_test_signals+IDS.p -m ../CHEUI_trained_models/CHEUI_m6A_model1.h5 -o condition_X_m6A_read_level_predictions.txt -l X_rep1
python ../scripts/CHEUI_predict_model1.py -i condition_Y_m6A_signals+IDs.p/nanopolish_output_test_signals+IDS.p -m ../CHEUI_trained_models/CHEUI_m6A_model1.h5 -o condition_Y_m6A_read_level_predictions.txt -l Y_rep1
cat condition_X_m6A_read_level_predictions.txt condition_Y_m6A_read_level_predictions.txt > CHEUI_m6A_read_level_X_Y.txt
sort -k1 --parallel=20 CHEUI_m6A_read_level_X_Y.txt > CHEUI_m6A_read_level_X_Y.sorted.txt
First write a config.yml file to provide the information about the conditions and replicates
Example of config.yml:
# path to input
input: ./CHEUI_m6A_read_level_X_Y.sorted.txt
# sample labels used to run /scripts/CHEUI_predict_model1.py
sample_labels:
condition1:
rep1: X_rep1
condition2:
rep1: Y_rep1
# cutoff used to classify methylated reads
upper_cutoff: 0.7
# cutoff used to classify unmethylated reads
lower_cutoff: 0.3
out: ./CHEUI_differentialRNAMod_X_vs_Y.txt
Example command to run CHEUI-diff
python3 ../scripts/CHEUI_diffenrentialRNAMod.py -c config.yml
ID coverage_1 coverage_2 stoichiometry_1 stoichiometry_2 stoichiometry_diff statistic pval_U
txt1_212_TGGCAAATT 21 21 0.06666666666666667 0.06666666666666667 0.0 0.0 1.0
txt1_237_TGCTATGCC 24 24 0.0 0.0 0.0 0.0 1.0
txt1_255_CGGGACTTT 23 23 0.0 0.0 0.0 0.0 1.0
txt1_262_TTTGAAGAA 24 24 0.0 0.0 0.0 0.0 1.0