Nanom6A
Quantitative profiling of N6-methyladenosine at single-base resolution using Nanopore direct RNA sequencing
The default model is applicable only to "MinION or GridION R9.4.1 flowcells".
Gao, Y., Liu, X., Wu, B., Wang, H., Xi, F., Kohnen, M. V., ... & Gu, L. (2021). Quantitative profiling of N 6-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing. Genome Biology, 22(1), 1-17.
Install quick-start
In order to make it easy to install Nanom6A, we provided three different methods for users.
(1). Installing a pre-compiled binary release (the first method to use nanom6A)
To use the binary package, simply download the pre-compiled Linux binary from following link: https://drive.google.com/drive/folders/1Dodt6uJC7lBihSNgT3Mexzpl_uqBagu0?usp=sharing
Users can untar nanom6A_2021_10_22.tar.gz, and make sure the binaries in your PATH environment variable.
Testing the pre-compiled binary installation:
tar -xvzf nanom6A_2021_10_22.tar.gz
cd nanom6A_2021_10_22
sh run_binary.sh
User may still need sam2tsv in your $PATH (after 2021_10_22 version), you can install it through conda.
conda install -c hcc jvarkit-sam2tsv
We tested pre-compiled binary release in ubuntu and centos.
For ubuntu 16.04, user may need to install libgomp1 and libxcb1:
apt install libgomp1
apt install libxcb1
For ubuntu 20.10, user may need to install libncurses5.
apt install libncurses5
For centos 8.2, user may need to install ncurses.
yum install ncurses*
(2). Testing Nanom6A from source (the second method to use nanom6A)
In order to test nanopore from source code, you can install the dependence through conda.
Firstly, user can install miniconda or conda
wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py37_4.8.3-Linux-x86_64.sh
chmod 777 Miniconda3-py37_4.8.3-Linux-x86_64.sh
./Miniconda3-py37_4.8.3-Linux-x86_64.sh
# After installation, please close the shell terminal and open a new one.
Install conda environment
user may need to install libxrender and libxext.
apt install -y libxrender-dev
apt install -y libxext-dev
tar -xvzf nanom6A_2021_10_22.tar.gz
cd nanom6A_2021_10_22
conda env create -f conda.yml #install conda environment
Following list was the detailed dependence:
Source code dependence
| soft or module | version |
|---|---|
| bedtools | v2.29.2 |
| samtools | 1.3.1 |
| minimap2 | 2.17-r941 |
| python | 3.7.3 |
| h5py | 2.9.0 |
| statsmodels | 0.10.0 |
| joblib | 0.16.0 |
| xgboost | 0.80 |
| pysam | 0.16.0.1 |
| tqdm | 4.39.0 |
| pycairo | 1.19.1 |
| scikit-learn | 0.22 |
Testing the installation (Please make sure the dependence was installed).
tar -xvzf nanom6A_2021_10_22.tar.gz
cd nanom6A_2021_10_22
sh run_source_code.sh
(3). Testing docker (the third method to use nanom6A)
This is the simplest way to use nanom6A and save users from occasionally frustrating process
sudo docker pull gaoyubang/nanom6a:v1
Testing the Docker:
tar -xvzf nanom6A_2021_10_22.tar.gz
cd nanom6A_2021_10_22
sudo docker run -it -v `pwd`:/data gaoyubang/nanom6a:v1 /bin/bash
cd /data/
sh run_docker.sh
FAQ
- The screen/nohup log file might show following output:
Fontconfig error: Cannot load default config file
The incompatibility from cairo library caused this problem,which donot obstruct m6A identification. User can fix this issue using following method: alacritty/alacritty#2675
Manual for Nanom6A
1. Preproccess
Basecalling using guppy (version 3.6.1)
guppy_basecaller -i $f5 -s guppy --num_callers 40 --recursive --fast5_out --config rna_r9.4.1_70bps_hac.cfg
Convert merged single big fast5 into small size fast5 file
When the fast5 file was stored in multi_read formats, this step is required (mostly seuqenced with SQK-RNA002 kit). You can check the size of one fast5 file, if it's about ~300MB, this step is required. If it's about ~100KB, You have to skip this step to avoid error.
multi_to_single_fast5 -i guppy -s single -t 40 --recursive
resquiggle raw signals
The tombo resquiggle referance.transcript.fa should not be genome file, it should be the referance gene fasta file.
tombo resquiggle --overwrite --basecall-group Basecall_1D_001 single referance.transcript.fa --processes 40 --fit-global-scale --include-event-stdev
list all fast5 file
find single -name "*.fast5" >files.txt
- identification of m6A sites based on DRS reads
extracting signals
extract_raw_and_feature_fast --cpu=20 --fl=files.txt -o result --clip=10
predicting m6A site
the -g option should be the genome file.
predict_sites --cpu 20 -i result -o result_final -r data/ref.fa -g data/anno.fa -b data/gene2transcripts.txt
(1) the -r parameter is file of referance transcripts sequence.
>NM_001354612.2 CDS=54-1631
ggggccacgctgcgggcccgggccatggccgccgccgatgccgagAGACACCTATGGCTGCCGATGAAGGCTCAGCAGAGAAACA ....
(2) the -g parameter: please check your genome file index, make sure you index with samtools index ref.fa and picard CreateSequenceDictionary R=ref.fa O=ref.dict
(3) the -b parameter: the Gene information corresponding to each reference transcript.
EHMT1 NM_001354612.2 NM_001354611.2 NM_001145527.2 NM_001354259.2 NM_001354263.2 NM_024757.5
The main output is the ratio.x.tsv in the output dir. The header of ratio.x.tsv.
| gene|chrom | coordinate|mod number|total number|mod ratio |
|---|---|
| ACTB|chr7 | 5566813|162|639.0|0.2535211267605634 |
The genome_abandance.x.bed is the temp file without fillter using --support, you can fillter it with following cmd
wget https://raw.githubusercontent.com/gaoyubang/nanom6A/main/fillter_output.py
python fillter_output.py result_final/ratio.x.tsv result_final/genome_abandance.x.bed
The output result_final/genome_abandance.x.bed.filler.bed is the filltered output.
The header of genome_abandance.x.bed.filler.bed.
| chrom | coordinate | gene | read id | read pos | kmer |
|---|---|---|---|---|---|
| chr7 | 5567320 | ACTB | e88129423ae1.fast5 | 1257 | AAACA |
3. Visualization of m6A sites
nanoplot --input result_final -o plot_nano_plot
4. Train your own model
python train.py
The fast5 raw file of nanopore direct RNA sequence in our published studies:
repeat1:
https://sra-download.ncbi.nlm.nih.gov/traces/sra36/SRZ/012881/SRR12881185/poplar_guppy_recall.tar.gz
repeat2:
https://sra-download.ncbi.nlm.nih.gov/traces/sra68/SRZ/012822/SRR12822922/Ptr-WT-SDX-20200827.tar
Acknowledgement
2020.11.6 16:56 Fuzhou
LiuFuyuxaing helped me with testing the code and improvement of the Manual!
2021.3.4 22:00 Fuzhou
Fixed bugs due to difference between tombo aligned sequence and minimap2 aligned sequence.
Fixed bugs due to difference between Sam2tsv and samtools depth.
Thank you to Hang Qin from Institute of Plant Physiology and Ecology for bringing this to our attention!
2021.3.5 12:00 Fuzhou
Update binary and docker version due to bugs finding at 2021.3.4!
2021.3.11 22:00 Fuzhou
Fixed bugs lead to 1-based m6A sites in negative strand , Thank you to Yan Xin from The University of Hong Kong for bringing this to our attention!
2021.3.18 19:00 Fuzhou
Fixed bugs due to samtools depth default 8000 maximum coverage!
2021.10.22 12:15 Fuzhou
Fixed bugs due to overlap genes!
All suggestions are welcome to lfgu@fafu.edu.cn or yubanggaofafu@gmail.com