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https://github.com/bioinfomaticsCSU/deepsignal
Detecting methylation using signal-level features from Nanopore sequencing reads
https://github.com/bioinfomaticsCSU/deepsignal
bioinformatics epigenetics methylation nanopore-sequencing tensorflow
Last synced: about 2 months ago
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Detecting methylation using signal-level features from Nanopore sequencing reads
- Host: GitHub
- URL: https://github.com/bioinfomaticsCSU/deepsignal
- Owner: bioinfomaticsCSU
- License: gpl-3.0
- Created: 2018-12-11T20:19:10.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-06-04T08:27:40.000Z (about 1 year ago)
- Last Synced: 2024-05-12T20:48:27.569Z (about 2 months ago)
- Topics: bioinformatics, epigenetics, methylation, nanopore-sequencing, tensorflow
- Language: Python
- Homepage:
- Size: 304 KB
- Stars: 107
- Watchers: 3
- Forks: 21
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-nanopore - DeepSignals - [Python] - [DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning](https://academic.oup.com/bioinformatics/article/35/22/4586/5474907). (Software packages / DNA modification analysis)
README
# News
- 2021.03.15: We developed [deepsignal2](https://github.com/PengNi/deepsignal2). Compared to deepsignal, deepsignal2 has much smaller DNN model in size, and slightly better performance in 5mCpG detection of human.
# DeepSignal
[](https://www.python.org/)
[](https://pypi.org/project/deepsignal/)
[](https://github.com/bioinfomaticsCSU/deepsignal/blob/master/LICENSE)
[](https://pepy.tech/project/deepsignal)
[](https://pepy.tech/project/deepsignal/month)
## A deep-learning method for detecting DNA methylation state from Oxford Nanopore sequencing reads.
DeepSignal constructs a BiLSTM+Inception structure to detect DNA methylation state from Nanopore reads. It is
built with **Tensorflow** and **Python 3**.
## Contents
- [Installation](#Installation)
- [Trained models](#Trained-models)
- [Example data](#Example-data)
- [Quick start](#Quick-start)
- [Usage](#Usage)
## Installation
deepsignal is built on Python3. [tombo](https://github.com/nanoporetech/tombo) is required to re-squiggle the raw signals from nanopore reads before running deepsignal.
- Prerequisites:\
[Python 3.*](https://www.python.org/)\
[tensorflow](https://www.tensorflow.org/) (1.8.0<=tensorflow<=1.13.1)\
[tombo](https://github.com/nanoporetech/tombo)
- Dependencies:\
[numpy](http://www.numpy.org/)\
[h5py](https://github.com/h5py/h5py)\
[statsmodels](https://github.com/statsmodels/statsmodels/)\
[scikit-learn](https://scikit-learn.org/stable/)
#### 1. Create an environment
We highly recommend using a virtual environment for the installation of deepsignal and its dependencies. A virtual environment can be created and (de)activated as follows by using [conda](https://conda.io/docs/):
```bash
# create
conda create -n deepsignalenv python=3.7
# activate
conda activate deepsignalenv
# deactivate
conda deactivate
```
The virtual environment can also be created by using [virtualenv](https://github.com/pypa/virtualenv/).
#### 2. Install deepsignal
- After creating and activating the environment, download and install deepsignal (**latest version**) from github:
```bash
git clone https://github.com/bioinfomaticsCSU/deepsignal.git
cd deepsignal
python setup.py install
```
**or** install deepsignal using *pip*:
```bash
pip install deepsignal
```
- [tombo](https://github.com/nanoporetech/tombo) is required to be installed in the same environment:
```bash
# install using conda
conda install -c bioconda ont-tombo
# or install using pip
pip install ont-tombo
```
- install [tensorflow](https://www.tensorflow.org/) (version: 1.8.0<=tensorflow<=1.13.1) in the same environment:
```bash
# install using conda
conda install -c anaconda tensorflow==1.13.1
# or install using pip
pip install 'tensorflow==1.13.1'
```
If a GPU-machine is used, install the gpu version of tensorflow. The cpu version is not required:
```bash
# install using conda
conda install -c anaconda tensorflow-gpu==1.13.1
# or install using pip
pip install 'tensorflow-gpu==1.13.1'
```
## Trained models
The models we trained can be downloaded from [google drive](https://drive.google.com/open?id=1zkK8Q1gyfviWWnXUBMcIwEDw3SocJg7P).
Currently we have trained the following models:
* _model.CpG.R9.4_1D.human_hx1.bn17.sn360.v0.1.7+.tar.gz_: A CpG model trained using HX1 R9.4 1D reads (for **deepsignal>=0.1.7**).
* ~~_model.CpG.R9.4_1D.human_hx1.bn17.sn360.tar.gz_: A CpG model trained using HX1 R9.4 1D reads (for **deepsignal<=0.1.6**).~~
* ~~_model.GATC.R9_2D.tem.puc19.bn17.sn360.tar.gz_: A G*A*TC model trained using pUC19 R9 2D template reads (for **deepsignal<=0.1.6**).~~
## Example data
The example data can be downloaded from [google drive](https://drive.google.com/open?id=1zkK8Q1gyfviWWnXUBMcIwEDw3SocJg7P).
* ~~_fast5s.sample.tar.gz_: The data contain ~4000 yeast R9.4 1D reads each with called events (basecalled by Albacore), along with a genome reference.~~
## Quick start
To call modifications, the raw fast5 files should be basecalled ([Guppy or Albacore](https://nanoporetech.com/community)) and then be re-squiggled by [tombo](https://github.com/nanoporetech/tombo). At last, modifications of specified motifs can be called by deepsignal. The following are commands to call 5mC in CG contexts from the example data:
```bash
# 1. guppy basecall
guppy_basecaller -i fast5s.al -r -s fast5s.al.guppy --config dna_r9.4.1_450bps_hac_prom.cfg
cat fast5s.al.guppy/*.fastq > fast5s.al.guppy.fastq
# 2. tombo resquiggle
tombo preprocess annotate_raw_with_fastqs --fast5-basedir fast5s.al --fastq-filenames fast5s.al.guppy.fastq --sequencing-summary-filenames fast5s.al.guppy/sequencing_summary.txt --basecall-group Basecall_1D_000 --basecall-subgroup BaseCalled_template --overwrite --processes 10
tombo resquiggle fast5s.al GCF_000146045.2_R64_genomic.fna --processes 10 --corrected-group RawGenomeCorrected_001 --basecall-group Basecall_1D_000 --overwrite
# 3. deepsignal call_mods
deepsignal call_mods --input_path fast5s.al/ --model_path model.CpG.R9.4_1D.human_hx1.bn17.sn360.v0.1.7+/bn_17.sn_360.epoch_9.ckpt --result_file fast5s.al.CpG.call_mods.tsv --corrected_group RawGenomeCorrected_001 --nproc 10 --is_gpu no
python /path/to/deepsignal/scripts/call_modification_frequency.py --input_path fast5s.al.CpG.call_mods.tsv --result_file fast5s.al.CpG.call_mods.frequency.tsv
```
## Usage
#### 1. Basecall and re-squiggle
Before run deepsignal, the raw reads should be basecalled ([Guppy or Albacore](https://nanoporetech.com/community)) and then be processed by the *re-squiggle* module of [tombo](https://github.com/nanoporetech/tombo).
Note:
- If the fast5 files are in multi-read FAST5 format, please use _multi_to_single_fast5_ command from the [ont_fast5_api package](https://github.com/nanoporetech/ont_fast5_api) to convert the fast5 files first (Ref to [issue #173](https://github.com/nanoporetech/tombo/issues/173) in [tombo](https://github.com/nanoporetech/tombo)).
```bash
multi_to_single_fast5 -i $multi_read_fast5_dir -s $single_read_fast5_dir -t 30 --recursive
```
- If the basecall results are saved as fastq, run the [*tombo proprecess annotate_raw_with_fastqs*](https://nanoporetech.github.io/tombo/resquiggle.html) command before *re-squiggle*.
For the example data:
```bash
# 1. basecall
guppy_basecaller -i fast5s.al -r -s fast5s.al.guppy --config dna_r9.4.1_450bps_hac_prom.cfg
# 2. proprecess fast5 if basecall results are saved in fastq format
cat fast5s.al.guppy/*.fastq > fast5s.al.guppy.fastq
tombo preprocess annotate_raw_with_fastqs --fast5-basedir fast5s.al --fastq-filenames fast5s.al.guppy.fastq --sequencing-summary-filenames fast5s.al.guppy/sequencing_summary.txt --basecall-group Basecall_1D_000 --basecall-subgroup BaseCalled_template --overwrite --processes 10
# 3. resquiggle, cmd: tombo resquiggle $fast5_dir $reference_fa
tombo resquiggle fast5s.al GCF_000146045.2_R64_genomic.fna --processes 10 --corrected-group RawGenomeCorrected_001 --basecall-group Basecall_1D_000 --overwrite
```
#### 2. extract features
Features of targeted sites can be extracted for training or testing.
For the example data (deepsignal extracts 17-mer-seq and 360-signal features of each CpG motif in reads by default. Note that the value of *--corrected_group* must be the same as that of *--corrected-group* in tombo.):
```bash
deepsignal extract --fast5_dir fast5s.al/ --write_path fast5s.al.CpG.signal_features.17bases.rawsignals_360.tsv --corrected_group RawGenomeCorrected_001 --nproc 10
```
The extracted_features file is a tab-delimited text file in the following format:
- **chrom**: the chromosome name
- **pos**: 0-based position of the targeted base in the chromosome
- **strand**: +/-, the aligned strand of the read to the reference
- **pos_in_strand**: 0-based position of the targeted base in the aligned strand of the chromosome (_legacy column, not necessary for downstream analysis_)
- **readname**: the read name
- **read_strand**: t/c, template or complement
- **k_mer**: the sequence around the targeted base
- **signal_means**: signal means of each base in the kmer
- **signal_stds**: signal stds of each base in the kmer
- **signal_lens**: lens of each base in the kmer
- **cent_signals**: the central signals of the kmer
- **methy_label**: 0/1, the label of the targeted base, for training
#### 3. call modifications
The extracted features can be used to call modifications as follows (If a GPU-machine is used, set *--is_gpu* to "yes".):
```bash
# the CpGs are called by using the CpG model of HX1 R9.4 1D
deepsignal call_mods --input_path fast5s.al.CpG.signal_features.17bases.rawsignals_360.tsv --model_path model.CpG.R9.4_1D.human_hx1.bn17.sn360.v0.1.7+/bn_17.sn_360.epoch_9.ckpt --result_file fast5s.al.CpG.call_mods.tsv --nproc 10 --is_gpu no
```
**The modifications can also be called from the fast5 files directly**:
```bash
# use CPU
deepsignal call_mods --input_path fast5s.al/ --model_path model.CpG.R9.4_1D.human_hx1.bn17.sn360.v0.1.7+/bn_17.sn_360.epoch_9.ckpt --result_file fast5s.al.CpG.call_mods.tsv --corrected_group RawGenomeCorrected_001 --nproc 10 --is_gpu no
# or use GPU
CUDA_VISIBLE_DEVICES=0 deepsignal call_mods --input_path fast5s.al/ --model_path model.CpG.R9.4_1D.human_hx1.bn17.sn360.v0.1.7+/bn_17.sn_360.epoch_9.ckpt --result_file fast5s.al.CpG.call_mods.tsv --corrected_group RawGenomeCorrected_001 --nproc 10 --is_gpu yes
```
The modification_call file is a tab-delimited text file in the following format:
- **chrom**: the chromosome name
- **pos**: 0-based position of the targeted base in the chromosome
- **strand**: +/-, the aligned strand of the read to the reference
- **pos_in_strand**: 0-based position of the targeted base in the aligned strand of the chromosome (_legacy column, not necessary for downstream analysis_)
- **readname**: the read name
- **read_strand**: t/c, template or complement
- **prob_0**: [0, 1], the probability of the targeted base predicted as 0 (unmethylated)
- **prob_1**: [0, 1], the probability of the targeted base predicted as 1 (methylated)
- **called_label**: 0/1, unmethylated/methylated
- **k_mer**: the kmer around the targeted base
A modification-frequency file can be generated by the script [scripts/call_modification_frequency.py](https://github.com/bioinfomaticsCSU/deepsignal/blob/master/scripts/call_modification_frequency.py) with the modification_call file:
```bash
python /path/to/deepsignal/scripts/call_modification_frequency.py --input_path fast5s.al.CpG.call_mods.tsv --result_file fast5s.al.CpG.call_mods.frequency.tsv --prob_cf 0
```
The modification_frequency file is a tab-delimited text file in the following format:
- **chrom**: the chromosome name
- **pos**: 0-based position of the targeted base in the chromosome
- **strand**: +/-, the aligned strand of the read to the reference
- **pos_in_strand**: 0-based position of the targeted base in the aligned strand of the chromosome (_legacy column, not necessary for downstream analysis_)
- **prob_0_sum**: sum of the probabilities of the targeted base predicted as 0 (unmethylated)
- **prob_1_sum**: sum of the probabilities of the targeted base predicted as 1 (methylated)
- **count_modified**: number of reads in which the targeted base counted as modified
- **count_unmodified**: number of reads in which the targeted base counted as unmodified
- **coverage**: number of reads aligned to the targeted base
- **modification_frequency**: modification frequency
- **k_mer**: the kmer around the targeted base
#### 4. train new models
A new model can be trained as follows:
```bash
# need two independent datasets for training and validating
# use deepsignal train -h/--help for more details
deepsignal train --train_file /path/to/train_data/file --valid_file /path/to/valid_data/file --model_dir /dir/to/save/the/new/model
```
Publication
===========
Peng Ni, Neng Huang, Zhi Zhang, De-Peng Wang, Fan Liang, Yu Miao, Chuan-Le Xiao, Feng Luo, and Jianxin Wang, "DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning.", Bioinformatics 35, no. 22 (2019): 4586-4595. [doi:10.1093/bioinformatics/btz276](https://doi.org/10.1093/bioinformatics/btz276)
License
=========
Copyright (C) 2018 [Jianxin Wang](mailto:[email protected]), [Feng Luo](mailto:[email protected]), [Peng Ni](mailto:[email protected]), [Neng Huang](mailto:[email protected])
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see .
[Jianxin Wang](mailto:[email protected]), [Peng Ni](mailto:[email protected]), [Neng Huang](mailto:[email protected]),
School of Information Science and Engineering, Central South University, Changsha 410083, China
[Feng Luo](mailto:[email protected]), School of Computing, Clemson University, Clemson, SC 29634, USA