使用纳米孔测序读数的信号水平特征检测甲基化_Python_下载.zip

# 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 form