Merge branch 'release/0.1.7'

README.md

@@ -57,27 +57,28 @@ pip install ont-tombo[full]
 
 ```bash
 # install using conda
-conda install -c anaconda tensorflow==1.12.2
+conda install -c anaconda tensorflow==1.13.1
 # or install using pip
-pip install 'tensorflow==1.12.2'
+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==1.12.2
+conda install -c anaconda tensorflow-gpu==1.13.1
 # or install using pip
-pip install 'tensorflow-gpu==1.12.2'
+pip install 'tensorflow-gpu==1.13.1'
 ```
 
 ## Trained models
-The models we trained can be downloaded from [here](http://bioinformatics.csu.edu.cn/resources/softs/nipeng/DeepSignal/index.html), or [here](https://people.cs.clemson.edu/~luofeng/deepsignal/), or [google drive](https://drive.google.com/open?id=1zkK8Q1gyfviWWnXUBMcIwEDw3SocJg7P).
+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.tar.gz_: A CpG model trained using HX1 R9.4 1D reads.
-   * _model.GATC.R9_2D.tem.puc19.bn17.sn360.tar.gz_: A G*A*TC model trained using pUC19 R9 2D template reads (only in [google drive](https://drive.google.com/open?id=1zkK8Q1gyfviWWnXUBMcIwEDw3SocJg7P) now).
+   * _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 [here](http://bioinformatics.csu.edu.cn/resources/softs/nipeng/DeepSignal/index.html), or [here](https://people.cs.clemson.edu/~luofeng/deepsignal/), or [google drive](https://drive.google.com/open?id=1zkK8Q1gyfviWWnXUBMcIwEDw3SocJg7P).
+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.
 
@@ -121,12 +122,12 @@ The extracted_features file is a tab-delimited text file in the following format
 The extracted features can be used to call modifications as follows (If a GPU-machine is used, please 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/bn_17.sn_360.epoch_7.ckpt --result_file fast5s.al.CpG.call_mods.tsv --nproc 10 --is_gpu no
+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
-deepsignal call_mods --input_path fast5s.al/ --model_path model.CpG.R9.4_1D.human_hx1.bn17.sn360/bn_17.sn_360.epoch_7.ckpt --result_file fast5s.al.CpG.call_mods.tsv --reference_path GCF_000146045.2_R64_genomic.fna --corrected_group RawGenomeCorrected_001 --nproc 10 --is_gpu no
+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 --reference_path GCF_000146045.2_R64_genomic.fna --corrected_group RawGenomeCorrected_001 --nproc 10 --is_gpu no
 ```
 
 The modification_call file is a tab-delimited text file in the following format:

README.rst

@@ -10,6 +10,7 @@ v0.1.7
 Prevent Queue.qsize() from raising NotImplementedError on Mac OS X (github: vterron/lemon@9ca6b4b)
 covert raw signals to pA values before normalization in extract_feature module
 add denoise module
+add module-chosen options of model (rnn/base/cnn), re-train human model
 
 v0.1.6
 -------------

deepsignal/__init__.py

@@ -2,4 +2,4 @@
 DNA methylation state from Nanopore reads."""
 
 
-__version__ = '0.1.6'
+__version__ = '0.1.7'

setup.py

@@ -44,7 +44,7 @@ def run_tests(self):
     keywords=['methylation', 'nanopore', 'neural network'],
     version=find_version('deepsignal', '__init__.py'),
     url='https://github.com/bioinfomaticsCSU/deepsignal',
-    download_url='https://github.com/bioinfomaticsCSU/deepsignal/archive/v0.1.6.tar.gz',
+    download_url='https://github.com/bioinfomaticsCSU/deepsignal/archive/v0.1.7.tar.gz',
     license='GNU General Public License v3 (GPLv3)',
     author='Peng Ni, Neng Huang',
     # tests_require=['pytest'],