Update README and add new example prediction image

Author: Jan Ludwiczak

README.md

@@ -1,29 +1,46 @@
 # **DeepCoil** #
-[![DOI:10.1093/bioinformatics/bty1062 ](https://zenodo.org/badge/DOI/10.1093/bioinformatics/bty1062.svg)](https://doi.org/10.1093/bioinformatics/bty1062 )
+[![DOI:10.1093/bioinformatics/bty1062](https://zenodo.org/badge/DOI/10.1093/bioinformatics/bty1062.svg)](https://doi.org/10.1093/bioinformatics/bty1062 )
 ![build](https://github.com/labstructbioinf/DeepCoil/workflows/deepcoil/badge.svg) 
 
-**Fast and accurate prediction of coiled coil domains in protein sequences.**
-## **Installation** ##
+## **Fast and accurate prediction of coiled coil domains in protein sequences**
+### **New in version 2.0** ###
+- Faster inference time by applying *[SeqVec](https://github.com/rostlab/SeqVec)* embeddings instead of *psiblast* profiles.
+- Additional heptad predictions (*a* and *d* core positions).
+- No maximum sequence length limit.
+- Convenient interface for using *DeepCoil* within python scripts.
+- Automated peak detection for improved output readability.
+- Simplified installation with *pip*.
+
+Older DeepCoil versions are available [here](https://github.com/labstructbioinf/DeepCoil/releases). 
+
+### **Requirements and installation** ###
+DeepCoil requires `python>=3.6.1` and `pip>=19.0`. Other requirements are specified in the `requirements.txt` file.
+
 The most convenient way to install **DeepCoil** is to use pip:
 ```bash
-$ pip install deepcoil
+$ pip3 install deepcoil
 ```
 
-## **Usage** ##
+### **Usage** ###
 
-##### Running DeepCoil as standalone:
+#### Running DeepCoil standalone version:
 
 ```bash
-deepcoil [-h] -i FILE [-out_path DIR]
+deepcoil [-h] -i FILE [-out_path DIR] [-n_cpu NCPU] [--gpu] [--plot]
+                [--dpi DPI]
 ```
-| Option        | Description |
+| Argument        | Description |
 |:-------------:|-------------|
 | **`-i`** | Input file in FASTA format. Can contain multiple entries. |
-| **`-out_path`** | Directory where the predictions are saved. For each entry in the input file one file will be saved.|
-| **`--gpu`** | Flag for turning on the GPU usage. Results in faster inference on large datasets.|
+| **`-out_path`** | Directory where the predictions are saved. For each entry in the input file one file will be saved. Defaults to the current directory if not specified.|
+| **`-n_cpu`** | Number of CPUs to use in the prediction. By the default all cores will be used.|
+| **`--gpu`** | Flag for turning on the GPU usage. Allows faster inference on large datasets. Overrides **`-n_cpu`** option.|
+| **`--plot`** | Turns on the additional visual output of the predictions for each entry in the input. Plot files are saved in the **`-out_path`** directory.|
+| **`--dpi`** | DPI of the saved plots, active only with **`--plot`** option.|
 
+In a rare case of `deepcoil` being not available in your `PATH` after installation please look in the `$HOME/.local/bin/` or other system specific `pip` directory.
 
-##### Running DeepCoil within script:
+#### Running DeepCoil within script:
 
 ```python
 from deepcoil import DeepCoil
@@ -36,7 +53,12 @@ inp = {str(entry.id): str(entry.seq) for entry in SeqIO.parse('example/example.f
 
 results = dc.predict(inp)
 
-plot_preds(results['3WPA_1'], to_file='example/example.png')
+plot_preds(results['3WPA_1'], out_file='example/example.png')
 ```
-###### Example graphical output:
+`results[entry]`  for an entry of sequence length `N` contains two keys:
+- `['cc']` - per residue coiled coil propensity (`[N, 1]` shape)
+- `['hept']` - per residue core positions (`[N, 3]` shape, order in the second axis is: no/other position, *a* position, *d* position)
+
+Peak detection can be performed with the `deepcoil.utils.sharpen_preds` helper function.
+#### Example graphical output:
 ![Example](example/example.png)