update README with time estimates for installation and demos

Author: kathyxchen

README.md

@@ -7,6 +7,7 @@ You have found Selene, a Python library and command line interface for training
 ## Installation
 
 Selene is a Python 3+ package. We recommend using it with Python 3.6 or above. 
+Package installation should only take a few minutes (less than 10 minutes, typically ~2-3 minutes) with any of these methods (pip, conda, source).  
 
 ### Installing selene with [Anaconda](https://www.anaconda.com/download/) (for Linux):
 
@@ -29,6 +30,7 @@ git clone https://github.com/FunctionLab/selene.git
 The `setup.py` script requires NumPy. Please make sure you have this already installed.
 
 If you plan on working in the `selene` repository directly, we recommend [setting up a conda environment](https://conda.io/docs/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file) using `selene-cpu.yml` or `selene-gpu.yml` (if CUDA is enabled on your machine) and activating it.
+These environment YAML files list specific versions of package dependencies that we have used in the past to test Selene.
 
 Selene contains some Cython files. You can build these by running
 ```sh
@@ -51,11 +53,35 @@ For a more detailed overview of the components in the Selene software developmen
 
 ![summary figure](docs/source/_static/img/selene_overview.png)
 
-### Tutorials and examples
+## Documentation
 
-Tutorials for selene are available [here](https://github.com/FunctionLab/selene/tree/master/tutorials).
+The documentation for Selene is available [here](https://selene.flatironinstitute.org/).
 
-### Documentation
+## Examples
 
-The documentation for selene is available [here](https://selene.flatironinstitute.org/).
+In general, we recommend that the manuscript case studies and the tutorials be run on a machine with a GPU. All examples take significantly longer when run on a CPU machine. 
+
+### Tutorials
+
+Tutorials for Selene are available [here](https://github.com/FunctionLab/selene/tree/master/tutorials).
+
+It is possible to run the tutorials (Jupyter notebook examples) on a standard CPU machine--you should not expect to fully finish running the training examples unless you can run them for more than 2-3 days, but they can all be run to completion on CPU in a couple of days. You can also change the training parameters (e.g. total number of steps) so that they complete in a much faster amount of time.
+
+The non-training examples (variant effect prediction, _in silico_ mutagenesis) can be run fairly quickly (variant effect prediction might take 20-30 minutes, _in silico_ mutagenesis in 10-15 minutes). 
+
+Please see the [README](https://github.com/FunctionLab/selene/blob/master/tutorials/README.md) in the `tutorials` directory for links and descriptions to the specific tutorials.   
+
+### Manuscript case studies
+
+The code to reproduce case studies in the manuscript is available [here](https://github.com/FunctionLab/selene/tree/master/manuscript).
+
+Each case has its own directory and README describing how to run these cases. 
+We recommend consulting the step-by-step breakdown of each case study that we provide in the methods section of [the manuscript](https://doi.org/10.1101/438291) as well.  
+
+The manuscript examples were only tested on GPU.
+Our GPU (NVIDIA Tesla V100) time estimates:
+
+- Case study 1 finishes in about 1 day on a GPU node.
+- Case study 2 takes 6-7 days to run training (distributed the work across 4 v100s).
+- Case study 3 (variant effect prediction) takes about 1 day to run.