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+# DeepG <img src="man/figures/logo_small.png"  align="left" vspace="-1800px"/>
+
+**deepG: toolbox for deep neural networks optimized for genomic
+datasets** <!---
+% <p><img alt="DeepG logo" height="70px" src="man/figures/logo_small.png" align="left" hspace="-1000px" vspace="-180px"></p>
+-->
+
+The goal of the package is to speed up the development of
+bioinformatical tools for sequence classification, homology detection
+and other bioinformatical tasks. It is developed for biologists and
+advanced AI researchers. DeepG is a collaborative effort from the
+McHardy Lab at the *Helmholtz Centre for Infection Research*, the Chair of
+Statistical Learning and Data Science at the *Ludwig Maximilian
+University of Munich* and the Huttenhower lab at *Harvard T.H. Chan
+School of Public Health*.
+
+[![DOI](https://zenodo.org/badge/387820006.svg)](https://zenodo.org/badge/latestdoi/387820006)
+
+## Overview
+
+The package offers several functions to create, train and evaluate
+neural networks as well as data processing.
+
+- **Data processing**
+  - Create data generator to handle large collections of files.
+  - Different options to encode fasta/fastq file (one-hot encoding,
+    coverage or quality score encoding).
+  - Different options to handle ambiguous nucleotides.
+- **Deep learning architectures**
+  - Create network architectures with single function call.
+  - Custom loss and metric functions available.
+- **Model training**
+  - Automatically create model/data pipeline.
+- **Visualizing training progress**
+  - Visualize training progress and metrics in tensorboard.  
+- **Model evaluation**
+  - Evaluate trained models.
+- **Model interpretability**
+  - Use Integrated Gradient to visualize relationship of model’s
+    predictions with regard to its input.
+
+## Installation
+
+Install the tensorflow python package
+
+``` r
+install.packages("tensorflow")
+tensorflow::install_tensorflow()
+```
+
+and afterwards install the latest version of deepG from github
+
+``` r
+devtools::install_github("GenomeNet/deepG")
+```
+
+## Usage
+
+See the Package website at <https://deepg.de> for documentation and
+example code.
+
+<!-- ## Examples  -->
+
+<!-- ## Datasets -->
+<!-- The library comes with mutiple different datasets for testing: -->
+<!-- - The set `data(parenthesis)` contains 100k characters of the parenthesis synthetic language generated from a very simple counting language with a parenthesis and letter alphabet Σ = {( ) 0 1 2 3 4 }. The language is constrained to match parentheses, and nesting is limited to at most 4 levels deep. Each opening parenthesis increases and each closing parenthesis decreases the nesting level, respectively. Numbers are generated randomly, but are constrained to indicate the nesting level at their position. -->
+<!-- - The set `data(crispr_full)` containing all CRISPR loci found in NCBI representative genomes with neighbor nucleotides up and downstream. -->
+<!-- - The set `data(crispr_sample)` containing a subset of `data(crispr_full)`. -->
+<!-- - The set `data(ecoli)` contains the *E. coli* genome, see [the genome sequence of Escherichia coli K-12](https://science.sciencemag.org/content/277/5331/1453.long). -->
+<!-- - The set `data(ecoli_small)` contains a subset of `data(ecoli)`. -->
+<!---
+## Installation and Usage
+&#10;Please see our [Wiki](https://github.com/hiddengenome/deepG/wiki) for further installation instructions. It covers also usage instructions for multi-GPU machines.
+&#10;- [Installation on desktop machine](https://github.com/hiddengenome/deepG/wiki/Installation-of-deepG-on-desktop)
+- [Installation on GPU server](https://github.com/hiddengenome/deepG/wiki/Installation-of-deepG-on-GPU-server)
+- [Installation AWS](https://github.com/hiddengenome/deepG/wiki/Installation-AWS)
+- [GPU Usage](https://github.com/hiddengenome/deepG/wiki/manage-GPU-usage)
+- [Tensorboard Integration](https://github.com/hiddengenome/deepG/wiki/Tensorboard-integration)
+&#10;See the help files `?deepG` to get started and for questions use the [FAQ](https://github.com/hiddengenome/deepG/wiki/FAQ).
+-->