Make an actually useful README.md

It's pycharm's markdown so it might need to be fixed for github.

README.md

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-# checkMetab
+# MAGotator
 
-A tool for annotating metagenomic assemblies and bins. Outputs predicted metabolisms.
-
+MAGotator (MAG (Metagenome Assembled Genome) annotator) is a tool for annotating metagenomic assembled genomes that 
+outputs predicted metabolisms. It will annotated MAG's using [KEGG](https://www.kegg.jp/) (if provided by the user), 
+[UniRef90](https://www.uniprot.org/), [PFAM](https://pfam.xfam.org/), [dbCAN](http://bcb.unl.edu/dbCAN2/), 
+[RefSeq viral](https://www.ncbi.nlm.nih.gov/genome/viruses/) and the [MEROPS](https://www.ebi.ac.uk/merops/) peptidase 
+database. MAGotator is ran in two stages. First the annotation is done to give a full summary of the provided MAGs with 
+all annotations from these databases. Second the annotations are summarized to give an idea of what metabolisms each 
+genome is capable of. These summarizations are given in the form of tables. One table is counts of genes organized by
+the metabolisms they are a part of and the other is a measure of completeness of KEGG modules.
+
+## Installation
+To install MAGotator some dependencies need to be installed first then MAGotator can be installed from this repository. 
+In the future MAGotator will be available via both pip and conda.
+
+0. Install Dependencies
+
+    Dependencies can be installed via conda or by manually installing all dependencies.
+
+    _Conda Installation_
+
+    Installed MAGotator into a new [conda](https://docs.conda.io/en/latest/) environment using the provided 
+enviornment.yaml file.
+    ```bash
+    wget https://raw.githubusercontent.com/shafferm/checkMetab/master/environment.yaml
+    conda env create -f environment.yml -n MAGotator
+    ```
+    If this installation method is used then all further steps should be ran 
+
+    _Manual Installation_
+
+    If you do not install via a conda enviornment the dependencies [pandas](https://pandas.pydata.org/), 
+    [networkx](https://networkx.github.io/), [scikit-bio](http://scikit-bio.org/),
+    [prodigal](https://github.com/hyattpd/Prodigal), [mmseqs2](https://github.com/soedinglab/mmseqs2), 
+    [hmmer](http://hmmer.org/) and [tRNAscan-SE](http://lowelab.ucsc.edu/tRNAscan-SE/) manually.
+
+1. Download this repository using `git clone https://github.com/shafferm/checkMetab.git`
+2. Change directory into the MAGotator directory and install MAGotator using `pip install -e .`
+
+You have now installed MAGotator.
+
+## Setup
+
+To run MAGotator you need to set up the databases it needs in order to get annotations from those databases. All 
+databases but KEGG can be downloaded and set up for use with MAGotator for you automatically. To get KEGG annotations 
+and fully take advantage of the genome summarization capabilities of MAGotator you must have access to the KEGG 
+database. KEGG is a paid subscription service to download the protein files used by this annotator. If you do not have 
+access to KEGG then your data can be annotated with all other databases. Genome summarization will summarize the tRNAs, 
+peptidases and CAZy's in your data set but not the primary metabolisms.
+
+_I have access to KEGG_
+
+Then set up MAGotator using the following command:
+
+```bash
+MAGotator.py prepare_databases --output_dir MAGotator_data --kegg_loc kegg.pep
+```
+
+`kegg.pep` is the path to the amino acid FASTA file downloaded from KEGG. This can be any of the single files 
+provided by the KEGG FTP server or a concatenated version of the multiple provided files. `MAGotator_data` is the path 
+to the processed databases used by MAGotator. If you already have any of the databases downloaded to your server and 
+don't want to download them again then you can give them to the `prepare_databases` command by use the `--{db_name}_loc`
+ flags such as `--uniref_loc` and `--viral_loc`.
+
+_I don't have access to KEGG_
+
+Not a problem. Then use this command:
+
+```bash
+MAGotator.py prepare_databases --output_dir MAGotator_data --kegg_loc kegg.pep
+```
+
+Similar to above you can still provide locations of databases you have already downloaded so you don't have to do it
+again.
+
+To see that your set up worked use the command `MAGotator.py print_config` and the location of all databases provided 
+will be shown as well as the presence of additional annotation information.
+
+*NOTE:* Setting up MAGotator can take a long time (up to 5 hours) depending on the number of processors which you tell 
+it to use (using the `--threads` argument) and the speed of your internet connection. On my university server using 10 
+processors it takes about 2 hours to process the data when databases do not need to be downloaded.
+
+## Usage
+
+Once MAGotator is set up you are ready to annotate some MAGs. The following command will generate your full annotation: 
+
+```bash
+MAGotator.py annotate -i 'my_bins/*.fa' -o annotation
+```
+
+`my_bins` should be replaced with the path to a directory which contains all of your bins you would like to annotated. 
+If you only need to annotated a single genome (or an entire assembly) a direct path to a nucleotide fasta should be 
+provided. Using 20 processors MAGotator.py takes about 17 hours to annotate ~80 MAGs of medium quality or higher from a 
+mouse gut metagenome.
+
+In the output `annotation` folder there will be various files. `genes.faa` and `genes.fna` are fasta files with all 
+genes called by prodigal with additional header information gained from the annotation as nucleotide and amino acid 
+records respectively. `genes.gff` is a GFF3 with the same annotation information as well as gene locations.
+`scaffolds.fna` is a collection of all scaffolds/contigs given as input to `MAGotator.py annotate` with added bin 
+information in the headers. `annotations.tsv` is the most important output of the annotation. This includes all 
+annotation information about every gene from all MAGs. Each line is a different gene and each column contains annotation
+ information. `trnas.tsv` contains a summary of the tRNAs found in each MAG.
+
+Then after your annotation is finished you can summarize these anntotations with the following command:
+
+```bash
+MAGotator.py summarize_genomes -i annotation/annotations.tsv -o genome_summaries --trna_path annotation/trnas.tsv
+```
+
+This command will generate two files. The first is called `genome_summary.tsv` this contains a summary of  metabolisms 
+present in each genome. It gives gene by gene information across various metabolisms for every genome in your dataset. 
+The `module_summary.tsv` file contains all KEGG modules and will give completion statistics in terms of the number of 
+steps in each module that have been covered by at least one gene in each genome.