Update README.md

Author: DDesmonts

README.md

@@ -69,6 +69,8 @@ When importing the CSV file in DigestR, it should resemble the one below:
 
 ![](https://github.com/LewisResearchGroup/digestR/blob/main/Images/Mascot%20file%20format.png)
 
+An example data file can be found here: https://github.com/LewisResearchGroup/digestR/blob/main/Example%20Files/Data_Example.csv
+
 By default, Mascot creates a header, this 3 line header is required for the .csv preprocessing (see Prepare Mascot files). 
 
 #### 1. Prepare Mascot files: rd()
@@ -91,6 +93,11 @@ Subsequently, users have the option to download data from specific chromosomes b
 
 ![](https://github.com/LewisResearchGroup/DigestR/blob/main/Images/generate%20proteome.png)
 
+Example proteome files can be found here: 
+https://github.com/LewisResearchGroup/digestR/blob/main/Example%20Files/Human_proteome.csv
+https://github.com/LewisResearchGroup/digestR/blob/main/Example%20Files/Pfal_3D7_proteome.csv
+
+
 #### 4. Process mascot files: pm() 
 To create "digestion" maps, peptides identified by Mascot or MaxQuant need to be mapped to their proteomic location. First, the user needs to select a proteome to align peptides against (see Generate Proteome). DigestR will automatically detect and utilize all proteomes located within the "data/proteomes" subfolder. Users can also import their own proteomes into this subfolder. After proteome selection, users can align Mascot identified peptides along the selected proteome from a single or multiple files. These alignments generate "coincidence" or "digestion" maps that users can interact with.