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@@ -74,7 +74,8 @@
     <p>The developed code provided below is a Python program that uses biopython and the Tkinter library to create a graphical user interface (GUI) for analyzing the location of DNA features such as RBS, Start Codon, and Flanking DNA sequences. </p>
 
     <p>It searches for a specific DNA motif (RBS - Ribosome Binding Site) and identifies its position in both the forward and reverse directions. Additionally, it calculates the length of the flanking region and checks whether it falls within an ideal range.</p>
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+    <br>
+    <p>You can see our source code here:<a href="https://gitlab.igem.org/2023/software-tools/svce-chennai/-/blob/main/SeqPredict.py?ref_type=heads">SeqPredict</a></p>
     <p>Here's a step-by-step protocol to use and get the output from this code:</p>
     <p>1. Install Required Libraries:</p>
     <p>Ensure that you have the necessary libraries installed. You need to have Tkinter, Biopython, and Bio installed. You can install them using pip if they are not already installed.</p>
@@ -154,6 +155,9 @@
   <section class="desc-section">
     <h2>PromoterStrengthPredict 2.0</h2>
     <p>“PromoterStrengthPredict”, a tool developed by our alumni of the SVCE-CHENNAI 2017 team, adapts a machine learning approach to predict the strength of Sigma 70 promoters in E. coli, thus streamlining and enhancing promoter selection for the iGEM projects. We developed an upgraded version of this software, “PromoterStrengthPredict 2.0”, which involves finding the RBS strength when the sequence is given as the input. The output will be a 2-D plot between the RBS sequence score (X-axis) and the RBS strength value (Y-axis). 
+       <br>
+       <br>
+       You can see our source code here:<a href="https://gitlab.igem.org/2023/software-tools/svce-chennai/-/blob/main/PromoterStrengthPredictor_2.0.py?ref_type=heads">PromoterStrengthPredictor 2.0</a>
        <br>
        Here's a step-by-step protocol to use and get the output from this code:
        <br>