cleanup

tabs/annotation.html

@@ -3,7 +3,6 @@
   <p>This notebook demonstrates how to use the NTv3 post-trained model to perform genome annotation directly from a DNA sequence. It relies on a pipeline that applies a Hidden Markov Model (HMM) to the per-base probabilities returned by NTv3, converting them into a coherent gene model that respects biological constraints and valid transitions between genomic elements.</p>
   <p>The pipeline abstracts away all the underlying steps: running inference with the model, retrieving and processing the predicted probabilities, and applying the HMM to generate a consistent annotation. It returns a ready-to-use GFF file that can be visualized in any genome browser for the sequence of interest.</p>
   <p>If you're interested in exploring the intermediate probabilities, please refer to the <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_tutorials/01_tracks_prediction.ipynb" target="_blank" rel="noopener">track-prediction notebook</a>. These probabilities can be useful for assessing model confidence and identifying potentially interesting biological regions. This notebook focuses on the higher-level task of producing gene annotations directly from raw DNA.</p>
-  <p><strong>📝 Note for Google Colab users:</strong> This notebook is compatible with Colab! For faster inference, make sure to enable GPU: Runtime → Change runtime type → GPU (T4 or better recommended).</p>
   <p>
     <strong>🔗 Quick links:</strong><br>
     • <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_pipelines/02_genome_annotation.ipynb" target="_blank" rel="noopener">View notebook on Hugging Face</a><br>

tabs/functional_tracks.html

@@ -3,7 +3,6 @@
   <p>This notebook demonstrates how to use the NTv3 post-trained model to predict functional tracks and genome annotation directly from a DNA sequence.</p>
   <p>The pipeline abstracts away all the underlying steps: running inference with the model and plotting the predictions per tracks.</p>
   <p>If you're interested in exploring the intermediate probabilities, please refer to the <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_tutorials/01_tracks_prediction.ipynb" target="_blank" rel="noopener">track-prediction notebook</a>.</p>
-  <p><strong>📝 Note for Google Colab users:</strong> This notebook is compatible with Colab! For faster inference, make sure to enable GPU: Runtime → Change runtime type → GPU (T4 or better recommended).</p>
   <p>
     <strong>🔗 Quick links:</strong><br>
     • <a href="https://huggingface.co/spaces/InstaDeepAI/ntv3/blob/main/notebooks_pipelines/01_functional_track_prediction.ipynb" target="_blank" rel="noopener">View notebook on Hugging Face</a><br>