{
  "id": 437864,
  "title": "ChatGPT ideas - might be not so bad",
  "url": "/competitions/stanford-ribonanza-rna-folding/discussion/437864",
  "author_name": "",
  "post_date": "2023-09-08T12:40:17.254962700Z",
  "votes": 10,
  "comment_count": 3,
  "views": 0,
  "content": "<p><strong>Query:</strong>  what are the approaches to \" predicting the reactivity of an RNA sequence to two chemical modifiers DMS and 2A3.\"</p>\n<p><strong>Answer</strong> (Omitting experimental approach):</p>\n<ul>\n<li><p>Use RNA structure prediction software such as Mfold, ViennaRNA, or RNAfold to predict the secondary structure of the RNA sequence. The reactivity of bases can be influenced by their accessibility in the folded RNA structure.</p></li>\n<li><p>Machine Learning and Computational Approaches:</p></li>\n<li><p>-  Develop machine learning models trained on experimental data to predict reactivity based on sequence and structural features. Features could include base pairing probabilities, sequence motifs, and thermodynamic properties.</p></li>\n<li><p>-  Use deep learning techniques, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to model the relationship between RNA sequences and their reactivity to chemical modifiers.</p></li>\n<li><p>Sequence Motif Analysis:</p></li>\n<li><p>-  Look for specific sequence motifs that are known to be targets for DMS or 2A3 modification. For example, DMS preferentially modifies single-stranded adenosines and cytosines.</p></li>\n<li><p>Statistical Analysis:</p></li>\n<li><p>Analyze the correlation between the reactivity data and various sequence/structural features using statistical methods such as regression analysis or correlation coefficients.</p></li>\n<li><p>Comparative Analysis:</p></li>\n<li><p>Compare the reactivity of the RNA sequence with known reference sequences that have been experimentally characterized with DMS and 2A3. This can help in predicting relative reactivity.<br>\nMolecular Dynamics Simulations:</p></li>\n<li><p>Perform molecular dynamics simulations to study the interaction between the RNA sequence and the chemical modifiers at the atomic level. Simulations can provide insights into the binding and reactivity mechanisms.</p></li>\n<li><p>Incorporate Structural Constraints:<br>\n-- Utilize structural information from techniques like NMR or X-ray crystallography, if available, to improve reactivity predictions by considering the RNA's three-dimensional structure.</p></li>\n</ul>",
  "messages": [
    {
      "id": "2429192",
      "postDate": "09/08/2023 12:40:17",
      "content": "<p><strong>Query:</strong>  what are the approaches to \" predicting the reactivity of an RNA sequence to two chemical modifiers DMS and 2A3.\"</p>\n<p><strong>Answer</strong> (Omitting experimental approach):</p>\n<ul>\n<li><p>Use RNA structure prediction software such as Mfold, ViennaRNA, or RNAfold to predict the secondary structure of the RNA sequence. The reactivity of bases can be influenced by their accessibility in the folded RNA structure.</p></li>\n<li><p>Machine Learning and Computational Approaches:</p></li>\n<li><p>-  Develop machine learning models trained on experimental data to predict reactivity based on sequence and structural features. Features could include base pairing probabilities, sequence motifs, and thermodynamic properties.</p></li>\n<li><p>-  Use deep learning techniques, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to model the relationship between RNA sequences and their reactivity to chemical modifiers.</p></li>\n<li><p>Sequence Motif Analysis:</p></li>\n<li><p>-  Look for specific sequence motifs that are known to be targets for DMS or 2A3 modification. For example, DMS preferentially modifies single-stranded adenosines and cytosines.</p></li>\n<li><p>Statistical Analysis:</p></li>\n<li><p>Analyze the correlation between the reactivity data and various sequence/structural features using statistical methods such as regression analysis or correlation coefficients.</p></li>\n<li><p>Comparative Analysis:</p></li>\n<li><p>Compare the reactivity of the RNA sequence with known reference sequences that have been experimentally characterized with DMS and 2A3. This can help in predicting relative reactivity.<br>\nMolecular Dynamics Simulations:</p></li>\n<li><p>Perform molecular dynamics simulations to study the interaction between the RNA sequence and the chemical modifiers at the atomic level. Simulations can provide insights into the binding and reactivity mechanisms.</p></li>\n<li><p>Incorporate Structural Constraints:<br>\n-- Utilize structural information from techniques like NMR or X-ray crystallography, if available, to improve reactivity predictions by considering the RNA's three-dimensional structure.</p></li>\n</ul>",
      "rawMarkdown": "**Query:**  what are the approaches to \" predicting the reactivity of an RNA sequence to two chemical modifiers DMS and 2A3.\"\n\n**Answer** (Omitting experimental approach):\n\n- Use RNA structure prediction software such as Mfold, ViennaRNA, or RNAfold to predict the secondary structure of the RNA sequence. The reactivity of bases can be influenced by their accessibility in the folded RNA structure.\n\n- Machine Learning and Computational Approaches:\n- -  Develop machine learning models trained on experimental data to predict reactivity based on sequence and structural features. Features could include base pairing probabilities, sequence motifs, and thermodynamic properties.\n- -  Use deep learning techniques, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to model the relationship between RNA sequences and their reactivity to chemical modifiers.\n\n- Sequence Motif Analysis:\n\n- -  Look for specific sequence motifs that are known to be targets for DMS or 2A3 modification. For example, DMS preferentially modifies single-stranded adenosines and cytosines.\n\n- Statistical Analysis:\n\n- Analyze the correlation between the reactivity data and various sequence/structural features using statistical methods such as regression analysis or correlation coefficients.\n\n- Comparative Analysis:\n\n- Compare the reactivity of the RNA sequence with known reference sequences that have been experimentally characterized with DMS and 2A3. This can help in predicting relative reactivity.\nMolecular Dynamics Simulations:\n\n- Perform molecular dynamics simulations to study the interaction between the RNA sequence and the chemical modifiers at the atomic level. Simulations can provide insights into the binding and reactivity mechanisms.\n\n- Incorporate Structural Constraints:\n-- Utilize structural information from techniques like NMR or X-ray crystallography, if available, to improve reactivity predictions by considering the RNA's three-dimensional structure.",
      "votes": null
    },
    {
      "id": "2429199",
      "postDate": "09/08/2023 12:48:57",
      "content": "<p>Thanks to Sham Nasaev, here is chatGPT summary of the article recommended by orgs (\"Thoughts on how to think (and talk) about RNA structure\")</p>\n<p>The article titled \"Thoughts on how to think (and talk) about RNA structure\" does not specifically address approaches to predicting the reactivity of an RNA sequence to the chemical modifiers DMS and 2A3. However, it does discuss the general challenges and considerations in RNA secondary structure prediction.</p>\n<p>The article critiques the common practice of using thermodynamics-based algorithms like Mfold, RNAfold, and Sfold to predict RNA secondary structures. These algorithms often produce a single lowest free-energy structure, which is sometimes erroneously considered as the true structure of the RNA sequence. The article emphasizes that these predictions are based on assumptions and should not substitute for thorough experimental secondary structure determination.</p>\n<p>The article also mentions a hierarchy in methods commonly used to predict RNA secondary structure. The confidence level in an RNA secondary structure model depends on the number of pieces of computational and experimental information used to predict that model. This is linked to the time required to obtain the model and the accessibility to advanced inst</p>",
      "rawMarkdown": "Thanks to Sham Nasaev, here is chatGPT summary of the article recommended by orgs (\"Thoughts on how to think (and talk) about RNA structure\")\n\n \nThe article titled \"Thoughts on how to think (and talk) about RNA structure\" does not specifically address approaches to predicting the reactivity of an RNA sequence to the chemical modifiers DMS and 2A3. However, it does discuss the general challenges and considerations in RNA secondary structure prediction.\n\nThe article critiques the common practice of using thermodynamics-based algorithms like Mfold, RNAfold, and Sfold to predict RNA secondary structures. These algorithms often produce a single lowest free-energy structure, which is sometimes erroneously considered as the true structure of the RNA sequence. The article emphasizes that these predictions are based on assumptions and should not substitute for thorough experimental secondary structure determination.\n\nThe article also mentions a hierarchy in methods commonly used to predict RNA secondary structure. The confidence level in an RNA secondary structure model depends on the number of pieces of computational and experimental information used to predict that model. This is linked to the time required to obtain the model and the accessibility to advanced inst",
      "votes": null
    },
    {
      "id": "2429639",
      "postDate": "09/08/2023 17:38:32",
      "content": "<p>good to see you again Alex, thanks for the introduction to challenge and hope this will be another interesting and fun competition for all! </p>",
      "rawMarkdown": "good to see you again Alex, thanks for the introduction to challenge and hope this will be another interesting and fun competition for all!",
      "votes": null
    },
    {
      "id": "2431688",
      "postDate": "09/10/2023 10:01:41",
      "content": "<p>I find this funny!<br>\nThan i tried your query at google bard, it links back here. 😁</p>\n<p><a href=\"url\" target=\"_blank\">https://g.co/bard/share/67658cdebe38</a></p>",
      "rawMarkdown": "I find this funny!\nThan i tried your query at google bard, it links back here. 😁\n\n[https://g.co/bard/share/67658cdebe38](url)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2429199,
      "author_name": "alexandervc",
      "author_url": "",
      "post_date": "09/08/2023 12:48:57",
      "content": "<p>Thanks to Sham Nasaev, here is chatGPT summary of the article recommended by orgs (\"Thoughts on how to think (and talk) about RNA structure\")</p>\n<p>The article titled \"Thoughts on how to think (and talk) about RNA structure\" does not specifically address approaches to predicting the reactivity of an RNA sequence to the chemical modifiers DMS and 2A3. However, it does discuss the general challenges and considerations in RNA secondary structure prediction.</p>\n<p>The article critiques the common practice of using thermodynamics-based algorithms like Mfold, RNAfold, and Sfold to predict RNA secondary structures. These algorithms often produce a single lowest free-energy structure, which is sometimes erroneously considered as the true structure of the RNA sequence. The article emphasizes that these predictions are based on assumptions and should not substitute for thorough experimental secondary structure determination.</p>\n<p>The article also mentions a hierarchy in methods commonly used to predict RNA secondary structure. The confidence level in an RNA secondary structure model depends on the number of pieces of computational and experimental information used to predict that model. This is linked to the time required to obtain the model and the accessibility to advanced inst</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 2429639,
      "author_name": "zmcxjt",
      "author_url": "",
      "post_date": "09/08/2023 17:38:32",
      "content": "<p>good to see you again Alex, thanks for the introduction to challenge and hope this will be another interesting and fun competition for all! </p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 2431688,
      "author_name": "jimmyknox",
      "author_url": "",
      "post_date": "09/10/2023 10:01:41",
      "content": "<p>I find this funny!<br>\nThan i tried your query at google bard, it links back here. 😁</p>\n<p><a href=\"url\" target=\"_blank\">https://g.co/bard/share/67658cdebe38</a></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2429192": "**Query:**  what are the approaches to \" predicting the reactivity of an RNA sequence to two chemical modifiers DMS and 2A3.\"\n\n**Answer** (Omitting experimental approach):\n\n- Use RNA structure prediction software such as Mfold, ViennaRNA, or RNAfold to predict the secondary structure of the RNA sequence. The reactivity of bases can be influenced by their accessibility in the folded RNA structure.\n\n- Machine Learning and Computational Approaches:\n- -  Develop machine learning models trained on experimental data to predict reactivity based on sequence and structural features. Features could include base pairing probabilities, sequence motifs, and thermodynamic properties.\n- -  Use deep learning techniques, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to model the relationship between RNA sequences and their reactivity to chemical modifiers.\n\n- Sequence Motif Analysis:\n\n- -  Look for specific sequence motifs that are known to be targets for DMS or 2A3 modification. For example, DMS preferentially modifies single-stranded adenosines and cytosines.\n\n- Statistical Analysis:\n\n- Analyze the correlation between the reactivity data and various sequence/structural features using statistical methods such as regression analysis or correlation coefficients.\n\n- Comparative Analysis:\n\n- Compare the reactivity of the RNA sequence with known reference sequences that have been experimentally characterized with DMS and 2A3. This can help in predicting relative reactivity.\nMolecular Dynamics Simulations:\n\n- Perform molecular dynamics simulations to study the interaction between the RNA sequence and the chemical modifiers at the atomic level. Simulations can provide insights into the binding and reactivity mechanisms.\n\n- Incorporate Structural Constraints:\n-- Utilize structural information from techniques like NMR or X-ray crystallography, if available, to improve reactivity predictions by considering the RNA's three-dimensional structure.",
    "2429199": "Thanks to Sham Nasaev, here is chatGPT summary of the article recommended by orgs (\"Thoughts on how to think (and talk) about RNA structure\")\n\n \nThe article titled \"Thoughts on how to think (and talk) about RNA structure\" does not specifically address approaches to predicting the reactivity of an RNA sequence to the chemical modifiers DMS and 2A3. However, it does discuss the general challenges and considerations in RNA secondary structure prediction.\n\nThe article critiques the common practice of using thermodynamics-based algorithms like Mfold, RNAfold, and Sfold to predict RNA secondary structures. These algorithms often produce a single lowest free-energy structure, which is sometimes erroneously considered as the true structure of the RNA sequence. The article emphasizes that these predictions are based on assumptions and should not substitute for thorough experimental secondary structure determination.\n\nThe article also mentions a hierarchy in methods commonly used to predict RNA secondary structure. The confidence level in an RNA secondary structure model depends on the number of pieces of computational and experimental information used to predict that model. This is linked to the time required to obtain the model and the accessibility to advanced inst",
    "2429639": "good to see you again Alex, thanks for the introduction to challenge and hope this will be another interesting and fun competition for all!",
    "2431688": "I find this funny!\nThan i tried your query at google bard, it links back here. 😁\n\n[https://g.co/bard/share/67658cdebe38](url)"
  },
  "source": "meta"
}