{
  "id": 442411,
  "title": "Motivation",
  "url": "/competitions/stanford-ribonanza-rna-folding/discussion/442411",
  "author_name": "",
  "post_date": "2023-09-22T13:55:22.681818Z",
  "votes": 2,
  "comment_count": 1,
  "views": 0,
  "content": "<blockquote>\n  <p>… the reactivity of an RNA sequence to two chemical modifiers DMS and 2A3. These data can be measured efficiently through a mutational profiling (MaP) experiment…</p>\n</blockquote>\n<p>If the reactivities can be measured efficiently, what is the motivation behind using data science to predict these values besides (presumably) a marginal improvement over the existing experimental \"efficiency\"?</p>",
  "messages": [
    {
      "id": "2451397",
      "postDate": "09/22/2023 13:55:22",
      "content": "<blockquote>\n  <p>… the reactivity of an RNA sequence to two chemical modifiers DMS and 2A3. These data can be measured efficiently through a mutational profiling (MaP) experiment…</p>\n</blockquote>\n<p>If the reactivities can be measured efficiently, what is the motivation behind using data science to predict these values besides (presumably) a marginal improvement over the existing experimental \"efficiency\"?</p>",
      "rawMarkdown": ">... the reactivity of an RNA sequence to two chemical modifiers DMS and 2A3. These data can be measured efficiently through a mutational profiling (MaP) experiment...\n\nIf the reactivities can be measured efficiently, what is the motivation behind using data science to predict these values besides (presumably) a marginal improvement over the existing experimental \"efficiency\"?",
      "votes": null
    },
    {
      "id": "2451628",
      "postDate": "09/22/2023 16:25:42",
      "content": "<p>Efficiency can mean several things; in this context, we mean that it's possible to measure the reactivity of potentially millions of sequences at once and generate fairly reliable data. However, that process takes weeks to months, and requires access to some advanced equipment and skilled technicians. An accurate computational predictive model would be much faster and accessible to a wider audience. Also, millions of sequences is an impressive technological advance from the capabilities of even a few years ago, but it's still limited compared to the scale of data we're interested in. Assessing the human genome would require testing billions of sequences, and we're also interested in viral and bacterial genomes. An accurate computational predictor could serve as a useful screening tool to filter these larger targets down to an experimentally manageable set.</p>",
      "rawMarkdown": "Efficiency can mean several things; in this context, we mean that it's possible to measure the reactivity of potentially millions of sequences at once and generate fairly reliable data. However, that process takes weeks to months, and requires access to some advanced equipment and skilled technicians. An accurate computational predictive model would be much faster and accessible to a wider audience. Also, millions of sequences is an impressive technological advance from the capabilities of even a few years ago, but it's still limited compared to the scale of data we're interested in. Assessing the human genome would require testing billions of sequences, and we're also interested in viral and bacterial genomes. An accurate computational predictor could serve as a useful screening tool to filter these larger targets down to an experimentally manageable set.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2451628,
      "author_name": "brainbowrna",
      "author_url": "",
      "post_date": "09/22/2023 16:25:42",
      "content": "<p>Efficiency can mean several things; in this context, we mean that it's possible to measure the reactivity of potentially millions of sequences at once and generate fairly reliable data. However, that process takes weeks to months, and requires access to some advanced equipment and skilled technicians. An accurate computational predictive model would be much faster and accessible to a wider audience. Also, millions of sequences is an impressive technological advance from the capabilities of even a few years ago, but it's still limited compared to the scale of data we're interested in. Assessing the human genome would require testing billions of sequences, and we're also interested in viral and bacterial genomes. An accurate computational predictor could serve as a useful screening tool to filter these larger targets down to an experimentally manageable set.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2451397": ">... the reactivity of an RNA sequence to two chemical modifiers DMS and 2A3. These data can be measured efficiently through a mutational profiling (MaP) experiment...\n\nIf the reactivities can be measured efficiently, what is the motivation behind using data science to predict these values besides (presumably) a marginal improvement over the existing experimental \"efficiency\"?",
    "2451628": "Efficiency can mean several things; in this context, we mean that it's possible to measure the reactivity of potentially millions of sequences at once and generate fairly reliable data. However, that process takes weeks to months, and requires access to some advanced equipment and skilled technicians. An accurate computational predictive model would be much faster and accessible to a wider audience. Also, millions of sequences is an impressive technological advance from the capabilities of even a few years ago, but it's still limited compared to the scale of data we're interested in. Assessing the human genome would require testing billions of sequences, and we're also interested in viral and bacterial genomes. An accurate computational predictor could serve as a useful screening tool to filter these larger targets down to an experimentally manageable set."
  },
  "source": "meta"
}