text: user734: I am looking for publicly available data for a genomics deep learning project. My goal is to compare different architectures to predict biological insights from DNA sequences.
  I have heard about Janggu before, but I am looking for a few datasets which are like the MNIST equivalents in genomics. Kaggle doesn't seem to have many such datasets, and I would be happy if you share your insights about potential datasets that one could use.
  For example, it could be datasets that map 100-Mers to their ChIP-seq values or datasets that annotate a DNA sequence with the probability that a transcription factor would bind to it.
  Choong and Lee lists a few datasets on their IEEE paper from 2017:
  
  
  user3848: Good question and the Janggu link is cool. Thanks!
  Kaggle is/was heavily image recognition. There isn't an MNIST type data set in genomics, in case anyone is wondering this is a classic natural language processing (NLP) type analysis to identify handwritten single digit numbers.
  There has been several FDA precision challenges, one being described here:
  FDA precision challenges
  This could provide something towards a 'standard'.
  A key issue remains the accurate vectorisation of sequence and whether you think that is/was satisfactory in my personal opinion.
  There is an old review article here
  
original_text: 734: <p>I am looking for publicly available data for a genomics deep learning project. My goal is to compare different architectures to predict biological insights from DNA sequences.</p>
  <p>I have heard about <a href="https://janggu.readthedocs.io/en/latest/tutorial.html" rel="nofollow noreferrer">Janggu</a> before, but I am looking for a few datasets which are like the <a href="https://en.m.wikipedia.org/wiki/MNIST_database" rel="nofollow noreferrer">MNIST</a> equivalents in genomics. <a href="https://www.kaggle.com/tags/human-genetics" rel="nofollow noreferrer">Kaggle</a> doesn't seem to have many such datasets, and I would be happy if you share your insights about potential datasets that one could use.</p>
  <h2>For example, it could be datasets that map 100-Mers to their ChIP-seq values or datasets that annotate a DNA sequence with the probability that a transcription factor would bind to it.</h2>
  <p>Choong and Lee lists a few <a href="https://www.biorxiv.org/content/biorxiv/early/2017/10/25/186965.full.pdf" rel="nofollow noreferrer">datasets</a> on their IEEE paper from 2017:</p>
  <p><a href="https://i.stack.imgur.com/Hosg5.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Hosg5.png" alt="enter image description here" /></a></p>
   3848: <p>Good question and the Janggu link is cool. Thanks!</p>
  <p>Kaggle is/was heavily image recognition. There isn't an MNIST type data set in genomics, in case anyone is wondering this is a classic natural language processing (NLP) type analysis to identify handwritten single digit numbers.</p>
  <p>There has been several FDA precision challenges, one being described here:</p>
  <p><a href="https://precision.fda.gov/challenges/3/view/results" rel="nofollow noreferrer">FDA precision challenges</a></p>
  <p>This could provide something towards a 'standard'.</p>
  <p>A key issue remains the accurate vectorisation of sequence and whether you think that is/was satisfactory in my personal opinion.</p>
  <p>There is an old review article <a href="https://arxiv.org/abs/1802.00810" rel="nofollow noreferrer">here</a></p>
  