<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>

CC BY-SA 4.0
2020-11-22T22:03:38.493Z
Since I have a new method to address this problem. I would like to find a dataset which is like MNIST to evaluate my idea.
0x90
CC BY-SA 4.0
2020-11-22T22:02:21.540Z
2020-11-22T22:02:21.540Z
M__
<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.sstatic.net/Hosg5.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Hosg5.png" alt="enter image description here" /></a></p>

CC BY-SA 4.0
2020-11-22T21:34:53.160Z
related:
https://www.researchgate.net/post/Are_there_any_opensource_datasets_which_contain_the_DNA_of_an_individual_and_the_diseases_asssociated_with_this_DNA
0x90
CC BY-SA 4.0
2020-11-23T05:38:18.377Z
If you add more details about the type of deep learning you're evaluating we will be better equipped to direct you to resources. If you're doing something like compositional data analysis, check out [this paper](https://arxiv.org/pdf/2006.01392.pdf) and the list of 45 datasets with links on the last page.
jared_mamrot
CC BY-SA 4.0
2020-11-23T05:46:09.853Z
@jared_mamrot thank you. I would like to train networks that take chip-seq data and DNA sequences as inputs. The prediction layer may be any biological signal one could think of?
0x90
CC BY-SA 4.0
2020-11-23T21:07:52.623Z
Genomics is a *huge* field; you need to be more specific. If you're looking for *everything* with regards to publicly-available datasets, then there's [GenBank](https://www.ncbi.nlm.nih.gov/genbank/)
gringer
CC BY-SA 4.0
2020-11-24T17:48:45.680Z
David Reich Lab https://reich.hms.harvard.edu/datasets
0x90
<phylogenetics><phylogeny><deep-learning><tensorflow><pytorch>
What datasets are available out there for prediction based on DNA sequences?
# What datasets are available out there for prediction based on DNA sequences?

(Asked by: username_0 on 2020-11-22)

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](https://janggu.readthedocs.io/en/latest/tutorial.html) before, but I am looking for a few datasets which are like the [MNIST](https://en.m.wikipedia.org/wiki/MNIST_database) equivalents in genomics. [Kaggle](https://www.kaggle.com/tags/human-genetics) 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](https://www.biorxiv.org/content/biorxiv/early/2017/10/25/186965.full.pdf) on their IEEE paper from 2017:

[![enter image description here](https://i.sstatic.net/Hosg5.png)](https://i.sstatic.net/Hosg5.png)

**Tags:** phylogenetics, phylogeny, deep-learning, tensorflow, pytorch
> **Comments:**
> **username_0**: related: https://www.researchgate.net/post/Are_there_any_opensource_datasets_which_contain_the_DNA_of_an_individual_and_the_diseases_asssociated_with_this_DNA  
> **username_1**: If you add more details about the type of deep learning you're evaluating we will be better equipped to direct you to resources. If you're doing something like compositional data analysis, check out [this paper](https://arxiv.org/pdf/2006.01392.pdf) and the list of 45 datasets with links on the last page.  
> **username_0**: @jared_mamrot thank you. I would like to train networks that take chip-seq data and DNA sequences as inputs. The prediction layer may be any biological signal one could think of?  
> **username_2**: Genomics is a *huge* field; you need to be more specific. If you're looking for *everything* with regards to publicly-available datasets, then there's [GenBank](https://www.ncbi.nlm.nih.gov/genbank/)  
> **username_0**: David Reich Lab https://reich.hms.harvard.edu/datasets  

---

## 1 answers

### Answer by username_3 on 2020-11-22. Score: 2

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](https://precision.fda.gov/challenges/3/view/results)

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](https://arxiv.org/abs/1802.00810)

> **Comments:**
> **username_0**: Since I have a new method to address this problem. I would like to find a dataset which is like MNIST to evaluate my idea.  

---

