
# Dataset Description
Out of **20,577** human proteins (from [UniProt human proteome](https://www.uniprot.org/proteomes/UP000005640)), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of **12,703** proteins. The uShuffle algorithm ([python pacakge](https://github.com/guma44/ushuffle)) was then used to shuffle these protein sequences while maintaining their doublet distribution. The very few sequences for which uShuffle failed to create a shuffled version were eliminated.
Afterwards, h-CD-HIT algorithm ([web server](http://weizhong-lab.ucsd.edu/cdhit-web-server/cgi-bin/index.cgi)) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of **11,658** sequences.

# Citation
If you use this dataset, please cite our paper:
```
@article {
	author = {Geffen, Yaron and Ofran, Yanay and Unger, Ron},
	title = {DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts},
	year = {2022},
	doi = {10.1093/bioinformatics/btac474},
	URL = {https://doi.org/10.1093/bioinformatics/btac474},
	journal = {Bioinformatics}
}
```
license:mit region:us
{"license": "mit"}
TAGS
#license-mit #region-us 


# Dataset Description
Out of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their doublet distribution. The very few sequences for which uShuffle failed to create a shuffled version were eliminated.
Afterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 11,658 sequences.

If you use this dataset, please cite our paper:

# Dataset Description
Out of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their doublet distribution. The very few sequences for which uShuffle failed to create a shuffled version were eliminated.
Afterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 11,658 sequences.

If you use this dataset, please cite our paper:
TAGS
#license-mit #region-us 
 # Dataset Description
Out of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their doublet distribution. The very few sequences for which uShuffle failed to create a shuffled version were eliminated.
Afterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 11,658 sequences.

If you use this dataset, please cite our paper:
passage: TAGS
#license-mit #region-us 
# Dataset Description
Out of 20,577 human proteins (from UniProt human proteome), sequences shorter than 20 amino acids or longer than 512 amino acids were removed, resulting in a set of 12,703 proteins. The uShuffle algorithm (python pacakge) was then used to shuffle these protein sequences while maintaining their doublet distribution. The very few sequences for which uShuffle failed to create a shuffled version were eliminated.
Afterwards, h-CD-HIT algorithm (web server) was used with three subsequent filter stages at pairwise identity cutoffs of 0.9, 0.5 and 0.1, resulting in a total of 11,658 sequences.

If you use this dataset, please cite our paper: