DDIG: Detecting DIsease-causing Genetic variations

Posted on by Lukas Folkman

DDIG currently supports prediction of protein-coding
non-frameshifting (NFS) indels,
frameshifting (FS) indels,
nonsense (protein-truncating) and
synonymous (same-sense, silent)
variants in the GRCh37/hg19 assembly of the human genome.

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Target (optional):
Input your variants (format and examples):


Publications:
Synonymous variants:
M. Livingstone, L. Folkman, Y. Yang, P. Zhang, M. Mort, D. N. Cooper, Y. Liu, B. Stantic, and Y. Zhou, “Investigating DNA, RNA and protein-based features as a means to discriminate pathogenic synonymous variants.”, Human Mutation 38: 1336-1347 (2017). [link]

FS indels and nonsense variants:
L. Folkman, Y. Yang, Z. Li, B. Stantic, A. Sattar, M. Mort, D. N. Cooper, Y. Liu, and Y. Zhou, “DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels.”, Bioinformatics, 31 1599–1606 (2015). [link]

NFS indels:
H. Zhao, Y. Yang, H. Lin, X. Zhang, M. Mort, D. N. Cooper, Y. Liu and Y. Zhou, “DDIG-in: Discriminating between disease-associated and neutral non-frameshifting micro-indels”, Genome Biology 14 , R43 (2013). [link]

Learn more about DDIG’s output format
Download the training and testing datasets
Output example