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General Info

You will find general information regarding the submitted mutation in this section.

***You can refer to ...... text book for more info on amino acid biochemistry


DEOGEN2 is a variant-effect predictor that improves the previous version. It aims at the multi-level contextualization of both the target variant and the affected protein by integrating information related to molecular, domain gene and pathway aspects. These heterogeneous sources of information are then encoded and fed to a Random Forest, from which the deleterious predictions are obtained. More Info
REFERENCE: Raimondi, D., Gazzo, A. M., Rooman, M., Lenaerts, T., & Vranken, W. F. (2016). Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects. Bioinformatics, btw094.


DynaMine is a fast predictor of protein backbone dynamics using only sequence information as input.
REFERENCE: Elisa Cilia, Rita Pancsa, Peter Tompa, Tom Lenaerts, and Wim Vranken. From protein sequence to dynamics and disorder with DynaMine. Nature Communications 4:2741 doi: 10.1038/ncomms3741 (2013)


SNP MusiC is a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing.
REFERENCE: Ancien, F., Pucci, F., Godfroid, M. et al. Prediction and interpretation of deleterious coding variants in terms of protein structural stability. Sci Rep 8, 4480 (2018) doi:10.1038/s41598-018-22531-2


If you use MutaFrame in your work, cite BOTH of the following articles:

Daniele Raimondi, Ibrahim Tanyalcin, Julien Ferte, Andrea Gazzo, Gabriele Orlando, Tom Lenaerts, Marianne Rooman, Wim Vranken. DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins. Nucleic Acids Research, Volume 45, Issue W1, 3 July 2017, Pages W201–W206.

I. Tanyalcin et al. Lexicon Visualization Library and JavaScript for Scientific Data Visualization. Computing in Science & Engineering, vol. 20, no. 1, pp. 50-65, January/February 2018.