Suzanne de Bruijn

71 The impact of modern technologies on molecular diagnostics 126. Littink, K.W., Pott, J.W., Collin, R.W., Kroes, H.Y., Verheij, J.B., Blokland, E.A. et al. A novel nonsense mutation in CEP290 induces exon skipping and leads to a relatively mild retinal phenotype. Investigative Ophthalmology & Visual Science 51 , 3646-3652 (2010). 127. Roosing, S., Cremers, F.P.M., Riemslag, F.C.C., Zonneveld-Vrieling, M.N., Talsma, H.E., Klessens- Godfroy, F.J.M. et al. A rare form of retinal dystrophy caused by hypomorphic nonsense mutations in CEP290. Genes (Basel) 8 , 208 (2017). 128. DiStefano, M.T., Hemphill, S.E., Cushman, B.J., Bowser, M.J., Hynes, E., Grant, A.R. et al. Curating clinically relevant transcripts for the interpretation of sequence variants. Journal of Molecular Diagnostics 20 , 789-801 (2018). 129. Kircher, M.,Witten, D.M., Jain, P., O'Roak, B.J., Cooper, G.M. & Shendure, J. A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics 46 , 310- 315 (2014). 130. Grantham, R. Amino acid difference formula to help explain protein evolution. Science 185 , 862 (1974). 131. Schwarz, J.M., Cooper, D.N., Schuelke, M. & Seelow, D. MutationTaster2: mutation prediction for the deep-sequencing age. Nature Methods 11 , 361 (2014). 132. Pollard, K.S., Hubisz, M.J., Rosenbloom, K.R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Research 20 , 110-121 (2010). 133. Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P. et al. A method and server for predicting damaging missense mutations. Nature Methods 7 , 248-249 (2010). 134. Vaser, R., Adusumalli, S., Leng, S.N., Sikic, M. & Ng, P.C. SIFT missense predictions for genomes. Nature Protocols 11 , 1 (2015). 135. Desmet, F.-O., Hamroun, D., Lalande, M., Collod-Béroud, G., Claustres, M. & Béroud, C. Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Research 37 , E67-E67 (2009). 136. Shapiro, M.B. & Senapathy, P. RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression. Nucleic Acids Research 15 , 7155-74 (1987). 137. Yeo, G. & Burge, C.B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. Journal of Computational Biology 11 , 377-394 (2004). 138. Pertea, M., Lin, X. & Salzberg, S.L. GeneSplicer: a new computational method for splice site prediction. Nucleic Acids Research 29 , 1185-1190 (2001). 139. Reese, M.G., Eeckman, F.H., Kulp, D. & Haussler, D. Improved splice site detection in Genie. Journal of Computational Biology 4 , 311-323 (1997). 140. Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J.F., Darbandi, S.F., Knowles, D., Li, Y.I. et al. Predicting splicing from primary sequence with deep learning. Cell 176 , 535-548 (2019). 141. Sangermano, R., Khan, M., Cornelis, S.S., Richelle, V., Albert, S., Garanto, A. et al. ABCA4 midigenes reveal the full splice spectrum of all reported noncanonical splice site variants in Stargardt disease. Genome Research 28 , 100-110 (2018).

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