Page 86 - Molecular features of low-grade developmental brain tumours

3
84
CHAPTER 3
19. Keshet, I., Yisraeli, J. & Cedar, H. Effect of regional DNA methylation on gene expression. Proc Natl Acad Sci U S A 82, 2560-2564, doi:10.1073/pnas.82.9.2560 (1985).
20. Klutstein, M., Nejman, D., Greenfield, R. & Cedar, H. DNA Methylation in Cancer and Aging. Cancer Res 76, 3446-3450, doi:10.1158/0008-5472.CAN-15-3278 (2016).
21. Binder, H. et al. DNA methylation, transcriptome and genetic copy number signatures of diffuse cerebral WHO grade II/III gliomas resolve cancer heterogeneity and development. Acta Neuropathol Commun 7, 59, doi:10.1186/s40478-019-0704-8 (2019).
22. Jones, P. A. & Baylin, S. B. The epigenomics of cancer. Cell 128, 683-692, doi:10.1016/j.cell.2007.01.029 (2007).
23. Henshall, D. C. & Kobow, K. Epigenetics and Epilepsy. Cold Spring Harb Perspect Med 5, doi:10.1101/ cshperspect.a022731 (2015).
24. Gos, M. Epigenetic mechanisms of gene expression regulation in neurological diseases. Acta Neurobiol Exp (Wars) 73, 19-37 (2013).
25. Laffaire, J. et al. Methylation profiling identifies 2 groups of gliomas according to their tumorigenesis. Neuro Oncol 13, 84-98, doi:10.1093/neuonc/noq110 (2011).
26. Kobow, K. et al. Genomic DNA methylation distinguishes subtypes of human focal cortical dysplasia. Epilepsia 60, 1091-1103, doi:10.1111/epi.14934 (2019).
27. Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469-474, doi:10.1038/nature26000 (2018).
28. Stone, T. J. et al. Comprehensive molecular characterisation of epilepsy-associated glioneuronal tumours. Acta neuropathologica 135, 115-129, doi:10.1007/s00401-017-1773-z (2018).
29. Capper, D. et al. Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience. Acta neuropathologica 136, 181-210, doi:10.1007/s00401- 018-1879-y (2018).
30. Bongaarts, A. et al. Subependymal giant cell astrocytomas in Tuberous Sclerosis Complex have consistent TSC1/TSC2 biallelic inactivation, and no BRAF mutations. Oncotarget 8, 95516-95529, doi:10.18632/oncotarget.20764 (2017).
31. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120, doi:10.1093/bioinformatics/btu170 (2014).
32. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14, R36, doi:10.1186/gb-2013-14-4-r36 (2013).
33. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923-930, doi:10.1093/bioinformatics/ btt656 (2014).
34. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550, doi:10.1186/s13059-014-0550-8 (2014).
35. 35 Fortin, J. P. et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol 15, 503, doi:10.1186/s13059-014-0503-2 (2014).
36. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572-1573, doi:10.1093/bioinformatics/btq170 (2010).
37. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284-287, doi:10.1089/omi.2011.0118 (2012).
38. Croft, D. et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 39, D691-697, doi:10.1093/nar/gkq1018 (2011).