1. Wang, Y., et al. (2006) Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays. BMC Genomics 7: 59.

2. Oberthuer, A., et al. (2010) Comparison of performance of one-color and two-color gene-expression analyses in predicting clinical endpoints of neuroblastoma patients. Pharmacogenomics J. 10: 258–266.

3. Petryszak, R., et al. (2014) Expression Atlas update–a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments. Nucleic Acids Res. 42: D926–D932.

4. Grant, G.R., et al. (2007) Analysis and management of microarray gene expression data. Current Protocols in Molecular Biology Chapter 19:Unit 19.6.

5. Ritchie, M.E., et al. (2015) limma powers differential expression analyses for RNA-sequencing and microarray. Nucleic Acids Res. 43: e47. 

6. Wang, Z., et al. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10: 57–63.

7. Oshlack, A., et al. (2010) From RNA-seq reads to differential expression results. Genome Biology 11: 220.

8. Han, Y., et al. (2015) Advanced applications of RNA sequencing and challenges. Bioinform. Biol. Insights 9: 29–46.

9. Conesa, A., et al. (2016) A survey of best practices for RNA-seq data analysis. Genome Biology 17: 13.

10. Haas, B.J. and Zody, M.C., et al. (2010) Advancing RNA-Seq analysis. Nature Biotechnology 28: 421-423.

11. Kim, D. et al. (2019) Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907-915.

12. Curtis, R.K., et al. (2005) Pathways to the analysis of microarray data. Trends in Biotechnology 23: 429-435.

13. Werner, T. (2008) Bioinformatics applications for pathway analysis of microarray data. Current Opinion in Biotechnology 19: 50-54.

14. Alkan, C., et al. (2011) Genome structural variation discovery and genotyping. Nat. Rev. Genet. 12: 363–376.

15. Escaramís, G., et al. (2015) A decade of structural variants: description, history and methods to detect structural variation. Briefings in Functional Genomics 14: 305-314.

16. Ennis, C. (2014) Epigenetics 101: a beginner’s guide to explaining everything. The Guardian newspaper.

17. Esteller, M. (2007) Cancer epigenomics: DNA methylomes and histone-modification maps. Nature Rev. Gen. 8: 286-298.

18. Kurdyukov, S. and Bullock, M. (2016) DNA Methylation Analysis: Choosing the Right Method. Biology 5.

19. Fu, Y., et al. (2014) Gene expression regulation mediated through reversible m⁶A RNA methylation. Nature Rev. Gen. 15: 293-306.

20. Kimura, H. (2013) Histone modifications for human epigenome analysis. Journal of Human Genetics 58: 439-445.

21. Milek, M., et al. (2010) Transcriptome-wide analysis of protein-RNA interactions using high-throughput sequencing. Seminars in Cell & Developmental Biology 23: 206-212.

22. Helwa, R. and Hoheisel, J.D. (2010) Analysis of DNA-protein interactions: from nitrocellulose filter binding assays to microarray studies. Analytical and Bioanalytical Chemistry 398: 2551-2561.

23. Ascano, M., et al. (2013) Multi-disciplinary methods to define RNA-protein interactions and regulatory networks. Curr Opin Genet Dev. 23: 20–28.

24. Riley, K.J. and Steitz, J.A. The “Observer Effect” in genome-wide surveys of protein-RNA interactions. Molecular Cell 49: 601-604.

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