51. Additional file 2 of Clinical implementation of RNA sequencing for Mendelian disease diagnostics
- Author
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Yépez, Vicente A., Gusic, Mirjana, Kopajtich, Robert, Mertes, Christian, Smith, Nicholas H., Alston, Charlotte L., Ban, Rui, Beblo, Skadi, Berutti, Riccardo, Blessing, Holger, Ciara, Elżbieta, Distelmaier, Felix, Freisinger, Peter, Häberle, Johannes, Hayflick, Susan J., Hempel, Maja, Itkis, Yulia S., Kishita, Yoshihito, Klopstock, Thomas, Krylova, Tatiana D., Lamperti, Costanza, Lenz, Dominic, Makowski, Christine, Mosegaard, Signe, Müller, Michaela F., Muñoz-Pujol, Gerard, Nadel, Agnieszka, Ohtake, Akira, Okazaki, Yasushi, Procopio, Elena, Schwarzmayr, Thomas, Smet, Joél, Staufner, Christian, Stenton, Sarah L., Strom, Tim M., Terrile, Caterina, Tort, Frederic, Van Coster, Rudy, Vanlander, Arnaud, Wagner, Matias, Xu, Manting, Fang, Fang, Ghezzi, Daniele, Mayr, Johannes A., Piekutowska-Abramczuk, Dorota, Ribes, Antonia, Rötig, Agnès, Taylor, Robert W., Wortmann, Saskia B., Murayama, Kei, Meitinger, Thomas, Gagneur, Julien, and Prokisch, Holger
- Subjects
parasitic diseases ,population characteristics ,geographic locations ,health care economics and organizations - Abstract
Additional file 2: Fig. S1. Overview of the study. Fig. S2. Quality control. Fig. S3. DNA-RNA sample matching. Fig. S4. Aberrant events per sample. Fig. S5. Rare variants among expression outliers. Fig. S6. Power analysis of overexpression outliers. Fig. S7. Power analysis of underexpression outliers with respect to biological coefficient of variation. Fig. S8. Cases with many mtDNA expression outliers. Fig. S9. Rare variants among splicing outliers. Fig. S10. Splicing prediction algorithms evaluation. Fig. S11. Complex pattern of aberrant splicing. Fig. S12. Analysis of variants called by RNA-seq. Fig. S13. Rare variants leading to outliers. Fig. S14. Diagnostic rate across cohorts.
- Published
- 2022
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