| Autor: |
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, Prokisch, Holger |
| Rok vydání: |
2022 |
| Předmět: |
|
| DOI: |
10.6084/m9.figshare.19518428 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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