Conserved regulation of RNA processing in somatic cell reprogramming

<strong>Data set 1. Transcript expression across human RNA-Seq samples: estimated read counts. </strong>The file contains estimated read counts, generated by kallisto (https://pachterlab.github.io/kallisto/), for human transcripts and RNA-Seq samples used in this study (see Additional file 2 of the accompanying publication). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. Ensembl transcript identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.

<strong>Data set 2. Transcript expression across murine RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for mouse transcripts.

<strong>Data set 3. Transcript expression across simian RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for chimpanzee transcripts.

<strong>Data set 4. Transcript expression across across human RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 1, but instead of read counts, transcript abundances in transcripts per million (TPM), as estimated by kallisto (https://pachterlab.github.io/kallisto/), are listed. Format, column and row names as in Data set 1.

<strong>Data set 5. Transcript expression across murine RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for mouse transcripts.

<strong>Data set 6. Transcript expression across simian RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for chimpanzee transcripts.

<strong>Data set 7. Differential expression analyses across human RNA-Seq sample groups: log fold changes. </strong>The file contains log fold changes, inferred by edgeR (http://bioconductor.org/packages/release/bioc/html/edgeR.html), for human genes and the RNA-Seq sample group contrasts listed in Additional file 3 of the accompanying publication in a compressed (GZIP) TSV gene-by-comparison matrix. Ensembl gene identifiers and a descriptive contrast identifier serve as row and column names, respectively.

<strong>Data set 8. Differential expression analyses across murine RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for mouse genes.

<strong>Data set 9. Differential expression analyses across simian RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for chimpanzee genes.

<strong>Data set 10. Differential expression analyses across human RNA-Seq sample groups: false discovery rates. </strong>The file contains false discovery rates (FDR) for the differential expression analyses summarized in Data set 7. Format, column and row names as in Data set 7.

<strong>Data set 11. Differential expression analyses across murine RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for mouse genes.

<strong>Data set 12. Differential expression analyses across simian RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for chimpanzee genes.

<strong>Data set 13. Quantification of alternative splicing events across human RNA-Seq samples. </strong>The file contains ‘percent spliced in’ (PSI) values computed by SUPPA (https://github.com/comprna/SUPPA) for annotated alternative splicing events (inferred from the transcript annotation of the human genome, Ensembl release 84; http://www.ensembl.org/). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. SUPPA-provided event identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.

<strong>Data set 14. Quantification of alternative splicing events across murine RNA-Seq samples. </strong>As in Data set 13, but for mouse alternative splicing events.

<strong>Data set 15. Differential splicing analyses across human RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>The file contains ΔPSI values for human alternative splicing events (as in Data set 13). The RNA-Seq sample group contrasts are listed in Additional file 3 of the accompanying publication. Values were inferred by SUPPA’s diffSplice functionality (https://github.com/comprna/SUPPA). The format is a compressed (GZIP) tab-separated gene-by-comparison matrix. SUPPA event identifiers and a descriptive contrast identifier serve as row and column names, respectively.

<strong>Data set 16. Differential splicing analyses across murine RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>As in Data set 15, but for mouse alternative splicing events.

<strong>Data set 17. Differential splicing analyses across human RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of human alternative splicing events summarized in Data set 15. Format, column and row names as in Data set 15.

<strong>Data set 18. Differential splicing analyses across murine RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of mouse alternative splicing events summarized in Data set 16. Format, column and row names as in Data set 15.

<strong>Data set 19. Transcript expression across murine RNA-Seq time course data: estimated read counts. </strong>As in Data set 2, but for the time course data generated for the accompanying publication.

<strong>Data set 20. Transcript expression across murine RNA-Seq time course data: estimated transcript abundances. </strong>As in Data set 5, but for the time course data generated for the accompanying publication.

<strong>Data set 21. Quantification of alternative splicing events across murine RNA-Seq time course data. </strong>As in Data set 14, but for the time course data generated for the accompanying publication.