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191 results for “somatic cell reprogramming”
Conserved regulation of RNA processing in somatic cell reprogramming
<p><strong>Data set 1. Transcript expression across human RNA-Seq samples: estimated read counts. </strong>The file contains estimated read counts, generated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), 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.</p> <p><strong>Data set 2. Transcript expression across murine RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for mouse transcripts.</p> <p><strong>Data set 3. Transcript expression across simian RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for chimpanzee transcripts.</p> <p><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 (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), are listed. Format, column and row names as in Data set 1.</p> <p><strong>Data set 5. Transcript expression across murine RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for mouse transcripts.</p> <p><strong>Data set 6. Transcript expression across simian RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for chimpanzee transcripts.</p> <p><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 (<a href="http://bioconductor.org/packages/release/bioc/html/edgeR.html">http://bioconductor.org/packages/release/bioc/html/edgeR.html</a>), 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.</p> <p><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.</p> <p><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.</p> <p><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.</p> <p><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.</p> <p><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.</p> <p><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 (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>) for annotated alternative splicing events (inferred from the transcript annotation of the human genome, Ensembl release 84; <a href="http://www.ensembl.org/">http://www.ensembl.org/</a>). 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.</p> <p><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.</p> <p><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 (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>). 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.</p> <p><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.</p> <p><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.</p> <p><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.</p> <p><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.</p> <p><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.</p> <p><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.</p>
Chemical-based external stimulation reprograms human somatic cells into pluripotency (single cell ATAC-seq)
GEO Series GSE178324. Homo sapiens. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Myc and SAGA Rewire an Alternative Splicing Network During Early Somatic Cell Reprogramming [mESCs_RNASEQ]
GEO Series GSE67691. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
DOT1L-interacting protein AF10 (MLLT10) is a barrier to somatic cell reprogramming
GEO Series GSE161043. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
KMT1A (SUV39H1) prevents somatic cell nuclear transfer (SCNT) mediated reprogramming
GEO Series GSE99072. Bos taurus. 6 samples. Type: Expression profiling by high throughput sequencing.
Coordination of engineered factors with TET1/2 promotes early stage epigenetic modification during somatic cell reprogramming
GEO Series GSE46202. Mus musculus. 8 samples. Type: Methylation profiling by high throughput sequencing.
Reprogramming of two somatic nuclei in the same ooplasm leads to pluripotent embryonic stem cells
GEO Series GSE36468. Mus musculus. 8 samples. Type: Expression profiling by array.
Deterministic direct reprogramming of somatic cells to pluripotency [ChIP-Seq]
GEO Series GSE49766. Mus musculus. 52 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Resolving the function of FoxO6 during Somatic Cell Reprogramming by RNA-seq
GEO Series GSE161057. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
High-Resolution Dissection of Conducive Somatic Cell Reprogramming to Naïve Ground State Pluripotency in Mbd3/NuRD and Gatad2a/NuRD Depleted Systems
GEO Series GSE102518. Mus musculus. 243 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Transcriptional reprogramming of somatic cells after nuclear transfer to mouse 4-cell embryos
GEO Series GSE162345. Mus musculus; Oryx dammah. 30 samples. Type: Expression profiling by high throughput sequencing.
Impaired stem cell differentiation and somatic cell reprogramming in DIDO3 mutants with altered RNA processing and increased R-loop levels
GEO Series GSE152346. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Orphan Nuclear Receptors ERRα/γ are competence factors for somatic cell reprogramming
GEO Series GSE31704. Homo sapiens. 6 samples. Type: Expression profiling by array.
The histone chaperone CAF-1 safeguards somatic cell identity during transcription factor-induced reprogramming (sequencing)
GEO Series GSE72744. Mus musculus. 61 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
LincRNA-p21 Blocks Somatic Cell Reprogramming by Sustaining H3K9me3 and CpG Methylation at Pluripotent Gene Promoters
GEO Series GSE57967. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Deterministic direct reprogramming of somatic cells to pluripotency
GEO Series GSE49767. Mus musculus. 71 samples. Type: Expression profiling by array; Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
NCoR/SMRT co-repressors cooperate with c-MYC to create an epigenetic barrier to somatic cell reprogramming [RNA-seq]
GEO Series GSE70738. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of gene expression during somatic cell reprogramming
GEO Series GSE49940. Mus musculus. 51 samples. Type: Expression profiling by array.
Netrin-1 facilitates somatic cell reprogramming by limiting Dcc pro-apoptotic activity.
GEO Series GSE54107. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
High-Resolution Dissection of Conducive Somatic Cell Reprogramming to Naïve Ground State Pluripotency in Mbd3/NuRD and Gatad2a/NuRD Depleted Systems [RRBS]
GEO Series GSE107853. Mus musculus. 10 samples. Type: Methylation profiling by high throughput sequencing.
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