Distinct pathways for removal of defective RNA polymerase II transcription complexes at a promoter-proximal pause checkpoint
RNA-seq (gene expression) · human · 36 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 36 samples: 12 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE219076_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA906985 and SRA study SRP410641. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 36 samples.
How we read this: 12 replicated groups read from 36 sample titles; they account for 36 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM6767150 HEK293_WT_PRO_rep1 HEK293_WT_PRO
- GSM6767151 HEK293_WT_PRO_rep2 HEK293_WT_PRO
- GSM6767152 HEK293_WT_PRO_rep3 HEK293_WT_PRO
- GSM6767153 HEK293_ARMC5_KO_PRO_rep1 HEK293_ARMC5_KO_PRO
- GSM6767154 HEK293_ARMC5_KO_PRO_rep2 HEK293_ARMC5_KO_PRO
- GSM6767155 HEK293_ARMC5_KO_PRO_rep3 HEK293_ARMC5_KO_PRO
- GSM6767156 HEK293_WT_RNAseq_rep1 HEK293_WT_RNAseq
- GSM6767157 HEK293_ARMC5_KO_RNAseq_rep1 HEK293_ARMC5_KO_RNAseq
- GSM6767158 HEK293_WT_RNAseq_rep2 HEK293_WT_RNAseq
- GSM6767159 HEK293_ARMC5_KO_RNAseq_rep2 HEK293_ARMC5_KO_RNAseq
- GSM6767160 HEK293_WT_RNAseq_rep3 HEK293_WT_RNAseq
- GSM6767161 HEK293_ARMC5_KO_RNAseq_rep3 HEK293_ARMC5_KO_RNAseq
- GSM6767162 HEK293_WT_TTseq_rep1 HEK293_WT_TTseq
- GSM6767163 HEK293_ARMC5_KO_TTseq_rep1 HEK293_ARMC5_KO_TTseq
- GSM6767164 HEK293_WT_TTseq_rep2 HEK293_WT_TTseq
- GSM6767165 HEK293_ARMC5_KO_TTseq_rep2 HEK293_ARMC5_KO_TTseq
- GSM6767166 HEK293_WT_TTseq_rep3 HEK293_WT_TTseq
- GSM6767167 HEK293_ARMC5_KO_TTseq_rep3 HEK293_ARMC5_KO_TTseq
- GSM8019194 INTS12_R1 INTS12
- GSM8019195 INTS12_R2 INTS12
- GSM8019196 INTS12_R3 INTS12
- GSM8019197 INTS4_R1 INTS4
- GSM8019198 INTS4_R2 INTS4
- GSM8019199 INTS4_R3 INTS4
- GSM8019200 INTS6_R1 INTS6
- GSM8019201 INTS6_R2 INTS6
- GSM8019202 INTS6_R3 INTS6
- GSM8019203 NT_R1 NT
- GSM8019204 NT_R2 NT
- GSM8019205 NT_R3 NT
- GSM8019206 ctrl_R1 ctrl
- GSM8019207 ctrl_R2 ctrl
- GSM8019208 ctrl_R3 ctrl
- GSM8019209 INTS4_dTAG_R1 INTS4_dTAG
- GSM8019210 INTS4_dTAG_R2 INTS4_dTAG
- GSM8019211 INTS4_dTAG_R3 INTS4_dTAG
- GSE287976 Single-Cell Omics Analysis of Human Basophils Reveals Two Transcriptionally Distinct Populations 12 samples shares transcription, distinct
- GSE318645 Dual Immune Checkpoint Blockade Induces Distinct Systemic and Intratumoral Immune Activation in Recurrent Glioblastoma 226 samples shares checkpoint, distinct
- GSE297483 Transcriptional stratification of necrotizing enterocolitis identifies distinct disease subtypes 23 samples shares transcription, distinct
- GSE332630 Multimodal single-cell analyses reveal distinct fusion-regulated transcriptional programs in Ewing sarcoma 22 samples shares transcription, distinct
- GSE310664 Analysis of experimental colorectal liver metastases identifies distinct proliferation independent transcriptional programs in micro- versus macrometastases 22 samples shares transcription, distinct
- GSE337533 A Single-Cell Atlas of Transcriptional and Immunoglobulin Repertoire Evolution in Early B Cell Development 66 samples shares transcription
- GSE335494 B-cell depletion improves therapeutic index of combination checkpoint blockade in patients with advanced melanoma 44 samples shares checkpoint
- GSE272348 Late-stage tertiary lymphoid structures in hepatocellular carcinoma treated with neoadjuvant immune checkpoint blockade 31 samples shares checkpoint
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.