A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders
ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 87 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Mus musculus (mouse)
- Samples
- 87 samples: 14 conditions, mostly in duplicateGroups 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.
- Paper
- A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disordersNature communications · 2024
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 GSE254090_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1068528 and SRA study SRP485683. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 46 samples.
How we read this: 14 replicated groups read from 87 sample titles; they account for 46 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.
- GSM8033144 CN3_e11.5
- GSM8033145 CN3_e10.5
- GSM8033146 CN7_e10.5_R1 CN7_e10.5
- GSM8033147 CN7_e10.5_R2 CN7_e10.5
- GSM8033148 CN7_e11.5_R1 CN7_e11.5
- GSM8033149 CN7_e11.5_R2 CN7_e11.5
- GSM8033150 CN7neg_e11.5_R1 CN7neg_e11.5
- GSM8033151 CN7neg_e11.5_R2 CN7neg_e11.5
- GSM8033152 SMN_e10.5_R1 SMN_e10.5
- GSM8033153 SMN_e10.5_R2 SMN_e10.5
- GSM8033154 SMN_e11.5_R1 SMN_e11.5
- GSM8033155 SMN_e11.5_R2 SMN_e11.5
- GSM8033156 bulkRNA_cMN3_Isl1-pos_E10-5A
- GSM8033157 bulkRNA_cMN3_Isl1-pos_E10-5B
- GSM8033158 bulkRNA_cMN3_Isl1-pos_E11-5A
- GSM8033159 bulkRNA_cMN3_Isl1-pos_E11-5B
- GSM8033160 bulkRNA_cMN4_Isl1-pos_E10-5A
- GSM8033161 bulkRNA_cMN4_Isl1-pos_E10-5B
- GSM8033162 bulkRNA_cMN4_Isl1-pos_E11-5A
- GSM8033163 bulkRNA_cMN4_Isl1-pos_E11-5B
- GSM8033164 bulkRNA_cMN6_Hb9-pos_E11-5A1 bulkRNA_cMN6_Hb9-pos_E1…
- GSM8033165 bulkRNA_cMN6_Hb9-pos_E11-5A2 bulkRNA_cMN6_Hb9-pos_E1…
- GSM8033166 bulkRNA_cMN6_Hb9-pos_E11-5B
- GSM8033167 bulkRNA_cMN7_Isl1-pos_E10-5A
- GSM8033168 bulkRNA_cMN7_Isl1-pos_E10-5B
- GSM8033169 bulkRNA_cMN7_Isl1-pos_E11-5A
- GSM8033170 bulkRNA_cMN7_Isl1-pos_E11-5B
- GSM8033171 CN347SMN_e11.5_R1_ATAC
- GSM8033172 CN347SMN_e11.5_R1_GEX
- GSM8033173 CN347SMN_e11.5_R2_ATAC
- GSM8033174 CN347SMN_e11.5_R2_GEX
- GSM8033175 CN34_e10.5_R1 CN34_e10.5
- GSM8033176 CN34_e10.5_R2 CN34_e10.5
- GSM8033177 CN34_e11.5_R1 CN34_e11.5
- GSM8033178 CN34_e11.5_R2 CN34_e11.5
- GSM8033179 CN6_e10.5_R1 CN6_e10.5
- GSM8033180 CN6_e10.5_R2 CN6_e10.5
- GSM8033181 CN6_e11.5_R1 CN6_e11.5
- GSM8033182 CN6_e11.5_R2 CN6_e11.5
- GSM8033183 CN7_e10.5_R1 CN7_e10.5
+ 47 more — browse all 87 samples with per-sample file links →
- GSE328028 T cell lymphoma associated STAT3 variants impose a type 1 regulatory phenotype 28 samples shares regulatory, variants
- GSE217664 Decoding Trp53 regulatory program in various tissues and organs 404 samples shares regulatory
- GSE223050 The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1–linked chromatin opening 336 samples shares regulatory
- GSE297622 Temporal and Context-Dependent Requirements for the Transcription Factor Foxp3 in Regulatory T Cell Development and Maintenance 103 samples shares regulatory
- GSE233057 A gene regulatory network of the endothelial-to-hematopoietic transition 63 samples shares regulatory
- GSE252463 Interpreting regulatory mechanisms of Hippo signaling through a deep learning sequence model 59 samples shares regulatory
- GSE213756 Serine enrichment in tumors promotes regulatory T cell accumulation through sphinganine mediated regulation of c-Fos 32 samples shares regulatory
- GSE290082 Enhancer-driven gene regulatory networks reveal transcription factors governing T cell adaptation and differentiation in the tumor microenvironment. 25 samples shares regulatory
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.