Identification of known and novel long non-coding RNAs potentially responsible for the effects of BMD GWAS loci [hFOB RNA-seq]
RNA-seq (gene expression) · human · 18 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
- 18 samples: 6 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.
- Paper
- Identification of Known and Novel Long Noncoding RNAs Potentially Responsible for the Effects of Bone Mineral Density (BMD) Genomewide Association Study (GWAS) LociJournal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2022
Read the full summary ↓
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 GSE293846_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 PRJNA1246576 and SRA study SRP576530. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 17 samples.
How we read this: 6 replicated groups read from 18 sample titles; they account for 17 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.
- GSM8891724 hFOB cells, Day0, replicate 1 hFOB cells, Day0,
- GSM8891725 hFOB cells, Day0, replicate 2 hFOB cells, Day0,
- GSM8891726 hFOB cells, Day0, replicate 3 hFOB cells, Day0,
- GSM8891727 hFOB cells, Day2, replicate 1 hFOB cells, Day2,
- GSM8891728 hFOB cells, Day2, replicate 2 hFOB cells, Day2,
- GSM8891729 hFOB cells, Day2, replicate 3 hFOB cells, Day2,
- GSM8891730 hFOB cells, Day4, replicate 1 hFOB cells, Day4,
- GSM8891731 hFOB cells, Day4, replicate 2 hFOB cells, Day4,
- GSM8891732 hFOB cells, Day4, replicate 3 hFOB cells, Day4,
- GSM8891733 hFOB cells, Day6, repliacte 1
- GSM8891734 hFOB cells, Day6, replicate 2 hFOB cells, Day6,
- GSM8891735 hFOB cells, Day6, replicate 3 hFOB cells, Day6,
- GSM8891736 hFOB cells, Day8, replicate 1 hFOB cells, Day8,
- GSM8891737 hFOB cells, Day8, replicate 2 hFOB cells, Day8,
- GSM8891738 hFOB cells, Day8, replicate 3 hFOB cells, Day8,
- GSM8891739 hFOB cells, Day10, replicate 1 hFOB cells, Day10,
- GSM8891740 hFOB cells, Day10, replicate 2 hFOB cells, Day10,
- GSM8891741 hFOB cells, Day10, replicate 3 hFOB cells, Day10,
- GSE293119 Integration of Alzheimer’s GWAS, 3D genomics, and single-cell CRISPRi non-coding screen implicates causal variants in a microglial enhancer regulating TSPAN14. [RNA-Seq] 18 samples shares gwas, non-coding, rna-seq
- GSE281465 Dissecting Non-Coding GWAS Loci with High-Resolution 3D Chromatin Interactions Reveals Causal Genes with Relevance to Heart Failure [RNA-seq] 3 samples shares gwas, non-coding, rna-seq
- GSE293118 Integration of Alzheimer’s GWAS, 3D genomics, and single-cell CRISPRi non-coding screen implicates causal variants in a microglial enhancer regulating TSPAN14. [scRNA-Seq] 2 samples shares gwas, non-coding, rna-seq
- GSE319266 Identification of target genes and regulatory networks for bone mineral density GWAS loci through systematic targeting and inhibition [hFOB BMD STING-seq] 24 samples shares gwas, bmd
- GSE311725 Functional characterization of GWAS findings highlights a role for REST in endothelial plasticity and atherosclerosis [RNA-seq] 8 samples shares gwas, rna-seq
- GSE261282 Bioinformatics and single-cell CRISPRi-based screen reveals effector genes and implicates multi-tissue etiology for BMD [bulk RNA-seq] 3 samples shares bmd, rna-seq
- GSE340442 total RNA-seq from CD4-positive, alpha-beta T cell (ENCSR547BMD) 1 samples shares bmd, rna-seq
- GSE281464 Dissecting Non-Coding GWAS Loci with High-Resolution 3D Chromatin Interactions Reveals Causal Genes with Relevance to Heart Failure [Perturb-seq] 7 samples shares gwas, non-coding
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.