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Quantitative analysis of cis-regulatory elements in transcription with KAS-ATAC-seq [Mouse KAS-seq]

GSE256229 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 26 samples Submitted 2024/06/21 Platform GPL24247Platform GPL19057Platform GPL16417
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 26 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
26 samples: 13 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
Quantitative analysis of cis-regulatory elements in transcription with KAS-ATAC-seqNature communications · 2024
Summary (written by the submitters)
Cis-regulatory elements (CREs) are pivotal in orchestrating gene expression throughout diverse biological systems. Accurate identification and in-depth characterization of functional CREs are crucial for decoding gene regulation network and dynamics during cellular processes. In this study, we developed a new KAS-seq (Opti-KAS-seq) procedure, with enhanced efficiency of capturing single-stranded DNA (ssDNA), broader genomic coverage, and adaptability to various sample types.
Read the full summary ↓
By integrating the highly sensitive Opti-KAS-seq with ATAC-seq, we further introduce KAS-ATAC-seq, a new method that provides quantitative insights into transcriptional activity of CREs. A main advantage of KAS-ATAC-seq lies in its precise measurement of ssDNA levels within both proximal and distal ATAC-seq peaks. This feature is particularly adept at identifying ssDNA promoter and Single-Stranded Transcribing Enhancers (SSTEs). SSTEs are highly enriched with nascent RNA transcripts and specific transcription factors (TFs) binding sites that determine cellular identity. Moreover, KAS-ATAC-seq provides a detailed characterization and functional implications of various SSTE subtypes; KAS-ATAC-seq signals exhibit more robust correlation with enhancer activities when compared with ATAC-seq data and active histone mark profiles. Our analysis of promoters and SSTEs during mouse neural differentiation demonstrates that KAS-ATAC-seq can effectively identify immediate-early activated CREs in response to RA treatment. We further discovered that ETS TFs and YY1 are critical in initiating early neural differentiation from mESCs to NPCs. Our findings indicate that KAS-ATAC-seq provides more precise annotation of functional CREs in transcription. Future applications of KAS-ATAC-seq would help elucidate the intricate dynamics of gene regulation in diverse biological processes and biomedical applications.
Published in
Quantitative analysis of cis-regulatory elements in transcription with KAS-ATAC-seq
Lyu R, Gao Y, Wu T et al. · Nature communications 2024 · PMID 39127768 · doi:10.1038/s41467-024-50680-8
This dataset
Open GSE256229 in finder → Find more mouse ChIP / ATAC / CUT&Tag datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE256229_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1078738 and SRA study SRP490697. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39127768) ↗
Study design
13 conditions, mostly in duplicate
Mouse_heart_KAS-seq, ×2 Mouse_heart_KAS-seq_Input, ×2 Mouse_heart_opti-KAS-seq, ×2 Mouse_heart_opti-KAS-seq_Input, ×2 Mouse_lung_KAS-seq, ×2 Mouse_lung_KAS-seq_Input, ×2 Mouse_lung_opti-KAS-seq, ×2 Mouse_lung_opti-KAS-seq_Input, ×2 +5 more

Supports a between-group comparison across 26 samples.

How we read this: 13 replicated groups read from 26 sample titles; they account for 26 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.

Samples in this study
Datasets on a similar topic

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