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Reorganization of nuclear lamina – genome interactions upon differentiation of embryonic stem cells.

GSE17051 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 8 samples Submitted 2010/05/27 Platform GPL8840
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 8 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
8 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI.
Summary (written by the submitters)
The three-dimensional organization of chromosomes within the nucleus and its dynamics during differentiation are largely unknown. We present a genome-wide analysis of the interactions between chromatin and the nuclear lamina during differentiation of mouse embryonic stem cells (ESCs) into lineage-committed neural precursor cells (NPCs) and terminally differentiated astrocytes.
Read the full summary ↓
Chromatin in each of these cell types shows a similar organization into large lamina associated domains (LADs), which represent a transcriptionally repressive environment. During sequential differentiation steps, lamina interactions are progressively modified at hundreds of genomic locations. This remodeling is typically confined to individual transcription units and involves many genes that determine cellular identity. From ESCs to NPCs, the majority of genes that move away from the lamina are concomitantly activated. Strikingly, a significant amount remain inactive yet become primed for activation by further differentiation. These results suggest that lamina-genome interactions are widely involved in the control of gene expression programs during lineage commitment and terminal differentiation. UPDATE WM20121119: We have added per-probe Hidden Markov Model state calls for the 4 cell types. These calls are not used in Peric-Hupkes, Meuleman et al. (2010), but are used in Meuleman, Peric-Hupkes et al. (2012). The procedure to arrive at this HMM state calls is as follows: We fitted a two-state HMM whereby emissions are distributed as Student's t variables. Mean and variance of DamID signals differ between states, but the degree of freedom (nu) is the same. Gaps in the probe coverage were filled by evenly spaced null probe-values. The parameters were estimated by an adaptation of the ECME algorithm to the HMM framework, showing faster convergence than regular EM when nu is unknown (Filion et al., Cell, 2010). State calls were derived through the Viterbi algorithm. This process was repeated separately for each cell type, yielding per-probe calls. Probes in the ‘bound’ (1) state are indicated as LAD-probes, probes in the ‘unbound’ (0) state as inter-LAD-probes. UPDATE WM20130301: We have also added (1-based) BED files of constitutive LAD and inter-LAD regions (mm9) across the 4 cell types. These regions are the result of simply concatenating directly adjacent cLAD/ciLAD Hidden Markov Model state calls. Again, these calls are not used in Peric-Hupkes, Meuleman et al. (2010), but are used in Meuleman, Peric-Hupkes et al. (2012).
This dataset
Download · which file do I need?

Direct links to NCBI, 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.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need.

All supplementary files ↗
3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE17051_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

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 PRJNA117779. Searching any of these in the dataset finder brings you back here.

Samples in this study

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