Herein we list studies in which public datasets qualified with the NGS-QC approach were used. In addition we provide links allowing to download the subset of datasets used per study.
Gene regulatory network reconstruction incorporating 3D chromosomal architecture reveals key transcription factors and DNA elements driving neural lineage commitment
Valeriya Malysheva, Marco Antonio Mendoza-Parra, Matthias Blum, Mikhail Spivakov and Hinrich Gronemeyer (soon published)
As a part of this study we analyzed 8068 mouse ChIP-seq profiles targeting >600 TFs deposited in the GEO database. TF binding sites were annotated using MACS2 with the “--nomodel --extsize 150 --gsize mm” as running parameters. Peaks with a p-value less than 1e-10-30 have been retained and used as an input for reconstruction of GRN.Download peak files
As part of this study, 2332 mouse or 3913 human TF ChIP-sequencing datasets were integrated into a gene regulatory network per model organism (qcGenomics). These networks have been reconstructed by (i) applying global/local quality-scores datasets classification (see NGS-QC Generator approach); (ii) promoter-enhancer distance criterion; as well as (iii) binding sites enrichment confidence. As part of this download networks established for different promoter-enhancer distances (2.5, 10 or 20 kb) are available; each of them computed for a minimal binding sites enrichment confidence (MACS2 peak calling; p-value=1x10-50) and a minimal quality score of CCC (quality range from A to D; see NGS-QC Generator approach).
Download qcGenomics GRN