Decoding the regulatory architecture of the maize leaf
Transcription factors (TF) binding is key to understanding and characterizing the effect of genetic variability on phenotypic differences. Here, we used a novel scalable ChIP-seq approach to annotate the regulatory landscape of the maize genome with binding data from 104 leaf TFs. TF binding regions co-localized with open chromatin regions, with ~70% of TF binding nearby genes. TF binding sites are evolutionarily conserved and show enrichment for GWAS-hits, cis-expression QTLs. Furthermore, the...
Provenance — who produced it, who reused it
Linked to 3 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
2 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently