Cell type-specific gene expression relies on transcription factors (TFs) binding DNA
sequence motifs embedded in chromatin. Understanding how motifs are accessed in …

Transcription factors (TFs) recognize specific DNA sequences to control chromatin and
transcription, forming a complex system that guides expression of the genome. Despite keen …

Abstract JASPAR (http://jaspar. genereg. net/) is an open-access database containing
manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six …

Abstract JASPAR (http://jaspar. genereg. net) is an open-access database of curated, non-
redundant transcription factor (TF)-binding profiles stored as position frequency matrices …

The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the
cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that …

Background The binding of transcription factors (TF) to genomic targets is critical in the
regulation of gene expression. Short, double-stranded DNA sequence motifs are routinely …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Abstract Design of de novo synthetic regulatory DNA is a promising avenue to control gene
expression in biotechnology and medicine. Using mutagenesis typically requires screening …

A class of cis-regulatory elements, called enhancers, play a central role in orchestrating
spatiotemporally precise gene-expression programs during development. Consequently …

Identifying functional effects of noncoding variants is a major challenge in human genetics.
To predict the noncoding-variant effects de novo from sequence, we developed a deep …