Faculty Opinions recommendation of Human cardiac cis-regulatory elements, their cognate transcription factors, and regulatory DNA sequence variants.

ABSTRACTMotivationChromatin immuno-precipitation sequencing (ChIP-Seq) of histone post-translational modifications coupled with de novo motif elucidation and enrichment analyses can identify transcription factors responsible for orchestrating transitions between cell-and disease-states. However, the identified regulatory elements can span several kilobases (kb) in length, which complicates motif-based analyses. Restricting the length of the target DNA sequence(s) can reduce false positives. Therefore, we present HisTrader, a computational tool to identify the regions accessible to transcription factors, nucleosome free regions (NFRs), within histone modification peaks to reduce the DNA sequence length required for motif analyses.ResultsHisTrader accurately identifies NFRs from H3K27Ac ChIP-seq profiles of the lung cancer cell line A549, which are validated by the presence of DNaseI hypersensitivity. In addition, HisTrader reveals that multiple NFRs are common within individual regulatory elements; an easily overlooked feature that should be considered to improve sensitivity of motif analyses using histone modification ChIP-seq data.Availability and implementationThe HisTrader script is open-source and available on GitHub (https://github.com/SvenBaileyLab/Histrader) under a GNU general public license (GPLv3). HisTrader is written in PERL and can be run on any platform with PERL installed.

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Learning immune cell differentiation

10.1101/2019.12.21.885814 ◽

2019 ◽

Cited By ~ 2

Author(s):

Alexandra Maslova ◽

Ricardo N. Ramirez ◽

Ke Ma ◽

Hugo Schmutz ◽

Chendi Wang ◽

...

Keyword(s):

Transcription Factors ◽

Dna Sequence ◽

Immune Cell ◽

Cell Types ◽

Regulatory Elements ◽

Open Chromatin ◽

Binding Motifs ◽

Human Dna ◽

High Concordance ◽

Cell Type Specific

SUMMARYThe mammalian genome contains several million cis-regulatory elements, whose differential activity marked by open chromatin determines organogenesis and differentiation. This activity is itself embedded in the DNA sequence, decoded by sequence-specific transcription factors. Leveraging a granular ATAC-seq atlas of chromatin activity across 81 immune cell-types we show that a convolutional neural network (“AI-TAC”) can learn to infer cell-type-specific chromatin activity solely from the DNA sequence. AI-TAC does so by rediscovering, with astonishing precision, binding motifs for known regulators, and some unknown ones, mapping them with high concordance to positions validated by ChIP-seq data. AI-TAC also uncovers combinatorial influences, establishing a hierarchy of transcription factors (TFs) and their interactions involved in immunocyte specification, with intriguingly different strategies between lineages. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs. Thus, Deep Learning can reveal the regulatory syntax that drives the full differentiative complexity of the immune system.

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