McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes

Genome Biology
Dina HafezUwe Ohler

Abstract

Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features. Predicted expression patterns were 73-98% accurate, predicted assignments showed strong Hi-C interaction enrichment, enhancer-associated histone modifications were evident, and known functional motifs were recovered. Our model provides a general framework to link globally identified enhancers to targets and contributes to deciphering the regulatory genome.

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Citations

Nov 18, 2018·Bioinformatics·Ioannis A TamposisPantelis G Bagos
Feb 8, 2020·Epigenetics & Chromatin·Florian SchmidtMarcel H Schulz
Oct 24, 2020·Briefings in Bioinformatics·Mingguang ShiHao Tang
May 7, 2021·Nature Reviews. Molecular Cell Biology·Ivana Jerkovic, Giacomo Cavalli
Aug 7, 2021·Proceedings of the National Academy of Sciences of the United States of America·Laura-Jayne GardinerAnthony Hall
Jul 30, 2021··Ronald J. NowlingJohn G. Peters

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Methods Mentioned

BETA
dissection
ChIP-seq
RNA-seq
Hi-C
transgenic
PCR

Software Mentioned

McEnahncer
McEnhancer
Vienna Tiles
Bowtie2
REDfly
AME
TomTom
GenomicRanges
HIPPIE Browser Extensible Data ( BED
VT

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