Enhancer–gene maps in the human and zebrafish genomes using evolutionary linkage conservation

Abstract

The spatiotemporal expression of genes is controlled by enhancer sequences that bind transcription factors. Identifying the target genes of enhancers remains difficult because enhancers regulate gene expression over long genomic distances. To address this, we used an evolutionary approach to build two genome-wide maps of predicted enhancer–gene associations in the human and zebrafish genomes. Evolutionary conserved sequences were linked to their predicted target genes using PEGASUS, a bioinformatics method that relies on evolutionary conservation of synteny. The analysis of these maps revealed that the number of predicted enhancers linked to a gene correlate with its expression breadth. Comparison of both maps identified hundreds of putative vertebrate ancestral regulatory relationships from which we could determine that predicted enhancer–gene distances scale with genome size despite strong positional conservation. The two maps represent a resource for further studies, including the prioritization of sequence variants in whole genome sequence of patients affected by genetic diseases.

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

“…increase in transcriptional complexity, e.g. due to alternative splicing 47 , expression differences across tissues 48 , and distant enhancer interactions [49][50][51][52] , which were not accounted for in the present models. The prediction performance was thus correlated (Pearson's r = 0.616, p-value < 3e-3) with the genomic complexity of the model organisms ( Supplementary Fig.…”

The genome of the bowfin (Amia calva) illuminates the developmental evolution of ray-finned fishes

“…Bow n and gar diverged from teleosts before the TGD and thus maintained more one-to-one gene relations with tetrapods 7 and also have slower rates of molecular sequence evolution than teleosts 7,21 . Thus, the genome of the gar has been leveraged as an intermediate stepping stone or 'bridge' for identifying hidden orthologies of genetic elements between teleosts and tetrapods 7,22,23 . Adding the bow n genome, the 'holostean bridge' will capture more genomic diversity across the ray-nned tree of life and will help resolve critical questions about vertebrate evolution in which analyzing only one holostean representative is insu cient (e.g.…”

“…Total RNA was extracted (Qiagen RNeasy mini plus) from RNAlaterpreserved(Ambion) embryos/larvae at stages 22-23, 23-24, 24-25, 26-27, 28-29, and 30-3168 . Stage[22][23] was de ned as the bow n phylotypic stage by similarity to the phylotypic stages of zebra sh and medaka76 . Five embryos were pooled for stages22-23 and 24-25; one embryo/larvae for stages 23-24, 26-27, 28-29, and 30-31.…”

“…Stage[22][23] was de ned as the bow n phylotypic stage by similarity to the phylotypic stages of zebra sh and medaka76 . Five embryos were pooled for stages22-23 and 24-25; one embryo/larvae for stages 23-24, 26-27, 28-29, and 30-31. Libraries were generated with an Illumina TruSeq Stranded mRNA Library Preparation Kit with IDT for Illumina Unique Dual Index and sequenced on an Illumina NextSeq v2.5 (Mid Output ow cell, 2x75bp paired end format, 150 cycle v2.5 NextSeq500 reagent cartridge).…”

Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

“…); and 4) score-based methods (e.g. EpiTensor [61], GeneHancer [62] and PEGASUS [63,64]). Although these algorithms have significantly advanced our knowledge of ETGs, they are affected by one or more of the following issues: 1) the lack of a genome-wide exhaustive reference list of enhancers; 2) the lack of a large gold standard which is required for supervised learning algorithms, i.e.…”