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Biology subjects

Suciu, M. C.

Publications and source records attributed to Suciu, M. C..

3 recordsLinked to original sources

Multipartite super-enhancers function in an orientation-dependent manner

Transcriptional enhancers regulate gene expression in a developmental-stage and cell-specific manner. They were originally defined as individual regulatory elements that activate expression regardless of distance and orientation to their cognate genes. Genome-wide studies have shown that the mammalian enhancer landscape is much more complex, with different classes of individual enhancers and clusters of enhancer-like elements combining in additive, synergistic and redundant manners, possibly acting as single, integrated regulatory elements. These so-called super-enhancers are largely defined as clusters of enhancer-like elements which recruit particularly high levels of Mediator and often drive high levels of expression of key lineage-specific genes. Here, we analysed 78 erythroid-specific super-enhancers and showed that, as units, they preferentially interact in a directional manner, to drive expression of their cognate genes. Using the well characterised -globin super-enhancer, we show that inverting this entire structure severely downregulates -globin expression and activates flanking genes 5 of the super-enhancer. Our detailed genetic dissection of the -globin locus clearly attributes the clusters functional directionality to its sequence orientation, demonstrating that, unlike regular enhancers, super-enhancers act in an orientation-dependent manner. Together, these findings identify a novel emergent property of super-enhancers and revise current models by which enhancers are thought to contact and activate their cognate genes.

molecular biology↗

An integrated platform to systematically identify causal variants and genes for polygenic human traits.

Genome-wide association studies (GWAS) have identified over 150,000 links between common genetic variants and human traits or complex diseases. Over 80% of these associations map to polymorphisms in non-coding DNA. Therefore, the challenge is to identify disease-causing variants, the genes they affect, and the cells in which these effects occur. We have developed a platform using ATAC-seq, DNaseI footprints, NG Capture-C and machine learning to address this challenge. Applying this approach to red blood cell traits identifies a significant proportion of known causative variants and their effector genes, which we show can be validated by direct in vivo modelling.

genetics↗

Fast and Accurate Genomic Analyses using Genome Graphs

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, which impairs read alignment and downstream analysis accuracy. Reference genome structures incorporating known genetic variation have been shown to improve the accuracy of genomic analyses, but have so far remained computationally prohibitive for routine large-scale use. Here we present a graph genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million indels. Our Graph Genome Pipeline requires 6.5 hours to process a 30x coverage WGS sample on a system with 36 CPU cores compared with 11 hours required by the GATK Best Practices pipeline. Using complementary benchmarking experiments based on real and simulated data, we show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, or about 20,000 additional variants being detected per sample, while variant calling specificity is unaffected. Structural variations (SVs) incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is a significant advance towards fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

bioinformatics↗