Supergenes on Steroids

At the birth of supergenes, the genomic landscape is dramatically re-organized leading to pronounced differences in phenotypes and increased intrasexual diversity. Two of the best-studied supergenes in vertebrates are arguably the inversion polymorphisms on chromosomes 2 and 11 in the white-throated sparrow (Zonotrichia albicollis) and the ruff (Calidris pugnax), respectively. In both species, regions of suppressed recombination determine plumage coloration and social behavioral phenotypes. Despite the apparent lack of gene overlap between these two supergenes, in both cases the alternative phenotypes seem to be driven largely by alterations in steroid hormone pathways. Here, we explore the interplay between genomic architecture and steroid-related genes. Due to the highly pleiotropic effects of such genes and their universal involvement in social behavior and genomic architecture, forces favouring their linkage are likely to have substantial effects on the evolution of behavioral phenotypes, individual fitness, and life history strategies. We propose that the differentiation of steroid-related genes, inside both supergenes, lies at the core of phenotypic differentiation in both of these interesting species.

simplifyEnrichment: an R/Bioconductor package for Clustering and Visualizing Functional Enrichment Results

MotivationFunctional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.Resultswe proposed a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut can efficiently cluster functional terms into groups where terms showed more consistent similarities within groups and were more mutually exclusive between groups. We compared binary cut clustering on the similarity matrices from different similarity measurements and we found the semantic similarity worked well with binary cut while the similarity matrices based on gene overlap showed less consistent patterns and they were not recommended to work with binary cut. We implemented the binary cut algorithm into an R package simplifyEnrichment which additionally provides functionalities for visualizing, summarizing and comparing the clusterings.Availability and implementationThe simplifyEnrichment package and the documentations are available at https://bioconductor.org/packages/simplifyEnrichment/. The reports for the analysis of all datasets benchmarked in the paper are available at https://simplifyenrichment.github.io/. The scripts that performed the analysis are available at https://github.com/jokergoo/simplifyEnrichment_manuscript.

Genome modularization reveals overlapped gene topology is necessary for efficient viral reproduction

ABSTRACTSequence overlap between two genes is common across all genomes, with viruses having high proportions of these gene overlaps. The biological function and fitness effects of gene overlaps are not fully understood, and their effects on gene cluster and genome-level refactoring are unknown. The bacteriophage ϕX174 genome has ∼26% of nucleotides involved in encoding more than one gene. In this study we use an engineered ϕX174 phage containing a genome with all gene overlaps removed, to show that gene overlap is critical to maintaining optimal viral fecundity. Through detailed phenotypic measurements we reveal that genome modularization in ϕX174 causes virion replication, stability, and attachment deficiencies. Quantitation of the complete phage proteome across an infection cycle reveals almost half the proteins display abnormal expression patterns. Taken together, we have for the first time comprehensively demonstrated that gene modularization severely perturbs the coordinated functioning of a bacteriophage replication cycle. This work highlights the biological importance of gene overlap in natural genomes and that reducing gene overlap disruption should be an integral part of future genome engineering projects.

Properties and abundance of overlapping genes in viruses

Overlapping genes are commonplace in viruses and play an important role in their function and evolution. However, aside from studies on specific groups of viruses, relatively little is known about the extent and nature of gene overlap and its determinants in viruses as a whole. Here, we present an extensive characterisation of gene overlap in viruses through an analysis of reference genomes present in the NCBI virus genome database. We find that over half the instances of gene overlap are very small, covering <10 nt, and 84 per cent are <50 nt in length. Despite this, 53 per cent of all viruses still contained a gene overlap of 50 nt or larger. We also investigate several predictors of gene overlap such as genome structure (single- and double-stranded RNA and DNA), virus family, genome length, and genome segmentation. This revealed that gene overlap occurs more frequently in DNA viruses than in RNA viruses, and more frequently in single-stranded viruses than in double-stranded viruses. Genome segmentation is also associated with gene overlap, particularly in single-stranded DNA viruses. Notably, we observed a large range of overlap frequencies across families of all genome types, suggesting that it is a common evolutionary trait that provides flexible genome structures in all virus families.

Optimized functional annotation of ChIP-seq data

Different ChIP-seq peak callers often produce different output results from the same input. Since different peak callers are known to produce differentially enriched peaks with a large variance in peak length distribution and total peak count, accurately annotating peak lists with their nearest genes can be an arduous process. Functional genomic annotation of histone modification ChIP-seq data can be a particularly challenging task, as chromatin marks that have inherently broad peaks with a diffuse range of signal enrichment (e.g., H3K9me1, H3K27me3) differ significantly from narrow peaks that exhibit a compact and localized enrichment pattern (e.g., H3K4me3, H3K9ac). In addition, varying degrees of tissue-dependent broadness of an epigenetic mark can make it difficult to accurately and reliably link sequencing data to biological function. Thus, there exists an unmet need to develop a software program that can precisely tailor the computational analysis of a ChIP-seq dataset to the specific peak coordinates of the data and its surrounding genomic features. geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to investigate peak summary statistics for the first-closest gene, second-closest gene, ..., nth-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. We tested geneXtendeR on 547 human transcription factor ChIP-seq ENCODE datasets and 198 human histone modification ChIP-seq ENCODE datasets, providing the analysis results as case studies. The geneXtendeR R/Bioconductor package (including detailed introductory vignettes) is available under the GPL-3 Open Source license and is freely available to download from Bioconductor at: https://bioconductor.org/packages/geneXtendeR/

Sense-antisense gene overlap causes evolutionary retention of the few introns in Giardia genome and the implications

BackgroundIt is widely accepted that the last eukaryotic common ancestor (LECA) and early eukaryotes were intron-rich and intron loss dominated subsequent evolution, thus the presence of only very few introns in some modern eukaryotes must be the consequence of massive loss. But it is striking that few eukaryotes were found to have completely lost introns. Despite extensive research, the causes of massive intron losses remain elusive, and actually the reverse question – how the few introns are retained under the pressure of loss is equally significant but was rarely studied, except that it was conjectured that the essential functions of some introns prevent their loss. The extremely few (eight) spliceosome-mediated cis-spliced introns in the relatively simple genome of Giardia lamblia provide an excellent opportunity to explore this question.ResultsOur investigation of the intron-containing genes and introns in Giardia found three types of intron distribution patterns: ancient intron in ancient gene, relatively new intron in ancient gene, and relatively new intron in relatively new gene, which can reflect to some extent the dynamic evolution of introns in Giardia. Not finding any special features or functional importance of these introns responsible for the retention, we noticed and experimentally verified that some intron-containing genes form sense-antisense gene pairs with functional genes on their complementary strands, and that the introns just reside in the overlapping regions.ConclusionsIn Giardia’s evolution, despite constant pressure of intron loss, intron gain can still occur in both ancient and newly-evolved genes, but only a few introns have been retained; the evolutionary retention of introns is most likely not due to the functional constraint of the introns themselves but the causes outside of introns, such as the constraints imposed by other genomic functional elements overlapping with the introns. These findings can not only provide some clues to find new genomic functional elements -- in the areas overlapping with introngs, but suggest that “functional constraint” of introns may not be necessarily directly associated with intron loss and gain, or that the real functions or the way of functioning of introns are probably still outside of our current knowledge.

A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities

Cross-sectional epidemiological studies have shown that the incidence of several nervous system diseases is more frequent in epilepsy patients than in the general population. Some comorbidities (e.g., Alzheimer’s disease and Parkinson’s disease) are also risk factors for the development of seizures; suggesting they may share pathophysiological mechanisms with epilepsy.A literature-based approach was used to identify gene overlap between epilepsy and its comorbidities as a proxy for a shared genetic basis for disease, or genetic pleiotropy, as a first effort to identify shared mechanisms. While the results identified neurological disorders as the group of diseases with the highest gene overlap, this analysis was insufficient for identifying putative common mechanisms shared across epilepsy and its comorbidities. This motivated the use of a dedicated literature mining and knowledge assembly approach in which a cause-and-effect model of epilepsy was captured with Biological Expression Language.After enriching the knowledge assembly with information surrounding epilepsy, its risk factors, its comorbidities, and antiepileptic drugs, a novel comparative mechanism enrichment approach was used to propose several downstream effectors (including the GABA receptor, GABAergic pathways, etc.) that could explain the therapeutic effects carbamazepine in both the contexts of epilepsy and AD.We have made the Epilepsy Knowledge Assembly available at https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/epilepsy.bel and queryable through NeuroMMSig at http://neurommsig.scai.fraunhofer.de. The source code used for analysis and tutorials for reproduction are available on GitHub at https://github.com/cthoyt/epicom.