Evaluating Ortholog Prediction Algorithms in a Yeast Model Clade

RSD, respectively, so that they can predict orthologs across multiple taxa) against a set of 2,723 groups of high-quality curated orthologs from 6 Saccharomycete yeasts in the Yeast Gene Order Browser. of all algorithms dramatically increased in these traps.) for evolutionary and functional genomics studies where the objective is the accurate inference of single-copy orthologs (e.g., molecular phylogenetics), but that all algorithms fail to accurately predict orthologs when paralogy is rampant

Complete Bacteriophage Transfer in a Bacterial Endosymbiont (Wolbachia) Determined by Targeted Genome Capture

Bacteriophage flux can cause the majority of genetic diversity in free-living bacteria. This tenet of bacterial genome evolution generally does not extend to obligate intracellular bacteria owing to their reduced contact with other microbes and a predominance of gene deletion over gene transfer. However, recent studies suggest intracellular coinfections in the same host can facilitate exchange of mobile elements between obligate intracellular bacteria—a means by which these bacteria can partially mitigate the reductive forces of the intracellular lifestyle. To test whether bacteriophages transfer as single genes or larger regions between coinfections, we sequenced the genome of the obligate intracellular Wolbachia strain wVitB from the parasitic wasp Nasonia vitripennis and compared it against the prophage sequences of the divergent wVitA coinfection. We applied, for the first time, a targeted sequence capture array to specifically trap the symbiont's DNA from a heterogeneous mixture of eukaryotic, bacterial, and viral DNA. The tiled array successfully captured the genome with 98.3% efficiency. Examination of the genome sequence revealed the largest transfer of bacteriophage and flanking genes (52.2 kb) to date between two obligate intracellular coinfections. The mobile element transfer occurred in the recent evolutionary past based on the 99.9% average nucleotide identity of the phage sequences between the two strains. In addition to discovering an evolutionary recent and large-scale horizontal phage transfer between coinfecting obligate intracellular bacteria, we demonstrate that “targeted genome capture” can enrich target DNA to alleviate the problem of isolating symbiotic microbes that are difficult to culture or purify from the conglomerate of organisms inside eukaryotes

Multi-tissue integrative analysis of personal epigenomes

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis.

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available

Identification of the miRNAome in human fracture callus and nonunion tissues

Background: Nonunions remain a challenging post-traumatic complication that often leads to a financial and health burden that affects the patient's quality of life. Despite a wealth of knowledge about fracture repair, especially gene and more recently miRNA expression, much remains unknown about the molecular differences between normal physiological repair (callus tissue) and a nonunion. To probe this lack of knowledge, we embarked on a study that sought to identify and compare the human miRNAome of normal bone to that present in a normal fracture callus and those from two different classic nonunion types, hypertrophic and oligotrophic. Methods: Normal bone and callus tissue samples were harvested during revision surgery from patients with physiological fracture repair and nonunions (hypertrophic and oligotrophic) and analyzed using histology. Also, miRNAs were isolated and screened using microarrays followed by bioinformatic analyses, including, differential expression, pathways and biological processes, as well as elucidation of target genes. Results: Out of 30,424 mature miRNAs (from 203 organisms) screened via microarrays, 635 (∼2.1%) miRNAs were found to be upregulated and 855 (∼2.8%) downregulated in the fracture callus and nonunion tissues as compared to intact bone. As our tissue samples were derived from humans, we focused on the human miRNAs and out of the 4223 human miRNAs, 86 miRNAs (∼2.0%) were upregulated and 51 (∼1.2%) were downregulated. Although there were similarities between the three experimental samples, we also found specific miRNAs that were unique to individual samples. We further identified the predicted target genes from these differentially expressed miRNAs as well as the relevant biological processes, including specific signaling pathways that are activated in all three experimental samples. Conclusion: Collectively, this is the first comprehensive study reporting on the miRNAome of intact bone as compared to fracture callus and nonunion tissues. Further, we identify specific miRNAs involved in normal physiological fracture repair as well as those of nonunions. The translational potential of this article: The data generated from this study further increase our molecular understanding of the roles of miRNAs during normal and aberrant fracture repair and this knowledge can be used in the future in the development of miRNA-based therapeutics for skeletal regeneration

Genetic determination of regional connectivity in modelling the spread of COVID-19 outbreak for more efficient mitigation strategies

Abstract For the COVID-19 pandemic, viral transmission has been documented in many historical and geographical contexts. Nevertheless, few studies have explicitly modeled the spatiotemporal flow based on genetic sequences, to develop mitigation strategies. Additionally, thousands of SARS-CoV-2 genomes have been sequenced with associated records, potentially providing a rich source for such spatiotemporal analysis, an unprecedented amount during a single outbreak. Here, in a case study of seven states, we model the first wave of the outbreak by determining regional connectivity from phylogenetic sequence information (i.e. “genetic connectivity”), in addition to traditional epidemiologic and demographic parameters. Our study shows nearly all of the initial outbreak can be traced to a few lineages, rather than disconnected outbreaks, indicative of a mostly continuous initial viral flow. While the geographic distance from hotspots is initially important in the modeling, genetic connectivity becomes increasingly significant later in the first wave. Moreover, our model predicts that isolated local strategies (e.g. relying on herd immunity) can negatively impact neighboring regions, suggesting more efficient mitigation is possible with unified, cross-border interventions. Finally, our results suggest that a few targeted interventions based on connectivity can have an effect similar to that of an overall lockdown. They also suggest that while successful lockdowns are very effective in mitigating an outbreak, less disciplined lockdowns quickly decrease in effectiveness. Our study provides a framework for combining phylodynamic and computational methods to identify targeted interventions