There is growing use of ontologies for the measurement of cross-species phenotype similarity. Such similarity measurements contribute to diverse applications, such as identifying genetic models for human diseases, transferring knowledge among model organisms, and studying the genetic basis of evolutionary innovations. Two organismal features, whether genes, anatomical parts, or any other inherited feature, are considered to be homologous when they are evolutionarily derived from a single feature in a common ancestor. A classic example is the homology between the paired fins of fishes and vertebrate limbs. Anatomical ontologies that model the structural relations among parts may fail to include some known anatomical homologies unless they are deliberately added as separate axioms. The consequences of neglecting known homologies for applications that rely on such ontologies has not been well studied. Here, we examine how semantic similarity is affected when external homology knowledge is included. We measure phenotypic similarity between orthologous and non-orthologous gene pairs between humans and either mouse or zebrafish, and compare the inclusion of real with faux homology axioms. Semantic similarity was preferentially increased for orthologs when using real homology axioms, but only in the more divergent of the two species comparisons (human to zebrafish, not human to mouse), and the relative increase was less than 1% to non-orthologs. By contrast, inclusion of both real and faux random homology axioms preferentially increased similarities between genes that were initially more dissimilar in the other comparisons. Biologically meaningful increases in semantic similarity were seen for a select subset of gene pairs. Overall, the effect of including homology axioms on cross-species semantic similarity was modest at the levels of divergence examined here, but our results hint that it may be greater for more distant species comparisons.

Purpose: The pace of Mendelian gene discovery is slowed by the “n-of-1 problem” – the difficulty of establishing causality of a putatively pathogenic variant in a single person or family. Identification of an unrelated person with an overlapping phenotype and suspected pathogenic variant in the same gene can overcome this barrier but is often impeded by lack of a convenient or widely-available way to share data on candidate variants / genes among families, clinicians and researchers. Methods: Social networking among families, clinicians and researchers was used to identify three children with variants of unknown significance in KDM1A and similar phenotypes. Results: De novo variants in KDM1A underlie a new syndrome characterized by developmental delay and distinctive facial features. Conclusion: Social networking is a potentially powerful strategy to discover genes for rare Mendelian conditions, particularly those with non-specific phenotypic features. To facilitate the efforts of families to share phenotypic and genomic information with each other, clinicians, and researchers, we developed the Repository for Mendelian Genomics Family Portal (RMD-FP). Design and development of a web-based tool, MyGene2, that enables families, clinicians and researchers to search for gene matches based on analysis of phenotype and exome data deposited into the RMD-FP is underway.

QuicK-mer is a unified pipeline for estimating genome copy-number from high-throughput Illumina sequencing data. QuicK-mer utilizes the Jellyfish application to efficiently tabulate counts of predefined sets of k-mers. The program performs GC-normalization using defined control regions and reports paralog-specific estimates of copy-number suitable for downstream analysis. The package is freely available at https://github.com/KiddLab/QuicK-mer

Complex chromosomal rearrangements consist of structural genomic alterations involving multiple instances of deletions, duplications, inversions, or translocations that co-occur either on the same chromosome or represent different overlapping events on homologous chromosomes. We present SVelter, an algorithm that first identifies regions of the genome suspected to harbor a complex event and then iteratively rearranges the local genome structure, in a randomized fashion, with each structure scored against characteristics of the observed sequencing data. We show that SVelter is able to accurately reconstruct these regions when compared to well-characterized genomes that have been deep sequenced with both short and long read technologies.

Horizontal Gene Transfer (HGT) in eukaryotic plastids and mitochondrial genomes is frequently observed, and plays an important role in organism evolution. In yeasts, recent mitochondrial HGT has been suggested between S. cerevisiae and S. paradoxus. However, few strains have been explored due to the lack of accurate mitochondrial genome annotations. Mitochondrial genome sequences are important to understand how frequent these introgressions occur and their role in cytonuclear incompatibilities. In fact, most of the Bateson-Dobzhansky-Muller genetic incompatibilities described in yeasts are driven by these cytonuclear incompatibilities. In this study, we have explored the mitochondrial inheritance of several worldwide distributed Saccharomyces species isolated from different sources and geographic origins. We demonstrated the existence of recombination hotspots in the mitochondrial region COX2-ORF1, likely mediated by the transfer of two different types of ORF1, encoding a free-standing homing endonuclease, or facilitated by AT tandem repeats and GC clusters. These introgressions were shown to occur both at intra- and interspecific levels. Based on our results we proposed a model which involve several ancestral hybridization events among Saccharomyces strains in wild environments.

Recent work has leveraged the unique genealogical structure and extensive genotyping (>30%) of the Icelandic population to perform long-range phasing (LRP), enabling accurate imputation and association analysis of rare variants in target samples typed on genotyping arrays. Here, we develop a fast and accurate LRP method, Eagle, that extends this paradigm to outbred populations by harnessing long (>4cM) identical-by-descent (IBD) tracts shared among distantly related individuals. We applied Eagle to N=150K samples (0.2% of the British population) from the UK Biobank, and we determined that it is 1-2 orders of magnitude faster than existing methods while achieving exquisite phasing accuracy (switch error rate ≈0.3%, corresponding to perfect phase at the scale of >10Mb). Moreover, we observed that Eagle imputed masked genotypes with accuracy R2>0.75 down to a minor allele frequency of 0.1%. Compared to computationally tractable alternatives, Eagle attained large improvements in phasing and imputation accuracy at N=150K and smaller improvements at smaller sample sizes, illustrating the advantages that LRP-based imputation will yield as very large reference panels become available.

Recombination has essential roles in increasing genetic variability within a population and in ensuring successful meiotic events. The objective of this study is to (i) infer the population scaled recombination rate (ρ), and (ii) identify and characterize localities of increased recombination rate for the domestic cat, Felis silvestris catus. SNPs (n = 701) were genotyped in twenty-two cats of Eastern random bred origin. The SNPs covered ten different chromosomal regions (A1, A2, B3, C2, D1, D2, D4, E2, F2, X) with an average region size of 850 Kb and an average SNP density of 70 SNPs/region. The Bayesian method in the program inferRho was used to infer regional population recombination rates and hotspots localities. The regions exhibited variable population recombination rates and four decisive recombination hotspots were identified on cat chromosome A2, D1, and E2 regions. No correlation was detected between the GC content and the locality of recombination spots. The hotspots enclosed L2 LINE elements and MIR and tRNA-Lys SINE elements in agreement with hotspots found in other mammals.

As the second most common type of variations in the human genome, insertions and deletions (indels) have been linked to many diseases, but indels of more than a few bases are still challenging to discover from short-read sequencing data. Scalpel (http://scalpel.sourceforge.net) is open-source software for reliable indel detection based on the micro-assembly technique. To date, it has been successfully used to discover mutations in novel candidate genes for autism, and is extensively used in other large-scale studies of human diseases. This protocol gives an overview of the algorithm and describes how to use Scalpel to perform highly accurate indel calling from whole genome and exome sequencing data. We provide detailed instructions for an exemplary family-based de novo study, but we also characterize the other two supported modes of operation for single sample and somatic analysis. Indel normalization, visualization, and annotation of the mutations are also illustrated. Using a standard server, indel discovery and characterization in the exonic regions of the example sequencing data can be finished in ~6 hours after read mapping.

Abstract The availability of genomes across the tree of life is highly biased toward vertebrates, pathogens, human disease models, and organisms with small and streamlined genomes. Recent progress in genomics has enabled the de novo decoding of the genome of virtually any organism, greatly expanding its potential for understanding the biology and evolution of the full spectrum of biodiversity. The increasing diversity of sequencing technologies, assays, and de novo assembly algorithms have augmented the complexity of de novo genome sequencing projects in non-model organisms. To reduce the costs and challenges in de novo genome sequencing projects and streamline their experimental design and analysis, we developed iWGS (in silico Whole Genome Sequencer and Analyzer), an automated pipeline for guiding the choice of appropriate sequencing strategy and assembly protocols. iWGS seamlessly integrates the four key steps of a de novo genome sequencing project: data generation (through simulation), data quality control, de novo assembly, and assembly evaluation and validation. The last three steps can also be applied to the analysis of real data. iWGS is designed to enable the user to have great flexibility in testing the range of experimental designs available for genome sequencing projects, and supports all major sequencing technologies and popular assembly tools. Three case studies illustrate how iWGS can guide the design of de novo genome sequencing projects and evaluate the performance of a wide variety of user-specified sequencing strategies and assembly protocols on genomes of differing architectures. iWGS, along with a detailed documentation, is freely available at http://as.vanderbilt.edu/rokaslab/tools.html.