Reconstruction of gross avian genome structure, organization and evolution suggests that the chicken lineage most closely resembles the dinosaur avian ancestor.

BACKGROUND: The availability of multiple avian genome sequence assemblies greatly improves our ability to define overall genome organization and reconstruct evolutionary changes. In birds, this has previously been impeded by a near intractable karyotype and relied almost exclusively on comparative molecular cytogenetics of only the largest chromosomes. Here, novel whole genome sequence information from 21 avian genome sequences (most newly assembled) made available on an interactive browser (Evolution Highway) was analyzed. RESULTS: Focusing on the six best-assembled genomes allowed us to assemble a putative karyotype of the dinosaur ancestor for each chromosome. Reconstructing evolutionary events that led to each species' genome organization, we determined that the fastest rate of change occurred in the zebra finch and budgerigar, consistent with rapid speciation events in the Passeriformes and Psittaciformes. Intra- and interchromosomal changes were explained most parsimoniously by a series of inversions and translocations respectively, with breakpoint reuse being commonplace. Analyzing chicken and zebra finch, we found little evidence to support the hypothesis of an association of evolutionary breakpoint regions with recombination hotspots but some evidence to support the hypothesis that microchromosomes largely represent conserved blocks of synteny in the majority of the 21 species analyzed. All but one species showed the expected number of microchromosomal rearrangements predicted by the haploid chromosome count. Ostrich, however, appeared to retain an overall karyotype structure of 2n=80 despite undergoing a large number (26) of hitherto un-described interchromosomal changes. CONCLUSIONS: Results suggest that mechanisms exist to preserve a static overall avian karyotype/genomic structure, including the microchromosomes, with widespread interchromosomal change occurring rarely (e.g., in ostrich and budgerigar lineages). Of the species analyzed, the chicken lineage appeared to have undergone the fewest changes compared to the dinosaur ancestor.

Pharmacogenomics in early-phase clinical development.

Pharmacogenomics (PGx) offers the promise of utilizing genetic fingerprints to predict individual responses to drugs in terms of safety, efficacy and pharmacokinetics. Early-phase clinical trial PGx applications can identify human genome variations that are meaningful to study design, selection of participants, allocation of resources and clinical research ethics. Results can inform later-phase study design and pipeline developmental decisions. Nevertheless, our review of the clinicaltrials.gov database demonstrates that PGx is rarely used by drug developers. Of the total 323 trials that included PGx as an outcome, 80% have been conducted by academic institutions after initial regulatory approval. Barriers for the application of PGx are discussed. We propose a framework for the role of PGx in early-phase drug development and recommend PGx be universally considered in study design, result interpretation and hypothesis generation for later-phase studies, but PGx results from underpowered studies should not be used by themselves to terminate drug-development programs.

Crowdfunding the Azolla fern genome project: a grassroots approach.

Much of science progresses within the tight boundaries of what is often seen as a "black box". Though familiar to funding agencies, researchers and the academic journals they publish in, it is an entity that outsiders rarely get to peek into. Crowdfunding is a novel means that allows the public to participate in, as well as to support and witness advancements in science. Here we describe our recent crowdfunding efforts to sequence the Azolla genome, a little fern with massive green potential. Crowdfunding is a worthy platform not only for obtaining seed money for exploratory research, but also for engaging directly with the general public as a rewarding form of outreach.

Genome-wide association study of perioperative myocardial infarction after coronary artery bypass surgery.

OBJECTIVES: Identification of patient subpopulations susceptible to develop myocardial infarction (MI) or, conversely, those displaying either intrinsic cardioprotective phenotypes or highly responsive to protective interventions remain high-priority knowledge gaps. We sought to identify novel common genetic variants associated with perioperative MI in patients undergoing coronary artery bypass grafting using genome-wide association methodology. SETTING: 107 secondary and tertiary cardiac surgery centres across the USA. PARTICIPANTS: We conducted a stage I genome-wide association study (GWAS) in 1433 ethnically diverse patients of both genders (112 cases/1321 controls) from the Genetics of Myocardial Adverse Outcomes and Graft Failure (GeneMAGIC) study, and a stage II analysis in an expanded population of 2055 patients (225 cases/1830 controls) combined from the GeneMAGIC and Duke Perioperative Genetics and Safety Outcomes (PEGASUS) studies. Patients undergoing primary non-emergent coronary bypass grafting were included. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome variable was perioperative MI, defined as creatine kinase MB isoenzyme (CK-MB) values ≥10× upper limit of normal during the first postoperative day, and not attributable to preoperative MI. Secondary outcomes included postoperative CK-MB as a quantitative trait, or a dichotomised phenotype based on extreme quartiles of the CK-MB distribution. RESULTS: Following quality control and adjustment for clinical covariates, we identified 521 single nucleotide polymorphisms in the stage I GWAS analysis. Among these, 8 common variants in 3 genes or intergenic regions met p<10(-5) in stage II. A secondary analysis using CK-MB as a quantitative trait (minimum p=1.26×10(-3) for rs609418), or a dichotomised phenotype based on extreme CK-MB values (minimum p=7.72×10(-6) for rs4834703) supported these findings. Pathway analysis revealed that genes harbouring top-scoring variants cluster in pathways of biological relevance to extracellular matrix remodelling, endoplasmic reticulum-to-Golgi transport and inflammation. CONCLUSIONS: Using a two-stage GWAS and pathway analysis, we identified and prioritised several potential susceptibility loci for perioperative MI.

An Exploration into Fern Genome Space.

Ferns are one of the few remaining major clades of land plants for which a complete genome sequence is lacking. Knowledge of genome space in ferns will enable broad-scale comparative analyses of land plant genes and genomes, provide insights into genome evolution across green plants, and shed light on genetic and genomic features that characterize ferns, such as their high chromosome numbers and large genome sizes. As part of an initial exploration into fern genome space, we used a whole genome shotgun sequencing approach to obtain low-density coverage (∼0.4X to 2X) for six fern species from the Polypodiales (Ceratopteris, Pteridium, Polypodium, Cystopteris), Cyatheales (Plagiogyria), and Gleicheniales (Dipteris). We explore these data to characterize the proportion of the nuclear genome represented by repetitive sequences (including DNA transposons, retrotransposons, ribosomal DNA, and simple repeats) and protein-coding genes, and to extract chloroplast and mitochondrial genome sequences. Such initial sweeps of fern genomes can provide information useful for selecting a promising candidate fern species for whole genome sequencing. We also describe variation of genomic traits across our sample and highlight some differences and similarities in repeat structure between ferns and seed plants.

High-coverage sequencing and annotated assemblies of the budgerigar genome.

BACKGROUND: Parrots belong to a group of behaviorally advanced vertebrates and have an advanced ability of vocal learning relative to other vocal-learning birds. They can imitate human speech, synchronize their body movements to a rhythmic beat, and understand complex concepts of referential meaning to sounds. However, little is known about the genetics of these traits. Elucidating the genetic bases would require whole genome sequencing and a robust assembly of a parrot genome. FINDINGS: We present a genomic resource for the budgerigar, an Australian Parakeet (Melopsittacus undulatus) -- the most widely studied parrot species in neuroscience and behavior. We present genomic sequence data that includes over 300× raw read coverage from multiple sequencing technologies and chromosome optical maps from a single male animal. The reads and optical maps were used to create three hybrid assemblies representing some of the largest genomic scaffolds to date for a bird; two of which were annotated based on similarities to reference sets of non-redundant human, zebra finch and chicken proteins, and budgerigar transcriptome sequence assemblies. The sequence reads for this project were in part generated and used for both the Assemblathon 2 competition and the first de novo assembly of a giga-scale vertebrate genome utilizing PacBio single-molecule sequencing. CONCLUSIONS: Across several quality metrics, these budgerigar assemblies are comparable to or better than the chicken and zebra finch genome assemblies built from traditional Sanger sequencing reads, and are sufficient to analyze regions that are difficult to sequence and assemble, including those not yet assembled in prior bird genomes, and promoter regions of genes differentially regulated in vocal learning brain regions. This work provides valuable data and material for genome technology development and for investigating the genomics of complex behavioral traits.

Response to Comment on "Whole-genome analyses resolve early branches in the tree of life of modern birds".

Mitchell et al. argue that divergence-time estimates for our avian phylogeny were too young because of an "inappropriate" maximum age constraint for the most recent common ancestor of modern birds and that, as a result, most modern bird orders diverged before the Cretaceous-Paleogene mass extinction event 66 million years ago instead of after. However, their interpretations of the fossil record and timetrees are incorrect.

Whole genome sequencing identifies circulating Beijing-lineage Mycobacterium tuberculosis strains in Guatemala and an associated urban outbreak.

Limited data are available regarding the molecular epidemiology of Mycobacterium tuberculosis (Mtb) strains circulating in Guatemala. Beijing-lineage Mtb strains have gained prevalence worldwide and are associated with increased virulence and drug resistance, but there have been only a few cases reported in Central America. Here we report the first whole genome sequencing of Central American Beijing-lineage strains of Mtb. We find that multiple Beijing-lineage strains, derived from independent founding events, are currently circulating in Guatemala, but overall still represent a relatively small proportion of disease burden. Finally, we identify a specific Beijing-lineage outbreak centered on a poor neighborhood in Guatemala City.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

The Physarum polycephalum Genome Reveals Extensive Use of Prokaryotic Two-Component and Metazoan-Type Tyrosine Kinase Signaling.

Physarum polycephalum is a well-studied microbial eukaryote with unique experimental attributes relative to other experimental model organisms. It has a sophisticated life cycle with several distinct stages including amoebal, flagellated, and plasmodial cells. It is unusual in switching between open and closed mitosis according to specific life-cycle stages. Here we present the analysis of the genome of this enigmatic and important model organism and compare it with closely related species. The genome is littered with simple and complex repeats and the coding regions are frequently interrupted by introns with a mean size of 100 bases. Complemented with extensive transcriptome data, we define approximately 31,000 gene loci, providing unexpected insights into early eukaryote evolution. We describe extensive use of histidine kinase-based two-component systems and tyrosine kinase signaling, the presence of bacterial and plant type photoreceptors (phytochromes, cryptochrome, and phototropin) and of plant-type pentatricopeptide repeat proteins, as well as metabolic pathways, and a cell cycle control system typically found in more complex eukaryotes. Our analysis characterizes P. polycephalum as a prototypical eukaryote with features attributed to the last common ancestor of Amorphea, that is, the Amoebozoa and Opisthokonts. Specifically, the presence of tyrosine kinases in Acanthamoeba and Physarum as representatives of two distantly related subdivisions of Amoebozoa argues against the later emergence of tyrosine kinase signaling in the opisthokont lineage and also against the acquisition by horizontal gene transfer.