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Background The domestic pig (Sus scrofa) is important both as a food source and as a biomedical model given its similarity in size, anatomy, physiology, metabolism, pathology, and pharmacology to humans. The draft reference genome (Sscrofa10.2) of a purebred Duroc female pig established using older clone-based sequencing methods was incomplete, and unresolved redundancies, short-range order and orientation errors, and associated misassembled genes limited its utility. Results We present 2 annotated highly contiguous chromosome-level genome assemblies created with more recent long-read technologies and a whole-genome shotgun strategy, 1 for the same Duroc female (Sscrofa11.1) and 1 for an outbred, composite-breed male (USMARCv1.0). Both assemblies are of substantially higher (>90-fold) continuity and accuracy than Sscrofa10.2. Conclusions These highly contiguous assemblies plus annotation of a further 11 short-read assemblies provide an unprecedented view of the genetic make-up of this important agricultural and biomedical model species. We propose that the improved Duroc assembly (Sscrofa11.1) become the reference genome for genomic research in pigs.

Significance Stacking traits in microalgae is limited by a lack of robust genome modification tools and selectable marker availability. This presents a key hurdle in developing strains for renewable products including biofuels. Here, we overcome these limitations by combining inducible Cre recombinase with constitutive Cas9 nuclease expression in the industrial strain, Nannochloropsis gaditana. With this system, we demonstrate marker- and reporter-free recapitulation of an important lipid productivity trait. In addition, we generate a strain harboring seven-gene knockouts within the photosystem antennae encoding genes. The combined use of relatively mature (Cre) and emerging (CAS9) genome modification technologies can thus accelerate the pace of industrial strain development and facilitate basic research into functionally redundant gene families., Robust molecular tool kits in model and industrial microalgae are key to efficient targeted manipulation of endogenous and foreign genes in the nuclear genome for basic research and, as importantly, for the development of algal strains to produce renewable products such as biofuels. While Cas9-mediated gene knockout has been demonstrated in a small number of algal species with varying efficiency, the ability to stack traits or generate knockout mutations in two or more loci are often severely limited by selectable agent availability. This poses a critical hurdle in developing production strains, which require stacking of multiple traits, or in probing functionally redundant gene families. Here, we combine Cas9 genome editing with an inducible Cre recombinase in the industrial alga Nannochloropsis gaditana to generate a strain, NgCas9+Cre+, in which the potentially unlimited stacking of knockouts and addition of new genes is readily achievable. Cre-mediated marker recycling is first demonstrated in the removal of the selectable marker and GFP reporter transgenes associated with the Cas9/Cre construct in NgCas9+Cre+. Next, we show the proof-of-concept generation of a markerless knockout in a gene encoding an acyl-CoA oxidase (Aco1), as well as the markerless recapitulation of a 2-kb insert in the ZnCys gene 5′-UTR, which results in a doubling of wild-type lipid productivity. Finally, through an industrially oriented process, we generate mutants that exhibit up to ∼50% reduction in photosynthetic antennae size by markerless knockout of seven genes in the large light-harvesting complex gene family.

Computational assignment of function to proteins with no known homologs is still an unsolved problem. We have created a novel, function-based approach to protein annotation and discovery called D-SPACE (Deep Semantic Protein Annotation Classification and Exploration), comprised of a multi-task, multi-label deep neural network trained on over 70 million proteins. Distinct from homology and motif-based methods, D-SPACE encodes proteins in high-dimensional representations (embeddings), allowing the accurate assignment of over 180,000 labels for 13 distinct tasks. The embedding representation enables fast searches for functionally related proteins, including homologs undetectable by traditional approaches. D-SPACE annotates all 109 million proteins in UniProt in under 35 hours on a single computer and searches the entirety of these in seconds. D-SPACE further quantifies the relative functional effect of mutations, facilitating rapid in silico mutagenesis for protein engineering applications. D-SPACE incorporates protein annotation, search, and other exploratory efforts into a single cohesive model.

Objectives Clinical performance of a low coverage, low cost, massively parallel sequencing (MPS)-based assay to stratify risk of trisomy 21, 18, and 13 pregnancies was determined. Methods The study included 1100 samples with birth outcome or karyotype results, comprising low-risk patients (84.2%) negative for risk indications from maternal age, serum screening, ultrasound, or family history, and high-risk patients (15.8%) with at least one of the aforementioned indications. Cell free DNA (cfDNA) was extracted from maternal plasma. Library preparation incorporated 96 index barcodes to enable sequencing on a HiSeq 2000 or 2500. Risk scores were calculated using chromosomal representation, fetal fraction, and maternal age at the estimated date of delivery. A risk score greater than or equal to 1 in 100 was used to stratify samples as high risk for trisomy 21, trisomy 18, or trisomy 13. Results Sensitivity and specificity were calculated based on risk group stratification. Trisomy 21, trisomy 18, and trisomy 13 were detected with greater than 99% sensitivity and 99.9% specificity. Fetal sex classification accuracy was 99.3%. Conclusions We conclude that simplified MPS can be used to stratify the risk of pregnancies for trisomy 21, trisomy 18, and trisomy 13 and accurately determine fetal sex. © 2015 John Wiley & Sons, Ltd.

ObjectiveThis study introduces a novel method, referred to as SeqFF, for estimating the fetal DNA fraction in the plasma of pregnant women and to infer the underlying mechanism that allows for such statistical modeling.MethodsAutosomal regional read counts from whole-genome massively paralle

BACKGROUND The development of sequencing-based noninvasive prenatal testing (NIPT) has been largely focused on whole-chromosome aneuploidies (chromosomes 13, 18, 21, X, and Y). Collectively, they account for only 30% of all live births with a chromosome abnormality. Various structural chromosome changes, such as microdeletion/microduplication (MD) syndromes are more common but more challenging to detect. Recently, several publications have shown results on noninvasive detection of MDs by deep sequencing. These approaches demonstrated the proof of concept but are not economically feasible for large-scale clinical applications. METHODS We present a novel approach that uses low-coverage whole genome sequencing (approximately 0.2×) to detect MDs genome wide without requiring prior knowledge of the event's location. We developed a normalization method to reduce sequencing noise. We then applied a statistical method to search for consistently increased or decreased regions. A decision tree was used to differentiate whole-chromosome events from MDs. RESULTS We demonstrated via a simulation study that the sensitivity difference between our method and the theoretical limit was CONCLUSIONS The limit of detection for any given MD syndrome is constrained by 4 factors: fetal fraction, MD size, coverage, and biological and technical variability of the event region. Our algorithm takes these factors into account and achieved 94.4% sensitivity and 99.4% specificity.

Age-related macular degeneration (AMD) is a common cause of blindness in older individuals. To accelerate the understanding of AMD biology and help design new therapies, we executed a collaborative genome-wide association study, including >17,100 advanced AMD cases and >60,000 controls of European and Asian ancestry. We identified 19 loci associated at P

To take full advantage of high-throughput genetic and physical interaction mapping projects, the raw interactions must first be assembled into models of cell structure and function. PanGIA (for physical and genetic interaction alignment) is a plug-in for the bioinformatics platform Cytoscape, designed to integrate physical and genetic interactions into hierarchical module maps. PanGIA identifies ‘modules’ as sets of proteins whose physical and genetic interaction data matches that of known protein complexes. Higher-order functional cooperativity and redundancy is identified by enrichment for genetic interactions across modules. This protocol begins with importing interaction networks into Cytoscape, followed by filtering and basic network visualization. Next, PanGIA is used to infer a set of modules and their functional inter-relationships. This module map is visualized in a number of intuitive ways, and modules are tested for functional enrichment and overlap with known complexes. The full protocol can be completed between 10 and 30 min, depending on the size of the data set being analyzed.

The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

Pseudomonas aeruginosa is an antibiotic-refractory pathogen with a large genome and extensive genotypic diversity. Historically, P. aeruginosa has been a major model system for understanding the molecular mechanisms underlying type I clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated protein (CRISPR-Cas)-based bacterial immune system function. However, little information on the phylogenetic distribution and potential role of these CRISPR-Cas systems in molding the P. aeruginosa accessory genome and antibiotic resistance elements is known. Computational approaches were used to identify and characterize CRISPR-Cas systems within 672 genomes, and in the process, we identified a previously unreported and putatively mobile type I-C P. aeruginosa CRISPR-Cas system. Furthermore, genomes harboring noninhibited type I-F and I-E CRISPR-Cas systems were on average ~300 kb smaller than those without a CRISPR-Cas system. In silico analysis demonstrated that the accessory genome (n = 22,036 genes) harbored the majority of identified CRISPR-Cas targets. We also assembled a global spacer library that aided the identification of difficult-to-characterize mobile genetic elements within next-generation sequencing (NGS) data and allowed CRISPR typing of a majority of P. aeruginosa strains. In summary, our analysis demonstrated that CRISPR-Cas systems play an important role in shaping the accessory genomes of globally distributed P. aeruginosa isolates., IMPORTANCE P. aeruginosa is both an antibiotic-refractory pathogen and an important model system for type I CRISPR-Cas bacterial immune systems. By combining the genome sequences of 672 newly and previously sequenced genomes, we were able to provide a global view of the phylogenetic distribution, conservation, and potential targets of these systems. This analysis identified a new and putatively mobile P. aeruginosa CRISPR-Cas subtype, characterized the diverse distribution of known CRISPR-inhibiting genes, and provided a potential new use for CRISPR spacer libraries in accessory genome analysis. Our data demonstrated the importance of CRISPR-Cas systems in modulating the accessory genomes of globally distributed strains while also providing substantial data for subsequent genomic and experimental studies in multiple fields. Understanding why certain genotypes of P. aeruginosa are clinically prevalent and adept at horizontally acquiring virulence and antibiotic resistance elements is of major clinical and economic importance.

This study introduces a novel method, referred to as SeqFF, for estimating the fetal DNA fraction in the plasma of pregnant women and to infer the underlying mechanism that allows for such statistical modeling.Autosomal regional read counts from whole-genome massively parallel single-end sequencing of circulating cell-free DNA (ccfDNA) from the plasma of 25 312 pregnant women were used to train a multivariate model. The pretrained model was then applied to 505 pregnant samples to assess the performance of SeqFF against known methodologies for fetal DNA fraction calculations.Pearson's correlation between chromosome Y and SeqFF for pregnancies with male fetuses from two independent cohorts ranged from 0.932 to 0.938. Comparison between a single-nucleotide polymorphism-based approach and SeqFF yielded a Pearson's correlation of 0.921. Paired-end sequencing suggests that shorter ccfDNA, that is, less than 150 bp in length, is nonuniformly distributed across the genome. Regions exhibiting an increased proportion of short ccfDNA, which are more likely of fetal origin, tend to provide more information in the SeqFF calculations.SeqFF is a robust and direct method to determine fetal DNA fraction. Furthermore, the method is applicable to both male and female pregnancies and can greatly improve the accuracy of noninvasive prenatal testing for fetal copy number variation.

Background The present study evaluated the accumulated changes in hair cortisol levels of patients with posttraumatic stress disorder (PTSD) attributed to the 2008 Wenchuan earthquake in China. Methods Sixty-four female adolescents from two townships who experienced the earthquake were recruited 7 months after the disaster, including 32 subjects with PTSD (PTSD group) and 32 subjects without PTSD (non-PTSD group). Twenty matched adolescents were recruited from an area that was not affected significantly by the earthquake as the control group. Hair cortisol concentrations were measured by the electrochemiluminescence immunoassay in each 3-cm segment of hair sample from the scalp. Results There was no significant difference at the baseline hair cortisol level in the three groups before the traumatic event ( p > .6). Hair cortisol levels changed over time and differed among groups ( p = .0042). The hair cortisol levels among the PTSD and non-PTSD subjects were elevated, suggesting increasing levels in response to stress. However, these two groups differed in their response. The non-PTSD subjects showed a significantly higher cortisol level than the PTSD group between month 2 and month 4 ( p = .0137) and also between month 5 and month 7 ( p = .0438) after the traumatic event. Conclusions This study revealed a blunted response curve to the disaster among PTSD subjects compared with subjects without PTSD. These findings suggest that hair cortisol level could be used to assess the integrated hypothalamic-pituitary-adrenal activity over a period of months after traumatic events and be used to serve as a biomarker in patients with PTSD.

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex., Author Summary One of the most important problems in biology and medicine is to identify the genetic mutations that affect human traits such as blood pressure, longevity, and onset of disease. Currently, large scientific teams are examining the genomes of thousands of people in an attempt to find mutations present only in individuals with certain traits. Until now, mutations have been largely examined in isolation, without regard to how they work together inside the cell. However, large pathway maps are now available which describe in detail the network of genes and proteins that underlies cell function. Here we show how to take advantage of these pathway maps to better identify relevant mutations and to show how these mutations work mechanistically. This basic approach of combining genetic information with known maps of the cell will have wide-ranging applications in understanding and treating disease.