An integrated 4249 marker FISH/RH map of the canine genome

BACKGROUND: The 156 breeds of dog recognized by the American Kennel Club offer a unique opportunity to map genes important in genetic variation. Each breed features a defining constellation of morphological and behavioral traits, often generated by deliberate crossing of closely related individuals, leading to a high rate of genetic disease in many breeds. Understanding the genetic basis of both phenotypic variation and disease susceptibility in the dog provides new ways in which to dissect the genetics of human health and biology. RESULTS: To facilitate both genetic mapping and cloning efforts, we have constructed an integrated canine genome map that is both dense and accurate. The resulting resource encompasses 4249 markers, and was constructed using the RHDF5000-2 whole genome radiation hybrid panel. The radiation hybrid (RH) map features a density of one marker every 900 Kb and contains 1760 bacterial artificial chromosome clones (BACs) localized to 1423 unique positions, 851 of which have also been mapped by fluorescence in situ hybridization (FISH). The two data sets show excellent concordance. Excluding the Y chromosome, the map features an RH/FISH mapped BAC every 3.5 Mb and an RH mapped BAC-end, on average, every 2 Mb. For 2233 markers, the orthologous human genes have been established, allowing the identification of 79 conserved segments (CS) between the dog and human genomes, dramatically extending the length of most previously described CS. CONCLUSIONS: These results provide a necessary resource for the canine genome mapping community to undertake positional cloning experiments and provide new insights into the comparative canine-human genome maps

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Animating the evolution of software

The use and development of open source software has increased significantly in the last decade. The high frequency of changes and releases across a distributed environment requires good project management tools in order to control the process adequately. However, even with these tools in place, the nature of the development and the fact that developers will often work on many other projects simultaneously, means that the developers are unlikely to have a clear picture of the current state of the project at any time. Furthermore, the poor documentation associated with many projects has a detrimental effect when encouraging new developers to contribute to the software. A typical version control repository contains a mine of information that is not always obvious and not easy to comprehend in its raw form. However, presenting this historical data in a suitable format by using software visualisation techniques allows the evolution of the software over a number of releases to be shown. This allows the changes that have been made to the software to be identified clearly, thus ensuring that the effect of those changes will also be emphasised. This then enables both managers and developers to gain a more detailed view of the current state of the project. The visualisation of evolving software introduces a number of new issues. This thesis investigates some of these issues in detail, and recommends a number of solutions in order to alleviate the problems that may otherwise arise. The solutions are then demonstrated in the definition of two new visualisations. These use historical data contained within version control repositories to show the evolution of the software at a number of levels of granularity. Additionally, animation is used as an integral part of both visualisations - not only to show the evolution by representing the progression of time, but also to highlight the changes that have occurred. Previously, the use of animation within software visualisation has been primarily restricted to small-scale, hand generated visualisations. However, this thesis shows the viability of using animation within software visualisation with automated visualisations on a large scale. In addition, evaluation of the visualisations has shown that they are suitable for showing the changes that have occurred in the software over a period of time, and subsequently how the software has evolved. These visualisations are therefore suitable for use by developers and managers involved with open source software. In addition, they also provide a basis for future research in evolutionary visualisations, software evolution and open source development

Visualization and analysis of software clones

Code clones are identical or similar fragments of code in a software system. Simple copy-paste programming practices of developers, reusing existing code fragments instead of implementing from the scratch, limitations of both programming languages and developers are the primary reasons behind code cloning. Despite the maintenance implications of clones, it is not possible to conclude that cloning is harmful because there are also benefits in using them (e.g. faster and independent development). As a result, researchers at least agree that clones need to be analyzed before aggressively refactoring them. Although a large number of state-of-the-art clone detectors are available today, handling raw clone data is challenging due to the textual nature and large volume. To address this issue, we propose a framework for large-scale clone analysis and develop a maintenance support environment based on the framework called VisCad. To manage the large volume of clone data, VisCad employs the Visual Information Seeking Mantra: overview first, zoom and filter, then provide details-on-demand. With VisCad users can analyze and identify distinctive code clones through a set of visualization techniques, metrics covering different clone relations and data filtering operations. The loosely coupled architecture of VisCad allows users to work with any clone detection tool that reports source-coordinates of the found clones. This yields the opportunity to work with the clone detectors of choice, which is important because each clone detector has its own strengths and weaknesses. In addition, we extend the support for clone evolution analysis, which is important to understand the cause and effect of changes at the clone level during the evolution of a software system. Such information can be used to make software maintenance decisions like when to refactor clones. We propose and implement a set of visualizations that can allow users to analyze the evolution of clones from a coarse grain to a fine grain level. Finally, we use VisCad to extract both spatial and temporal clone data to predict changes to clones in a future release/revision of the software, which can be used to rank clone classes as another means of handling a large volume of clone data. We believe that VisCad makes clone comprehension easier and it can be used as a test-bed to further explore code cloning, necessary in building a successful clone management system

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

A chromosome-level genome assembly enables the identification of the follicule stimulating hormone receptor as the master sex-determining gene in the flatfish Solea senegalensis

Sex determination (SD) shows huge variation among fish and a high evolutionary rate, as illustrated by the Pleuronectiformes (flatfishes). This order is characterized by its adaptation to demersal life, compact genomes and diversity of SD mechanisms. Here, we assembled the Solea senegalensis genome, a flatfish of great commercial value, into 82 contigs (614 Mb) combining long- and short-read sequencing, which were next scaffolded using a highly dense genetic map (28,838 markers, 21 linkage groups), representing 98.9% of the assembly. Further, we established the correspondence between the assembly and the 21 chromosomes by using BAC-FISH. Whole genome resequencing of six males and six females enabled the identification of 41 single nucleotide polymorphism variants in the follicle stimulating hormone receptor (fshr) consistent with an XX/XY SD system. The observed sex association was validated in a broader independent sample, providing a novel molecular sexing tool. The fshr gene displayed differential expression between male and female gonads from 86 days post-fertilization, when the gonad is still an undifferentiated primordium, concomitant with the activation of amh and cyp19a1a, testis and ovary marker genes, respectively, in males and females. The Y-linked fshr allele, which included 24 nonsynonymous variants and showed a highly divergent 3D protein structure, was overexpressed in males compared to the X-linked allele at all stages of gonadal differentiation. We hypothesize a mechanism hampering the action of the follicle stimulating hormone driving the undifferentiated gonad toward testisEuropean Union's Horizon 2020 research and innovation programme under grant agreement (AQUA-FAANG). Grant Number: 81792. Junta de Andalucía-FEDER Grant. Grant Number: P20-00938. Spanish Ministry of Economy and Competitiveness, FEDER Grants. Grant Numbers: RTI2018-096847-B-C21, RTI2018-096847-B-C22S