High-throughput computational methods and software for quantitative trait locus (QTL) mapping

De afgelopen jaren zijn vele nieuwe technologieen zoals Tiling arrays en High throughput DNA sequencing een belangrijke rol gaan spelen binnen het onderzoeksveld van de systeem genetica. Voor onderzoekers is het extreem belangrijk om te begrijpen dat deze methodes hun manier van werken zullen gaan beinvloeden. Deit proefschrift beschrijft mogelijke oplossingen voor deze 'Big Data' lawine die systemen genetica heeft getroffen.Dit proefschrift beschrijft de werkzaamheden uitgevoerd aan het Groningen Bioinformatics Centre om slimmere en geoptimaliseerde algoritmen zoals Pheno2Geno en MQM te ontwikkelen en een systeem om 'collaborative' research mogelijk te maken genaamd xQTL werkbank om door middel van high-throughput systemen genetica data te analyseren.In recent years many new technologies such as tiling arrays and high-throughput sequencinghave come to play an important role in systems genetics research. For researchers it is ofthe utmost importance to understand how this affects their research. This work describespossible solutions to this ‘Big Data’ avalanche which has hit systems genetics.This thesis describes the work carried out during the author’s 4 year PHD project at theGroningen Bioinformatics Centre to develop smarter and more optimized algorithms suchas Pheno2Geno and MQM, and to use a collaborative approach such as xQTL workbench tostore and analyse high-throughput systems genetics data

Emerging semantics to link phenotype and environment

abstract: Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.The final version of this article, as published in PeerJ, can be viewed online at: https://peerj.com/articles/1470

Conceptual Design and Analysis of Service Oriented Architecture (SOA) for Command and Control of Space Assets

The mission-unique model that has dominated the DoD satellite Command and Control community is costly and inefficient. It requires repeatedly “reinventing” established common C2 components for each program, unnecessarily inflating budgets and delivery schedules. The effective utilization of standards is scarce, and proprietary, non-open solutions are commonplace. IT professionals have trumpeted Service Oriented Architectures (SOAs) as the solution to large enterprise situations where multiple, functionally redundant but non-compatible information systems create large recurring development, test, maintenance, and tech refresh costs. This thesis describes the current state of Service Oriented Architectures as related to satellite operations and presents a functional analysis used to classify a set of generic C2 services. By assessing the candidate services’ suitability through a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, several C2 functionalities are shown to be more ready than others to be presented as services in the short term. Lastly, key enablers are identified, pinpointing the necessary steps for a full and complete transition from the paradigm of costly mission-unique implementations to the common, interoperable, and reusable space C2 SOA called for by DoD senior leaders

Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care

Medical Informatics (MI) and Bioinformatics (BI) are two interdisciplinary areas located at the intersection between computer science and medicine and biology, respectively. Historically, they have been separated and only occasionally have researchers of both disciplines collaborated. The completion of the Human Genome Project has brought about in this post genomic era the need for a synergy of these two disciplines to further advance in the study of diseases by correlating essential genotypic information with expressed phenotypic information. Biomedical Informatics (BMI) is the emerging technology that aims to put these two worlds together in the new rising genomic medicine. In this regard, institutions such as the European Commission have recently launched several initiatives to support a new combined research agenda, based on the potential for synergism of both disciplines. In this paper we review the results the BIOINFOMED study one of these projects funded by the E

Killing Two Birds with One Stone: The Concurrent Development of the Novel Alignment Free Tree Building Method, Scrawkov-Phy, and the Extensible Phyloinformatics Utility, EMU-Phy.

Many components of phylogenetic inference belong to the most computationally challenging and complex domain of problems. To further escalate the challenge, the genomics revolution has exponentially increased the amount of data available for analysis. This, combined with the foundational nature of phylogenetic analysis, has prompted the development of novel methods for managing and analyzing phylogenomic data, as well as improving or intelligently utilizing current ones. In this study, a novel alignment tree building algorithm using Quasi-Hidden Markov Models (QHMMs), Scrawkov-Phy, is introduced. Additionally, exploratory work in the design and implementation of an extensible phyloinformatics tool, EMU-Phy, is described. Lastly, features of the best-practice tools are inspected and provisionally incorporated into Scrawkov-Phy to evaluate the algorithm’s suitability for said features. This study shows that Scrawkov-Phy, as utilized through EMU-Phy, captures phylogenetic signal and reconstructs reasonable phylogenies without the need for multiple-sequence alignment or high-order statistical models. There are numerous additions to both Scrawkov-Phy and EMU-Phy which would improve their efficacy and the results of the provisional study shows that such additions are compatible