FuGEFlow: data model and markup language for flow cytometry

<p>Abstract</p> <p>Background</p> <p>Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.</p> <p>Methods</p> <p>We used the MagicDraw modelling tool to design a UML model (Flow-OM) according to the FuGE extension guidelines and the AndroMDA toolkit to transform the model to a markup language (Flow-ML). We mapped each MIFlowCyt term to either an existing FuGE class or to a new FuGEFlow class. The development environment was validated by comparing the official FuGE XSD to the schema we generated from the FuGE object model using our configuration. After the Flow-OM model was completed, the final version of the Flow-ML was generated and validated against an example MIFlowCyt compliant experiment description.</p> <p>Results</p> <p>The extension of FuGE for flow cytometry has resulted in a generic FuGE-compliant data model (FuGEFlow), which accommodates and links together all information required by MIFlowCyt. The FuGEFlow model can be used to build software and databases using FuGE software toolkits to facilitate automated exchange and manipulation of potentially large flow cytometry experimental data sets. Additional project documentation, including reusable design patterns and a guide for setting up a development environment, was contributed back to the FuGE project.</p> <p>Conclusion</p> <p>We have shown that an extension of FuGE can be used to transform minimum information requirements in natural language to markup language in XML. Extending FuGE required significant effort, but in our experiences the benefits outweighed the costs. The FuGEFlow is expected to play a central role in describing flow cytometry experiments and ultimately facilitating data exchange including public flow cytometry repositories currently under development.</p

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022

Enhancing systems biology models through semantic data integration

Studying and modelling biology at a systems level requires a large amount of data of different experimental types. Historically, each of these types is stored in its own distinct format, with its own internal structure for holding the data produced by those experiments. While the use of community data standards can reduce the need for specialised, independent formats by providing a common syntax, standards uptake is not universal and a single standard cannot yet describe all biological data. In the work described in this thesis, a variety of integrative methods have been developed to reuse and restructure already extant systems biology data. SyMBA is a simple Web interface which stores experimental metadata in a published, common format. The creation of accurate quantitative SBML models is a time-intensive manual process. Modellers need to understand both the systems they are modelling and the intricacies of the SBML format. However, the amount of relevant data for even a relatively small and well-scoped model can be overwhelming. Saint is a Web application which accesses a number of external Web services and which provides suggested annotation for SBML and CellML models. MFO was developed to formalise all of the knowledge within the multiple SBML specification documents in a manner which is both human and computationally accessible. Rule-based mediation, a form of semantic data integration, is a useful way of reusing and re-purposing heterogeneous datasets which cannot, or are not, structured according to a common standard. This method of ontology-based integration is generic and can be used in any context, but has been implemented specifically to integrate systems biology data and to enrich systems biology models through the creation of new biological annotations. The work described in this thesis is one step towards the formalisation of biological knowledge useful to systems biology. Experimental metadata has been transformed into common structures, a Web application has been created for the retrieval of data appropriate to the annotation of systems biology models and multiple data models have been formalised and made accessible to semantic integration techniques.EThOS - Electronic Theses Online ServiceBBSRCEPSRCGBUnited Kingdo

Translational software infrastructure for medical genetics

Diep in de kern van onze cellen zetelt het desoxyribonucleïnezuur (DNA) molecuul die bekend staat als het genoom.DNA codeert de informatie die het leven laat groeien, overleven, diversifiëren en evolueren.Helaas kunnen dezelfde mechanismes die ons laten aanpassen aan een veranderende omgeving ook genetische aandoeningen veroorzaken.Hoewel we in staat zijn een aantal van deze aandoeningen op te sporen door moderne technologische vorderingen, moet er nog veel ontdekt en begrepen worden.Dit proefschrift draagt software infrastructuur aan om de moleculaire oorzaak van genetische aandoeningen te onderzoeken, laat zien hoe nieuwe bevindingen vertaald worden van fundamenteel onderzoek naar nieuwe software voor genoom diagnostiek, en introduceert een raamwerk voor genetische analyses die de automatisering en validatie van nieuwe software ondersteunt voor toepassing in de patientenzorg.Eerst ontwikkelen we datamodellen en software die helpt te bepalen welke gebieden op het genoom verantwoordelijk zijn voor ziektes en andere fysieke kenmerken.Vervolgens trekken we deze principes door naar modelorganismen.Door moleculaire gelijkenissen te gebruiken, ontdekken we nieuwe manieren om nematodes in te zetten voor onderzoek naar menselijke ziektes.Daarnaast kunnen we onze kennis van het genoom en de evolutie gebruiken om te voorspellen hoe pathogeen nieuwe mutaties zijn.Het resultaat is een publieke website waar DNA snel en accuraat gescand kan worden op mogelijk ziekteverwekkende mutaties.Tenslotte presenteren we een compleet systeem voor geautomatiseerde DNA analyse, inclusief een protocol specifiek voor genoom diagnostiek om overzichtelijke patient rapportages te produceren voor medisch experts waarmee een diagnose sneller en makkelijker gesteld kan worden.Deep inside the core of our cells resides the deoxyribonucleic acid (DNA) molecule known as the genome.DNA encodes the information that allows life to grow, survive, diversify and evolve.Unfortunately, the same mechanisms that let us adapt to a changing environment can also cause genetic disorders.While we are able to diagnose a number of these disorders using modern technological advancements, much remains to be discovered and understood.This thesis presents software infrastructure for investigating the molecular etiology of genetic disease using data from model organisms, demonstrates how to translate findings from fundamental research into new software tools for genome diagnostics, and introduces a downstream genome analysis framework that assists the automation and validation of the latest tools for applied patient care.We first develop data models and software to help determine which region of the genome is responsible for diseases and other physical traits.We then extend these principles towards model organisms.By using molecular similarities, we discover new ways to use nematodes for research into human diseases.Additionally, we can use our knowledge of the genome and evolution to predict how pathogenic new mutations are.The result is a public website where DNA can be scanned quickly and accurately for probable pathogenic mutations.Finally, we present a complete system for automated DNA analysis, including a protocol specific for genome diagnostics to produce clear patient reports for medical experts with which a diagnosis is made faster and easier

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Doing Experimental Media Archaeology

The volume aims at taking the materiality of past media devices seriously and explores the heuristic possibilities of an experimental study of these devices. It offers a sophisticated reflection on the epistemological and heuristic potential of hands-on media historical research

Towards an automatic processing of proteomics data: from mass spectrometry to biological knowledge

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Inforrmática. Fecha de lectura: octubre de 201

Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.</p