AN INTEGRATED DESIGN APPROACH OF HIGH-PERFORMANCE GREEN BUILDINGS

The Large carbon footprint of conventional buildings coupled with their high energy consumption in recent years, there is a necessity for an emphasis on the design process of energy-efficient high-performance green buildings. However, there exists limited research on the integration of green technologies into a high-performance green building with a special focus on energy, daylighting and green material. To solve this issue, an integrated design approach is presented from the perspective of making it practical and easier for architects and designers to design a high-performance green building. This paper introduces the benefit of the application of Environmental Impact Assessment and the integration of various green technologies along with the LEED rating system in the design process. Relevant case studies of various green buildings are exemplified and enumerated throughout the paper for the purpose of investigating the practicality of the approach. Lastly, the payback period for the initial cost premium for the construction of a high-performance green building is also given due consideration in the design process

Hidden roles: perspectives, paths and lived experiences

This presentation was given as a workshop at the Festival of Hidden Ref on 21 September 2023. More details about the Hidden REF can be found here: https://hidden-ref.org/ Material for this workshop was adapted from previous Turing Way workshops for Open Science conference 2023 and Collaborations workshop 2023. Please also see our chapter on Research Infrastructures Roles in the Turing Way Guide to Collaboration

Ten Simple Rules for Organizing an Unconference

This is an open access article distributed under the terms of the Creative Commons Attribution License.Peer Reviewe

An intrinsically disordered proteins community for ELIXIR.

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled "An intrinsically disordered protein user community proposal for ELIXIR" held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

The Turing Way Workshop: Effective Collaboration in a Distributed (Research) World

<p>This is a 2.5 hours workshop on research collaboration.</p> <p>The linked worksheet can be found here: <a href="https://docs.google.com/presentation/d/1M9m3oGANoX0ScXjyiixCnpPw6CGfwZ3My3TMtcpEpho/edit#slide=id.gee88c72ed3_0_1">TEMPLATE</a></p&gt

Bioinformatische Identifikation und Charakterisierung von RNA-bindenden Proteinen in Bakterien

RNA-binding proteins (RBPs) have been extensively studied in eukaryotes, where they post-transcriptionally regulate many cellular events including RNA transport, translation, and stability. Experimental techniques, such as cross-linking and co-purification followed by either mass spectrometry or RNA sequencing has enabled the identification and characterization of RBPs, their conserved RNA-binding domains (RBDs), and the regulatory roles of these proteins on a genome-wide scale. These developments in quantitative, high-resolution, and high-throughput screening techniques have greatly expanded our understanding of RBPs in human and yeast cells. In contrast, our knowledge of number and potential diversity of RBPs in bacteria is comparatively poor, in part due to the technical challenges associated with existing global screening approaches developed in eukaryotes. Genome- and proteome-wide screening approaches performed in silico may circumvent these technical issues to obtain a broad picture of the RNA interactome of bacteria and identify strong RBP candidates for more detailed experimental study. Here, I report APRICOT (“Analyzing Protein RNA Interaction by Combined Output Technique”), a computational pipeline for the sequence-based identification and characterization of candidate RNA-binding proteins encoded in the genomes of all domains of life using RBDs known from experimental studies. The pipeline identifies functional motifs in protein sequences of an input proteome using position-specific scoring matrices and hidden Markov models of all conserved domains available in the databases and then statistically score them based on a series of sequence-based features. Subsequently, APRICOT identifies putative RBPs and characterizes them according to functionally relevant structural properties. APRICOT performed better than other existing tools for the sequence-based prediction on the known RBP data sets. The applications and adaptability of the software was demonstrated on several large bacterial RBP data sets including the complete proteome of Salmonella Typhimurium strain SL1344. APRICOT reported 1068 Salmonella proteins as RBP candidates, which were subsequently categorized using the RBDs that have been reported in both eukaryotic and bacterial proteins. A set of 131 strong RBP candidates was selected for experimental confirmation and characterization of RNA-binding activity using RNA co-immunoprecipitation followed by high-throughput sequencing (RIP-Seq) experiments. Based on the relative abundance of transcripts across the RIP-Seq libraries, a catalogue of enriched genes was established for each candidate, which shows the RNA-binding potential of 90% of these proteins. Furthermore, the direct targets of few of these putative RBPs were validated by means of cross-linking and co-immunoprecipitation (CLIP) experiments. This thesis presents the computational pipeline APRICOT for the global screening of protein primary sequences for potential RBPs in bacteria using RBD information from all kingdoms of life. Furthermore, it provides the first bio-computational resource of putative RBPs in Salmonella, which could now be further studied for their biological and regulatory roles. The command line tool and its documentation are available at https://malvikasharan.github.io/APRICOT/.RNA-bindende Proteine (RBPs) wurden umfangreich in Eukaryoten erforscht, in denen sie viele Prozesse wie RNA-Transport, -Translation und -Stabilität post-transkriptionell regulieren. Experimentelle Methoden wie Cross-linking and Koimmunpräzipitation mit nachfolgedener Massenspektromentrie / RNA-Sequenzierung ermöglichten eine weitreichende Charakterisierung von RBPs, RNA-bindenden Domänen (RBDs) und deren regulatorischen Rollen in eukaryotischen Spezies wie Mensch und Hefe. Weitere Entwicklungen im Bereich der hochdurchsatzbasierten Screeningverfahren konnten das Verständnis von RBPs in Eukaryoten enorm erweitern. Im Gegensatz dazu ist das Wissen über die Anzahl und die potenzielle Vielfalt von RBPs in Bakterien dürftig. In der vorliegenden Arbeit präsentiere ich APRICOT, eine bioinformatische Pipeline zur sequenzbasierten Identifikation und Charakterisierung von Proteinen aller Domänen des Lebens, die auf RBD-Informationen aus experimentellen Studien aufbaut. Die Pipeline nutzt Position Specific Scoring Matrices und Hidden-MarkovModelle konservierter Domänen, um funktionelle Motive in Proteinsequenzen zu identifizieren und diese anhand von sequenzbasierter Eigenschaften statistisch zu bewerten. Anschließend identifiziert APRICOT mögliche RBPs und charakterisiert auf Basis ihrer biologischeren Eigenschaften. In Vergleichen mit ähnlichen Werkzeugen übertraf APRICOT andere Programme zur sequenzbasierten Vorhersage von RBPs. Die Anwendungsöglichkeiten und die Flexibilität der Software wird am Beispiel einiger großer RBP-Kollektionen, die auch das komplette Proteom von Salmonella Typhimurium SL1344 beinhalten, dargelegt. APRICOT identifiziert 1068 Proteine von Salmonella als RBP-Kandidaten, die anschließend unter Nutzung der bereits bekannten bakteriellen und eukaryotischen RBDs klassifiziert wurden. 131 der RBP-Kandidaten wurden zur Charakterisierung durch RNA co-immunoprecipitation followed by high-throughput sequencing (RIP-seq) ausgewählt. Basierend auf der relativen Menge an Transkripten in den RIP-seq-Bibliotheken wurde ein Katalog von angereicherten Genen erstellt, der auf eine potentielle RNA-bindende Funktion in 90% dieser Proteine hindeutet. Weiterhin wurden die Bindungstellen einiger dieser möglichen RBPs mit Cross-linking and Co-immunoprecipitation (CLIP) bestimmt. Diese Doktorarbeit beschreibt die bioinformatische Pipeline APRICOT, die ein globales Screening von RBPs in Bakterien anhand von Informationen bekannter RBDs ermöglicht. Zudem enthält sie eine Zusammenstellung aller potentieller RPS in Salmonella, die nun auf ihre biologsche Funktion hin untersucht werden können. Das Kommondozeilen-Programm und seine Dokumentation sind auf https://malvikasharan.github.io/APRICOT/ verfügbar

The Turing Way: Engaging Community in Sharing Open and Reproducible Research Practices

<p><em>The Turing Way</em> is a handbook to reproducible, ethical and collaborative data science and research. We involve and support a diverse community of contributors to make data science accessible, comprehensible and effective for everyone. Our goal is to provide all the information that researchers and data scientists in academia, industry and the public sector need at the start of their projects to ensure that they are easy to reproduce at the end. In this talk, I will introduce the project and discuss the importance of meaningful engagement with the open science community for creating culture change in research.</p> <p>Please see the project on GitHub: https://github.com/alan-turing-institute/the-turing-way/</p&gt

The Turing Way: A Digital Commons for Open Science and Reproducibility

This presentation presents The Turing Way as a digital commons that has become a critical infrastructure in research and open science community. Specifically, we demonstrate how we can curate and present selected chapters to inform community about software citation