"gtrellis": an R/Bioconductor package for making genome-level Trellis graphics

BACKGROUND: Trellis graphics are a visualization method that splits data by one or more categorical variables and displays subsets of the data in a grid of panels. Trellis graphics are broadly used in genomic data analysis to compare statistics over different categories in parallel and reveal multivariate relationships. However, current software packages to produce Trellis graphics have not been designed with genomic data in mind and lack some functionality that is required for effective visualization of genomic data. RESULTS: Here we introduce the gtrellis package which provides an efficient and extensible way to visualize genomic data in a Trellis layout. gtrellis provides highly flexible Trellis layouts which allow efficient arrangement of genomic categories on the plot. It supports multiple-track visualization, which makes it straightforward to visualize several properties of genomic data in parallel to explain complex relationships. In addition, gtrellis provides an extensible framework that allows adding user-defined graphics. CONCLUSIONS: The gtrellis package provides an easy and effective way to visualize genomic data and reveal high dimensional relationships on a genome-wide scale. gtrellis can be flexibly extended and thus can also serve as a base package for highly specific purposes. gtrellis makes it easy to produce novel visualizations, which can lead to the discovery of previously unrecognized patterns in genomic data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1051-4) contains supplementary material, which is available to authorized users

EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations

Background: High-throughput sequencing data are dramatically increasing in volume. Thus, there is urgent need for efficient tools to perform fast and integrative analysis of multiple data types. Enriched heatmap is a specific form of heatmap that visualizes how genomic signals are enriched over specific target regions. It is commonly used and efficient at revealing enrichment patterns especially for high dimensional genomic and epigenomic datasets. Results: We present a new R package named EnrichedHeatmap that efficiently visualizes genomic signal enrichment. It provides advanced solutions for normalizing genomic signals within target regions as well as offering highly customizable visualizations. The major advantage of EnrichedHeatmap is the ability to conveniently generate parallel heatmaps as well as complex annotations, which makes it easy to integrate and visualize comprehensive overviews of the patterns and associations within and between complex datasets. Conclusions: EnrichedHeatmap facilitates comprehensive understanding of high dimensional genomic and epigenomic data. The power of EnrichedHeatmap is demonstrated by visualization of the complex associations between DNA methylation, gene expression and various histone modifications

Auswirkungen einer Ozonisierungsanlage auf den Bakteriengehalt des Wassers eines Schauaquariums

Die vorliegende Arbeit sollte klären, welchen Einfluß die Seewasser-Reinigungsanlage im Aquarium des Instituts für Meereskunde in Kiel auf den Bakteriengehalt des Aquariumwassers hat. Es wurde festgestellt, daß wesentliche Unterschiede in der Belastung des Wassers im Vergleich zum Fördewasser bestehen, insbesondere weist das Wasser des Aquariums eine sehr hohe Konzentration an Phosphat und Nitrat auf. Es wurden die auf Grund ihres Salzanspruches auf verschiedenen Nährböden wachsenden saprophytischen Bakterien gezählt. Es zeigte sich, daB die Saprophytenzahlen starken Schwankungen unterlagen; die Gründe hierfür könnten weitgehend analysiert werden. Die Saprophytenzahlen nehmen beim Durchlauf des Wassers durch die Reinigungsanlage ab, wenn Ozon eingeleitet wird. Es handelt sich aber in diesem Falle nicht um eine unmittelbare Wirkung des Ozons durch dessen oxydierende Wirkung, sondern um eine mittelbare, indem die Schaumbildung gefördert wird. Mit dem Schaum werden neben anderen partikulären Verunreinigungen auch Bakterien dem Wasserkreislauf entzogen. Zur Keimtötung reichen die verfügbaren Ozonmengen nicht aus. Die Anlage erfüllt zwar ihre Aufgabe, indem sie das Wasser von löslichen Proteinen und partikulären Verunreinigungen befreit und das giftige Nitrit zu unschädlichem Nitrat oxydiert, jedoch ist die Nutzung der Anlage nicht optimal. Zusätzliche Versuche mit Ozon zeigten, daß zur Keimtötung im belasteten Aquarienwasser mit den gegebenen Ozonmengen lange Einwirkungszeiten erforderlich sind. Es konnte auch gezeigt werden, daß der Wirkungsgrad des Ozons von der Belastung des Wassers mit oxydierbaren organischen und anorganischen Stoffen abhängig ist

Explainable artificial intelligence for omics data: a systematic mapping study

Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies’ AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners

A scoping review of distributed ledger technology in genomics: thematic analysis and directions for future research

OBJECTIVE: Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. MATERIALS AND METHODS: We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. RESULTS: Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. DISCUSSION: Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people’s attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. CONCLUSION: While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners

Explainable artificial intelligence for omics data: a systematic mapping study

Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies’ AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners

A scoping review of distributed ledger technology in genomics: thematic analysis and directions for future research

Objective Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. Materials and Methods We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. Results Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. Discussion Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people’s attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. Conclusion While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners