A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides scalable resources for storing data of different types and parallelizing data analysis computations. Moreover, these resources can be reused for different multi-omics analysis scenarios. Traditionally, developers are required to manage a cloud platform’s underlying infrastructure, configuration, maintenance and capacity planning. The serverless computing paradigm simplifies these operations by automatically allocating and maintaining both servers and virtual machines, as required for analysis tasks. This paradigm offers highly parallel execution and high scalability without manual management of the underlying infrastructure, freeing developers to focus on operational logic. This paper reviews serverless solutions in bioinformatics and evaluates their usage in omics data analysis and integration. We start by reviewing the application of the cloud computing model to a multi-omics data analysis and exposing some shortcomings of the early approaches. We then introduce the serverless computing paradigm and show its applicability for performing an integrative analysis of multiple omics data sources in the context of the COVID-19 pandemic.

In times of real threats to the continuity of the human civilization resulting from environmental degradation, depletion of natural resources, overpopulation, and other adverse factors, the issue of sustainable development is the subject of interest of many scientific disciplines. As a leading objective of this paper, the authors take up the topic of sustainable development seen through the lenses of the library and information science, which is considered with special attention paid to its economic, social, environmental, and cultural dimensions. In addition to reviewing the most important literature, the authors also explore the subject matter from a quantitative perspective. As a result of the research, the authors identify the key areas that affect libraries as cultural and scientific institutions, in which work related to the sustainability concept is actively carried out. Quantitative research allowed to determine the proportions of efforts made by scientists within the previously selected areas, and to outline trends observed within those areas—that is, to identify which areas have recently been gaining importance, and which may have ceased to be exploited. The authors hope that the research results not only shed light on the landscape of world science in the subject matter, but above all, that they support contemporary researches of these fields by identifying potentially the most important works influencing the shape of particular research areas, and the identification of current trends, which are present within the mentioned areas as well. Further research directions, which are potentially worth undertaking, are also emphasized.

Integrative analysis of multi-omics data is usually computationally demanding. It frequently requires building complex, multi-step analysis pipelines, applying dedicated techniques for data processing and combining several data sources. These efforts lead to a better understanding of life processes, current health state or the effects of therapeutic activities. However, many omics data analysis solutions focus only on a selected problem, disease, types of data or organisms. Moreover, they are implemented for general-purpose scientific computational platforms that most often do not easily scale the calculations natively. These features are not conducive to advances in understanding genotype–phenotypic relationships. Fortunately, with new technological paradigms, including Cloud computing, virtualization and containerization, these functionalities could be orchestrated for easy scaling and building independent analysis pipelines for omics data. Therefore, solutions can be re-used for purposes that they were not primarily designed. This paper shows perspectives of using Cloud computing advances and containerization approach for such a purpose. We first review how the Cloud computing model is utilized in multi-omics data analysis and show weak points of the adopted solutions. Then, we introduce containerization concepts, which allow both scaling and linking of functional services designed for various purposes. Finally, on the Bioconductor software package example, we disclose a verified concept model of a universal solution that exhibits the potentials for performing integrative analysis of multiple omics data sources.

The article presents the concept of application DLT (distributed ledger technologies) for building the electronic clinical documentation tracking system. After a short introduction to block chain issues, and discussion about the attempts of its application on various fields of everyday human life, including healthcare, basic requirements for tracking of clinical documentation system are presented, followed by the proposition of its architecture leveraging the distributed ledger technologies. The paper is concluded with a discussion about the possibilities of running such a system, regarding constraints coming from local legal regulations and general data protection regulation (GDPR), but also economic and social conditions, including ecological ones, which are part of the sustainable development trend.

NoSQL databases are more and more popular, because they fill the gap where traditional relational model of data does not fit. Social network analysis can be an example of an area, where a particular kind of NoSQL database -the graph one seems to be a natural choice. However, relational databases are developed for many years, they include advanced algorithms for indexing, query optimization etc. This raises the question, whether at the field of performance graph database and relation one are competitive. This article tries to give an answer to this question, by comparing performance of two leading databases from both sides: Neo4j and Oracle 11g.