Life Science Data Research Articles

Using text-mining to measure the scientific impact and legacy of ELIXIR, a distributed research infrastructure for life science data

Background ELIXIR is a pan-European public-funded research infrastructure dedicated to life science data. As such, it must demonstrate public value to its funders and stakeholders. We present methods to inventory research publications linked to ELIXIR that have received funding and support, as well as related citation metrics, used as performance metrics for these audiences. Methods To overcome challenges inherent in ELIXIR’s distributed structure, and the fact that those publishing ELIXIR-supported work are typically working part-time on ELIXIR matters, a semi-automated approach, consisting of text-mining followed by manual curation, is presented. A country-level case study (ELIXIR Italy) refines and expands the methods, notably by introducing more granularity in the curation process (e.g. considering all national-level grants, examining affiliations to report publication per institute) and by additionally looking at the scientific impact of the resources developed and operated by the Italian Node of ELIXIR. Results Overall, the methods described in this article have shown to: (1) be repeatable with acceptable levels of accuracy and consistency (notably across curators); (2) require reasonable effort in terms of curation of monthly ‘harvests’ of publications (obtained by text-mining); and (3) to be well-adapted to ELIXIR’s distributed nature. Conclusions Concrete examples are provided of downstream uses of the inventoried publications and their citations, both for ELIXIR as a whole and for the Italian case study. Limitations of the methods are discussed, particularly the challenges associated with using an ‘Open literature’ database (Europe PMC) for the text-mining, and the constraints related to curation capacity. The methods, along with the valuable lessons learned during their development, are sufficiently generic and pragmatic enough to be readily adapted by other similar research infrastructures.

CAMDA 2023: Finding patterns in urban microbiomes.

The Critical Assessment of Massive Data Analysis (CAMDA) addresses the complexities of harnessing Big Data in life sciences by hosting annual competitions that inspire research groups to develop innovative solutions. In 2023, the Forensic Challenge focused on identifying the city of origin for 365 metagenomic samples collected from public transportation systems and identifying associations between bacterial distribution and other covariates. For microbiome classification, we incorporated both taxonomic and functional annotations as features. To identify the most informative Operational Taxonomic Units, we selected features by fitting negative binomial models. We then implemented supervised models conducting 5-fold cross-validation (CV) with a 4:1 training-to-validation ratio. After variable selection, which reduced the dataset to fewer than 300 OTUs, the Support Vector Classifier achieved the highest F1 score (0.96). When using functional features from MIFASER, the Neural Network model outperformed other models. When considering climatic and demographic variables of the cities, Dirichlet regression over Escherichia, Enterobacter, and Klebsiella bacteria abundances suggests that population increase is indeed associated with a rise in the mean of Escherichia while decreasing temperature is linked to higher proportions of Klebsiella. This study validates microbiome classification using taxonomic features and, to a lesser extent, functional features. It shows that demographic and climatic factors influence urban microbial distribution. A Docker container and a Conda environment are available at the repository: GitHub facilitating broader adoption and validation of these methods by the scientific community.

NASA open science data repository: open science for life in space.

Space biology and health data are critical for the success of deep space missions and sustainable human presence off-world. At the core of effectively managing biomedical risks is the commitment to open science principles, which ensure that data are findable, accessible, interoperable, reusable, reproducible and maximally open. The 2021 integration of the Ames Life Sciences Data Archive with GeneLab to establish the NASA Open Science Data Repository significantly enhanced access to a wide range of life sciences, biomedical-clinical and mission telemetry data alongside existing 'omics data from GeneLab. This paper describes the new database, its architecture and new data streams supporting diverse data types and enhancing data submission, retrieval and analysis. Features include the biological data management environment for improved data submission, a new user interface, controlled data access, an enhanced API and comprehensive public visualization tools for environmental telemetry, radiation dosimetry data and 'omics analyses. By fostering global collaboration through its analysis working groups and training programs, the open science data repository promotes widespread engagement in space biology, ensuring transparency and inclusivity in research. It supports the global scientific community in advancing our understanding of spaceflight's impact on biological systems, ensuring humans will thrive in future deep space missions.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

Celebrating 30 years of access to NASA Space Life Sciences data.

NASA's space life sciences research programs established a decades-long legacy of enhancing our ability to safely explore the cosmos. From Skylab and the Space Shuttle Program to the NASA Balloon Program and the International Space Station National Lab, these programs generated priceless data that continue to paint a vibrant picture of life in space. These data are available to the scientific community in various data repositories, including the NASA Ames Life Sciences Data Archive (ALSDA) and NASA GeneLab. Here we recognize the 30-year anniversary of data access through ALSDA and the 10-year anniversary of GeneLab.

Wikidata subsetting: Approaches, tools, and evaluation

Wikidata is a massive Knowledge Graph (KG), including more than 100 million data items and nearly 1.5 billion statements, covering a wide range of topics such as geography, history, scholarly articles, and life science data. The large volume of Wikidata is difficult to handle for research purposes; many researchers cannot afford the costs of hosting 100 GB of data. While Wikidata provides a public SPARQL endpoint, it can only be used for short-running queries. Often, researchers only require a limited range of data from Wikidata focusing on a particular topic for their use case. Subsetting is the process of defining and extracting the required data range from the KG; this process has received increasing attention in recent years. Specific tools and several approaches have been developed for subsetting, which have not been evaluated yet. In this paper, we survey the available subsetting approaches, introducing their general strengths and weaknesses, and evaluate four practical tools specific for Wikidata subsetting – WDSub, KGTK, WDumper, and WDF – in terms of execution performance, extraction accuracy, and flexibility in defining the subsets. Results show that all four tools have a minimum of 99.96% accuracy in extracting defined items and 99.25% in extracting statements. The fastest tool in extraction is WDF, while the most flexible tool is WDSub. During the experiments, multiple subset use cases have been defined and the extracted subsets have been analyzed, obtaining valuable information about the variety and quality of Wikidata, which would otherwise not be possible through the public Wikidata SPARQL endpoint.

Comprehensive genomic profiling of penile squamous cell carcinoma and the impact of human papillomavirus status on immune-checkpoint inhibitor-related biomarkers.

Advanced penile squamous cell carcinoma (pSCC) is a rare and aggressive malignancy with limited success of immune-checkpoint inhibitors (ICIs). Approximately half of pSCC cases are associated with human papillomavirus (HPV) infection. Evaluation was done retrospectively of the landscape of somatic alterations and ICI-related biomarkers in pSCC by using the Caris Life Sciences data set with the aim to establish signatures for HPV-dependent oncogenesis. The pSCC tumors were analyzed by using next-generation sequencing (NGS) of DNA and RNA. Programmed death ligand 1 (PD-L1) expression was evaluated by immunohistochemistry (IHC). Microsatellite instability (MSI) was tested by fragment analysis, IHC (SP142; ≥1%), and NGS. Tumor mutational burden (TMB)-high was defined as ≥10 mutations/Mb. HPV16/18 status was determined by using whole-exome sequencing (WES) when available. Significance was adjusted for multiple comparisons (q value < .05). NGS of the overall cohort (N=108) revealed TP53 (46%), CDKN2A (26%), and PIK3CA (25%) to be the most common mutations. Overall, 51% of tumors were PD-L1+, 10.7% had high TMB, and 1.1% had mismatch repair-deficient (dMMR)/MSI-high status. Twenty-nine patients had their HPV status made available by WES (HPV16/18+, n=13; HPV16/18-, n=16). KMT2C mutations (33% vs. 0%) and FGF3 amplifications (30.8% vs. 0%) were specific to HPV16/18+ tumors, whereas CDKN2A mutations (0% vs. 37.5%) were exclusive to HPV16/18- tumors. TMB-high was exclusively found in the HPV16/18+ group (30.8%). The two groups had comparable PD-L1 and dMMR/MSI-H status. In a large and comprehensive NGS-based evaluation of somatic alterations in pSCC, HPV16/18+ versus HPV16/18- pSCCs were molecularly distinct tumors. Our finding that TMB-high is exclusive to HPV16/18+ tumors requires confirmation in larger data sets. Penile squamous cell carcinoma (pSCC) is a rare and aggressive malignancy in the advanced setting, with poor prognosis and little success with immune-checkpoint inhibitors (ICIs) in an unselected patient approach. Human papillomavirus (HPV) infection is a known risk factor for pSCC; its impact on genomic tumor profiling is less defined. Using next-generation sequencing, we explored the genetic landscape and ICI-related biomarkers of pSCC and HPV-driven oncogenic molecular signatures. Our results indicate that HPV-positive and HPV-negative pSCCs are molecularly distinct tumors. Increased tumor mutational burden is associated with HPV-positive tumors, and could serve as a biomarker for predicting therapeutic response to ICI-based therapies. Our results support the growing literature indicating that HPV status in pSCC can be used to guide patient stratification in ICI-based clinical trials.

BioConvert: a comprehensive format converter for life sciences.

Bioinformatics is a field known for the numerous standards and formats that have been developed over the years. This plethora of formats, sometimes complementary, and often redundant, poses many challenges to bioinformatics data analysts. They constantly need to find the best tool to convert their data into the suitable format, which is often a complex, technical and time consuming task. Moreover, these small yet important tasks are often difficult to make reproducible. To overcome these difficulties, we initiated BioConvert, a collaborative project to facilitate the conversion of life science data from one format to another. BioConvert aggregates existing software within a single framework and complemented them with original code when needed. It provides a common interface to make the user experience more streamlined instead of having to learn tens of them. Currently, BioConvert supports about 50 formats and 100 direct conversions in areas such as alignment, sequencing, phylogeny, and variant calling. In addition to being useful for end-users, BioConvert can also be utilized by developers as a universal benchmarking framework for evaluating and comparing numerous conversion tools. Additionally, we provide a web server implementing an online user-friendly interface to BioConvert, hence allowing direct use for the community.

Integrative computational approach for gene expression profiling of metastatic breast cancer

Background: Breast cancer (BC) is one of the most common cancers in women worldwide, including an estimated 570,000 deaths in 2015. More than 1.5 million women (25% of all women with cancer) are diagnosed with BC every year worldwide. BC is a metastatic cancer and usually spreads to distant organs such as bone, liver, lungs and the brain. Early detection of the disease can lead to better prognosis and higher survival rates. After genome sequencing, DNA microarray analysis has become the most widely used source of genome scale data in life sciences. Microarray's definition studies produce a large number of genetic expression and other genomics performance data, which promises to provide key information on genetic functioning and interaction within and within metabolic pathways. Aim: Prediction and functional enrichment of significant genes expressed in Breast Cancer. Materials and Methods: Microarray data for this study were downloaded from NCBI gene expression omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo), with accession number of GSE157737. It includes four different kind sets of samples, i.e., healthy control neutrophils, healthy donor monocytes, metastatic BC patient G-myeloid-derived suppressor cell (G-MDSCs) and sepsis patient G-MDSCs. The statistical method was applied to identify the differentially expressed gene (DEGs) using Limma package and significant analysis of microarray (SAM) test. Results: The top ten DEGs were identified between different sample sets using Limma package. Out of 32,321 genes, 93 genes were identified to be the significant genes through SAM test. The functional annotation of significant genes was done using different databases such as gene ontology, Kyoto Encyclopedia of Genes and Genomes and Database for Annotation, Visualisation and Integrated Discovery. Significant genes were found to be enriched in haematopoietic cell lineage pathway and the cancer-causing pathway. Conclusions: This research identifies the important genes that have function in BC and regulation of such genes and pathway can be a potential treatment for the BC and prevent the tumour growth.

Metrology for data in life sciences, healthcare and pharmaceutical manufacturing: Case studies from the National Physical Laboratory

Data metrology, i.e., the evaluation of data quality and its fitness-for-purpose, is an inherent part of many disciplines including physics and engineering. In other domains such as life sciences, medicine, and pharmaceutical manufacturing these tools are often added as an afterthought, if considered at all. The use of data-driven decision making and the advent of machine learning in these industries has created an urgent demand for harmonised, high-quality, content rich, and instantly available datasets across domains. The Findable, Accessible, Interoperable, Reproducible principles are designed to improve overall quality of research data. However, these principles alone do not guarantee that data is fit-for-purpose. Issues such as missing data and metadata, insufficient knowledge of measurement conditions or data provenance are well known and can be aided by applying metrological concepts to data preparation to increase confidence. This work conducted by National Physical Laboratory Data Science team showcases life sciences and healthcare projects where data metrology has been used to improve data quality.

Data-driven drug discovery for drug repurposing

To improve the decreased efficiency of drug discovery and development, drug repurposing (also called drug repositioning) has been expected, that it is a strategy for identifying new medical indications for approved, investigational or suspended drugs. Particularly, according to the rapid expansion of medical and life science data and the remarkable technological progress of AI technology in recent years, the approach of computational drug repurposing has been attracted as one of the applications in data-driven drug discovery. Computational drug repurposing is a method of systematical and strategical research for identifying novel indication candidates and prioritizing the indication candidates based on the various profiles of drugs, genes, and diseases. In this review article, the typical data science techniques for data-driven drug repurposing, 1. drug-target interaction prediction, 2. transcriptomics-based approach by using differentially gene expression profiles, 3. natural language processing and word embedding, and their current status were summarized. We have also introduced a use case of data-driven drug repurposing for the PPARγ/α agonist Netoglitazone that we actually analyzed. In addition, as an excellent successful case of data-driven drug repurposing in recent years, we have also discussed a repurposing case reported by BenevolentAI in 2020, that Baricitinib has been identified as a potential intervention for COVID-19, based on immunomodulatory treatment by its mechanism of action as a JAK1 and JAK2 inhibition.

An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data

For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed. The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics). 109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system. Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.

Database of space life investigations and bioinformatics of microbiology in extreme environments.

Biological experiments performed in space crafts like space stations, space shuttles, and recoverable satellites has enabled extensive spaceflight life investigations (SLIs). In particular, SLIs have revealed distinguished space effects on microbial growth, survival, metabolite production, biofilm formation, virulence development and drug resistant mutations. These provide unique perspectives to ground-based microbiology and new opportunities for industrial pharmaceutical and metabolite productions. SLIs are with specialized experimental setups, analysis methods and research outcomes, which can be accessed by established databases National Aeronautics and Space Administration (NASA) Life Science Data Archive, Erasmus Experiment Archive, and NASA GeneLab. The increasing research across diverse fields may be better facilitated by databases of convenient search facilities and categorized presentation of comprehensive contents. We therefore developed the Space Life Investigation Database (SpaceLID) http://bidd.group/spacelid/, which collected SLIs from published academic papers. Currently, this database provides detailed menu search facilities and categorized contents about the studied phenomena, materials, experimental procedures, analysis methods, and research outcomes of 448 SLIs of 90 species (microbial, plant, animal, human), 81 foods and 106 pharmaceuticals, including 232 SLIs not covered by the established databases. The potential applications of SpaceLID are illustrated by the examples of published experimental design and bioinformatic analysis of spaceflight microbial phenomena.

Data platforms for open life sciences-A systematic analysis of management instruments.

Open data platforms are interfaces between data demand of and supply from their users. Yet, data platform providers frequently struggle to aggregate data to suit their users’ needs and to establish a high intensity of data exchange in a collaborative environment. Here, using open life science data platforms as an example for a diverse data structure, we systematically categorize these platforms based on their technology intermediation and the range of domains they cover to derive general and specific success factors for their management instruments. Our qualitative content analysis is based on 39 in-depth interviews with experts employed by data platforms and external stakeholders. We thus complement peer initiatives which focus solely on data quality, by additionally highlighting the data platforms’ role to enable data utilization for innovative output. Based on our analysis, we propose a clearly structured and detailed guideline for seven management instruments. This guideline helps to establish and operationalize data platforms and to best exploit the data provided. Our findings support further exploitation of the open innovation potential in the life sciences and beyond.

Global Biodata Resources: Challenges to long-term sustainability of a crucial data infrastructure

Biodata resources under threat Research in the life sciences is data-driven and critically dependent on data integration and analysis enabled by open-access biodata resources. These resources are crucial to biological, life science, and biomedical research as well as to the commercial biotechnology and pharmaceutical sectors; they have substantial impacts both in fundamental and applied research, and they underpin and amplify the research investments made by funders, primarily through increased research efficiency and reduced duplication of effort. Worldwide life sciences data resources are now a major global research infrastructure that researchers, clinicians, and private sector scientists depend on to support their work. This infrastructure is growing rapidly as funders adopt open access policies that require researchers to archive their data and as technological advances enable generation of higher data volumes and more types of data. Despite being an established and vitally important infrastructure, biodata resources are primarily funded through fixed-term and often competitive mechanisms by research funding bodies, with funding for many at risk from budget reductions or changes in strategy. The Global Biodata Coalition (GBC) was founded and is driven by global research funding bodies to aid coordination of support for the life sciences data infrastructure among funders globally and to promote the development of longer-term and more stable funding solutions for crucial resources. The Global Biodata Coalition The GBC works with funders to understand the biodata resource infrastructure, to engage with resource managers and researchers, and to move towards more internationally coordinated, sustainable and more streamlined mechanisms to support biodata resources. Further interests encompass the processes and consequences of open biodata, in particular credit-giving for data contributions and processing; inclusion of data scientists from all parts of the world; and opportunities of interdisciplinarity. GBC activities span engagement with stakeholders, such as funders, biodata resource managers and users; exploration and definition of the funding and biodata resource landscape, and the mobilisation of change that improves sustainability and coordination of biodata resources. Understanding the global biodata infrastructure Collectively, life science data resources around the world form a vast, distributed, and interconnected infrastructure, arguably forming the largest infrastructure in biology. These resources are critical for ensuring the reproducibility and integrity of the entire life sciences research enterprise, but the infrastructure as a whole is poorly described. GBC is currently undertaking two activities to understand the infrastructure, and thereby provide context for funders in supporting this infrastructure over time: an inventory of all global biodata resources globally, and identification of global core biodata resources, which are those resources within the infrastructure that are, as a collection, most crucial for life sciences and biological research worldwide. Open biodata resources archive and provide analysis of basic research data that describes and documents biodiversity, taxonomy, and biogeography. Loss or diminution of these key resources would have a detrimental impact on biodiversity research. In this talk we will describe the Global Biodata Coalition and the challenges of sustaining the global infrastructure of linked biodata resources, focusing in particular on the role of the globally accessible open access resources as the foundation for global and regional research on biodiversity.

Community-driven ELIXIR activities in single-cell omics

Single-cell omics (SCO) has revolutionized the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research. The rapid development of single-cell technologies has been accompanied by the active development of data analysis methods, resulting in a plethora of new analysis tools and strategies every year. Such a rapid development of SCO methods and tools poses several challenges in standardization, benchmarking, computational resources and training. These challenges are in line with the activities of ELIXIR, the European coordinated infrastructure for life science data. Here, we describe the current landscape of and the main challenges in SCO data, and propose the creation of the ELIXIR SCO Community, to coordinate the efforts in order to best serve SCO researchers in Europe and beyond. The Community will build on top of national experiences and pave the way towards integrated long-term solutions for SCO research.

Extended Duration Orbiter Medical Project

Biomedical issues have presented a challenge to flight physicians, scientists, and engineers ever since the advent of high-speed, high-altitude airplane flight in the 1940s. In 1958, preparations began for the first manned space flights of Project Mercury. The medical data and flight experience gained through Mercury's six flights and the Gemini, Apollo, and Skylab projects, as well as subsequent space flights, comprised the knowledge base that was used to develop and implement the Extended Duration Orbiter Medical Project (EDOMP). The EDOMP yielded substantial amounts of data in six areas of space biomedical research. In addition, a significant amount of hardware was developed and tested under the EDOMP. This hardware was designed to improve data gathering capabilities and maintain crew physical fitness, while minimizing the overall impact to the microgravity environment. The biomedical findings as well as the hardware development results realized from the EDOMP have been important to the continuing success of extended Space Shuttle flights and have formed the basis for medical studies of crew members living for three to five months aboard the Russian space station, Mir. EDOMP data and hardware are also being used in preparation for the construction and habitation of International Space Station. All data sets were grouped to be non-attributable to individuals, and submitted to NASA s Life Sciences Data Archive.

Artificial intelligence in interdisciplinary life science and drug discovery research.

Artificial intelligence in interdisciplinary life science and drug discovery research.

A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning.

Machine learning has greatly advanced over the past decade, owing to advances in algorithmic innovations, hardware acceleration, and benchmark datasets to train on domains such as computer vision, natural-language processing, and more recently the life sciences. In particular, the subfield of machine learning known as deep learning has found applications in genomics, proteomics, and metabolomics. However, a thorough assessment of how the data preprocessing methods required for the analysis of life science data affect the performance of deep learning is lacking. This work contributes to filling that gap by assessing the impact of commonly used as well as newly developed methods employed in data preprocessing workflows for metabolomics that span from raw data to processed data. The results from these analyses are summarized into a set of best practices that can be used by researchers as a starting point for downstream classification and reconstruction tasks using deep learning.

A data management infrastructure for the integration of imaging and omics data in life sciences

BackgroundAs technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management.ResultsWe propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities.ConclusionsWe describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes.