The adaptive immune receptor repertoire community as a model for FAIR stewardship of big immunology data

Scott, Jamie K.; Breden, Felix

doi:10.1016/j.coisb.2020.10.001

Cited by 9 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…immuneML facilitates reproducibility, interoperability, and transparency of ML models immuneML draws on a broad range of techniques and design choices to ensure that it meets the latest expectations with regard to usability, reproducibility, interoperability, extensibility, and transparency [63][64][65][66] ( Figure 1 ).…”

Section: Results Immuneml Overviewmentioning

confidence: 99%

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires

et al. 2021

View full text Add to dashboard Cite

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML. 1.

show abstract

Section: Results Immuneml Overviewmentioning

confidence: 99%

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires

et al. 2021

View full text Add to dashboard Cite

show abstract

“…15 While these principles are becoming more widely adopted, SRP collaborative projects identified many barriers to making data FAIR and to facilitating broader data integration and sharing throughout the entire data lifecycle. Their experiences reflect commonalities with other institutions and initiatives from across a variety of disciplines who have recognized the value of the FAIR principles and sought to implement themdiscovering and clarifying the associated barriers along the wayincluding immunology, 16 plant sciences, 17 medical research, 18 chemistry, 19 geosciences, 20 computational toxicology, 21,22 psychology, 23 genomics, 24 ecology, 25 and human disease. 26 Figure 1.…”

Section: ■ Why Fair?mentioning

confidence: 74%

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Heacock

Lopez²,

Amolegbe

et al. 2022

Environ. Sci. Technol.

View full text Add to dashboard Cite

Environmental health sciences (EHS) span many diverse disciplines. Within the EHS community, the National Institute of Environmental Health Sciences Superfund Research Program (SRP) funds multidisciplinary research aimed to address pressing and complex issues on how people are exposed to hazardous substances and their related health consequences with the goal of identifying strategies to reduce exposures and protect human health. While disentangling the interrelationships that contribute to environmental exposures and their effects on human health over the course of life remains difficult, advances in data science and data sharing offer a path forward to explore data across disciplines to reveal new insights. Multidisciplinary SRP-funded teams are well-positioned to examine how to best integrate EHS data across diverse research domains to address multifaceted environmental health problems. As such, SRP supported collaborative research projects designed to foster and enhance the interoperability and reuse of diverse and complex data streams. This perspective synthesizes those experiences as a landscape view of the challenges identified while working to increase the FAIR-ness (Findable, Accessible, Interoperable, and Reusable) of EHS data and opportunities to address them.

show abstract

“…A similar platform, ARResT [57], combines IMGT/HighV-QUEST with the analysis and visualisation of the resulting immune repertoire data. The adaptive immune receptor repertoire community (AIRR-C) is one of a number of repositories for COVID-19 TCR repertoire data [58].…”

Section: Analysis Of "Bulk" Tcr Repertoire Sequencingmentioning

confidence: 99%

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

et al. 2022

View full text Add to dashboard Cite

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.

show abstract

The adaptive immune receptor repertoire community as a model for FAIR stewardship of big immunology data

Cited by 9 publications

References 23 publications

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

Contact Info

Product

Resources

About