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Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data

Author

Listed:
  • Matthew C. Altman

    (Systems Immunology, Benaroya Research Institute
    University of Washington)

  • Darawan Rinchai

    (Research Branch, Sidra Medicine)

  • Nicole Baldwin

    (Baylor Institute for Immunology Research, Baylor Research Institute)

  • Mohammed Toufiq

    (Research Branch, Sidra Medicine)

  • Elizabeth Whalen

    (Systems Immunology, Benaroya Research Institute)

  • Mathieu Garand

    (Research Branch, Sidra Medicine)

  • Basirudeen Syed Ahamed Kabeer

    (Research Branch, Sidra Medicine)

  • Mohamed Alfaki

    (Research Branch, Sidra Medicine)

  • Scott R. Presnell

    (Systems Immunology, Benaroya Research Institute)

  • Prasong Khaenam

    (Systems Immunology, Benaroya Research Institute)

  • Aaron Ayllón-Benítez

    (Inserm U1219 Bordeaux Population Health Research Center, Bordeaux University)

  • Fleur Mougin

    (Inserm U1219 Bordeaux Population Health Research Center, Bordeaux University)

  • Patricia Thébault

    (LaBRI, CNRS UMR5800, Bordeaux University)

  • Laurent Chiche

    (Hopital Européen)

  • Noemie Jourde-Chiche

    (Aix-Marseille University, C2VN, INSERM 1263)

  • J. Theodore Phillips

    (Baylor Institute for Immunology Research, Baylor Research Institute)

  • Goran Klintmalm

    (Baylor Institute for Immunology Research, Baylor Research Institute)

  • Anne O’Garra

    (Laboratory of Immunoregulation and Infection, The Francis Crick Institute
    National Heart and Lung Institute, Imperial College London)

  • Matthew Berry

    (Royal Cornwall Hospitals NHS Trust)

  • Chloe Bloom

    (National Heart and Lung Institute, Imperial College London)

  • Robert J. Wilkinson

    (The Francis Crick Institute
    Imperial College
    Wellcome Center for Infectious Diseases Research in Africa and Department of Medicine, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town Observatory)

  • Christine M. Graham

    (Laboratory of Immunoregulation and Infection, The Francis Crick Institute)

  • Marc Lipman

    (UCL Respiratory, Division of Medicine, University College London)

  • Ganjana Lertmemongkolchai

    (Centre for Research and Development of Medical Diagnostic Laboratories, Faculty of Associated Medical Sciences, Khon Kaen University)

  • Davide Bedognetti

    (Research Branch, Sidra Medicine)

  • Rodolphe Thiebaut

    (Inserm U1219 Bordeaux Population Health Research Center, Bordeaux University)

  • Farrah Kheradmand

    (Baylor College of Medicine & Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey VAMC)

  • Asuncion Mejias

    (Abigail Wexner Research Institute at Nationwide Children’s Hospital and the Ohio State University School of Medicine)

  • Octavio Ramilo

    (Abigail Wexner Research Institute at Nationwide Children’s Hospital and the Ohio State University School of Medicine)

  • Karolina Palucka

    (Baylor Institute for Immunology Research, Baylor Research Institute
    The Jackson Laboratory for Genomic Medicine)

  • Virginia Pascual

    (Baylor Institute for Immunology Research, Baylor Research Institute
    Weill Cornell Medicine)

  • Jacques Banchereau

    (Baylor Institute for Immunology Research, Baylor Research Institute
    The Jackson Laboratory for Genomic Medicine)

  • Damien Chaussabel

    (Systems Immunology, Benaroya Research Institute
    Research Branch, Sidra Medicine)

Abstract

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via: https://drinchai.shinyapps.io/BloodGen3Module/ .

Suggested Citation

  • Matthew C. Altman & Darawan Rinchai & Nicole Baldwin & Mohammed Toufiq & Elizabeth Whalen & Mathieu Garand & Basirudeen Syed Ahamed Kabeer & Mohamed Alfaki & Scott R. Presnell & Prasong Khaenam & Aaro, 2021. "Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data," Nature Communications, Nature, vol. 12(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24584-w
    DOI: 10.1038/s41467-021-24584-w
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    Cited by:

    1. Samuel B. Anyona & Qiuying Cheng & Sharley A. Wasena & Shamim W. Osata & Yan Guo & Evans Raballah & Ivy Hurwitz & Clinton O. Onyango & Collins Ouma & Philip D. Seidenberg & Benjamin H. McMahon & Chris, 2024. "Entire expressed peripheral blood transcriptome in pediatric severe malarial anemia," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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