Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes

Francesca Vitali, Qike Li, A. Grant Schissler, Joanne Berghout, Colleen Kenost, Yves A Lussier

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.

Original languageEnglish (US)
Pages (from-to)789-805
Number of pages17
JournalBriefings in bioinformatics
Volume20
Issue number3
DOIs
StatePublished - May 21 2019

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Precision Medicine
Transcriptome
Medicine
Bioinformatics
Metabolites
Computational methods
Proteins
Informatics
Proteome
Computational Biology
Research Personnel
Population

Keywords

  • n-of-1
  • personalome
  • precision medicine
  • single-subject studies

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

Cite this

Developing a 'personalome' for precision medicine : emerging methods that compute interpretable effect sizes from single-subject transcriptomes. / Vitali, Francesca; Li, Qike; Schissler, A. Grant; Berghout, Joanne; Kenost, Colleen; Lussier, Yves A.

In: Briefings in bioinformatics, Vol. 20, No. 3, 21.05.2019, p. 789-805.

Research output: Contribution to journalArticle

Vitali, Francesca ; Li, Qike ; Schissler, A. Grant ; Berghout, Joanne ; Kenost, Colleen ; Lussier, Yves A. / Developing a 'personalome' for precision medicine : emerging methods that compute interpretable effect sizes from single-subject transcriptomes. In: Briefings in bioinformatics. 2019 ; Vol. 20, No. 3. pp. 789-805.
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