Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes

Surojit Biswas (), Konstantin Kerner, Paulo José Pereira Lima Teixeira, Jeffery L. Dangl, Vladimir Jojic and Philip A. Wigge ()
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Surojit Biswas: Harvard Medical School
Konstantin Kerner: Botanical Institute, Biocenter, University of Cologne
Paulo José Pereira Lima Teixeira: Howard Hughes Medical Institute, University of North Carolina at Chapel Hill
Jeffery L. Dangl: Howard Hughes Medical Institute, University of North Carolina at Chapel Hill
Vladimir Jojic: University of North Carolina at Chapel Hill
Philip A. Wigge: Sainsbury Laboratory, University of Cambridge

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: Abstract Transcript levels are a critical determinant of the proteome and hence cellular function. Because the transcriptome is an outcome of the interactions between genes and their products, it may be accurately represented by a subset of transcript abundances. We develop a method, Tradict (transcriptome predict), capable of learning and using the expression measurements of a small subset of 100 marker genes to predict transcriptome-wide gene abundances and the expression of a comprehensive, but interpretable list of transcriptional programs that represent the major biological processes and pathways of the cell. By analyzing over 23,000 publicly available RNA-Seq data sets, we show that Tradict is robust to noise and accurate. Coupled with targeted RNA sequencing, Tradict may therefore enable simultaneous transcriptome-wide screening and mechanistic investigation at large scales.

Date: 2017
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DOI: 10.1038/ncomms15309

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