DraculR: A Web-Based Application for In Silico Haemolysis Detection in High-Throughput microRNA Sequencing Data

Melanie D. Smith, Shalem Y. Leemaqz, Tanja Jankovic-Karasoulos, Dylan McCullough, Dale McAninch, Anya L. Arthurs, James Breen, Claire T. Roberts, Katherine A. Pillman

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

The search for novel microRNA (miRNA) biomarkers in plasma is hampered by haemolysis, the lysis and subsequent release of red blood cell contents, including miRNAs, into surrounding fluid. The biomarker potential of miRNAs comes in part from their multicompartment origin and the long-lived nature of miRNA transcripts in plasma, giving researchers a functional window for tissues that are otherwise difficult or disadvantageous to sample. The inclusion of red-blood-cell-derived miRNA transcripts in downstream analysis introduces a source of error that is difficult to identify posthoc and may lead to spurious results. Where access to a physical specimen is not possible, our tool will provide an in silico approach to haemolysis prediction. We present DraculR, an interactive Shiny/R application that enables a user to upload miRNA expression data from a short-read sequencing of human plasma as a raw read counts table and interactively calculate a metric that indicates the degree of haemolysis contamination. The code, DraculR web tool and its tutorial are freely available as detailed herein.

Original languageEnglish
Article number448
Number of pages10
JournalGenes
Volume14
Issue number2
DOIs
Publication statusPublished - Feb 2023

Keywords

  • bioinformatics
  • biomarker
  • haemolysis
  • microRNA
  • plasma
  • prediction

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