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DraculR: A web based application for in silico haemolysis detection in high throughput small RNA sequencing data

Smith, M. D.; Leemaqz, S. Y.; Jankovic-Karasoulos, T.; McCullough, D.; McAninch, D.; Breen, J.; Roberts, C. T.; Pillman, K. A.

2022-03-31 genetic and genomic medicine
10.1101/2022.03.27.22273019 medRxiv
Show abstract

MotivationThe search for novel microRNA (miRNA) biomarkers in plasma is hampered by haemolysis, the lysis and subsequent release of red blood cell (RBC) contents, including miRNAs, into surrounding fluid. The biomarker potential of miRNAs comes in part from their multi-compartment 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 RBC derived miRNA transcripts in downstream analysis introduces a source of error that is difficult to identify post hoc 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. ResultsWe present DraculR, an interactive Shiny/R application that enables a user to upload microRNA expression data from short read sequencing of human plasma as a raw read counts table and interactively calculate a metric that indicates the degree of haemolysis contamination. Availability and implementationDraculR and its tutorial are freely available from (https://mxhp75.shinyapps.io/shinyVamp/). Code is available from (https://github.com/mxhp75/shinyVamp.git).

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.