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. 2019 Jan 8;47(D1):D113-D120.
doi: 10.1093/nar/gky981.

liqDB: a small-RNAseq knowledge discovery database for liquid biopsy studies

Affiliations

liqDB: a small-RNAseq knowledge discovery database for liquid biopsy studies

Ernesto Aparicio-Puerta et al. Nucleic Acids Res. .

Abstract

MiRNAs are important regulators of gene expression and are frequently deregulated under pathologic conditions. They are highly stable in bodily fluids which makes them feasible candidates to become minimally invasive biomarkers. In fact, several studies already proposed circulating miRNA-based biomarkers for different types of neoplastic, cardiovascular and degenerative diseases. However, many of these studies rely on small RNA sequencing experiments that are based on different RNA extraction and processing protocols, rendering results incomparable. We generated liqDB, a database for liquid biopsy small RNA sequencing profiles that provides users with meaningful information to guide their small RNA liquid biopsy research and to overcome technical and conceptual problems. By means of a user-friendly web interface, miRNA expression profiles from 1607 manually annotated samples can be queried and explored at different levels. Result pages include downloadable expression matrices, differential expression analysis, most stably expressed miRNAs, cluster analysis and relevant visualizations by means of boxplots and heatmaps. We anticipate that liqDB will be a useful tool in liquid biopsy research as it provides a consistently annotated large compilation of experiments together with tools for reproducible analysis, comparison and hypothesis generation. LiqDB is available at http://bioinfo5.ugr.es/liqdb.

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Figures

Figure 1.
Figure 1.
A schematic overview of liqDB. (A) the database can be queried in five different ways, either by browsing pre-calculated content or by instant processing of user-defined sets of samples. (B) liqDB was populated with 1607 samples from 19 different biofluids. The profiling of the data is carried out by means of sRNAbench (16) using both miRBase and miRGeneDB as annotations. (C) The general output consists of several sections, including miRNA profiles, differential expression (only if applicable) and download (shown in the figure).
Figure 2.
Figure 2.
(A) Differentially expressed genes for three different library preparation protocols. (B) Differentially expressed miRNAs between NEBnext and Illumina protocol using all plasma samples in liqDB. (C) Most Illumina samples (bright blue) cluster together except for two of them in the middle of the NEBnext cluster (dark blue).
Figure 3.
Figure 3.
Examples from the web interface. (A) Boxplot of least variable miRNAs, candidates to control downstream validation. (B) Example of differentially expressed miRNAs boxplots. (C) Example of heatmap displaying clustering of plasma samples using differentially expressed miRNAs. Men are marked in dark blue and women in bright. Generated using heatmaply (15).

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