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Rödiger, S.

Publications and source records attributed to Rödiger, S..

2 recordsLinked to original sources

PCRedux: A Data Mining and Machine Learning Toolkit for qPCR Experiments

MotivationQuantitative Real-time PCR (qPCR) is a widely used -omics method for the precise quantification of nucleic acids, in which the result is associated with the presence/absence or quantity of a specific nucleic acid sequence. As the amount of qPCR data increases worldwide, the manual assessment of results becomes challenging and difficult to reproduce. To overcome this, some automatable characteristics of amplification curves have been described in the literature, often with an appropriate "rule of thumb". ResultsWe developed PCRedux to analyze and calculate 90 numerical qPCR amplification curve descriptors ( features") from large datasets of qPCR amplification curves that are aimed for interpretable machine learning and development of decision support systems. In a case study of a diverse dataset with 3181 positive, negative and ambiguous amplification curves, as assessed by three human raters, we demonstrate a sensitivity >99 % and specificity >97 % in detecting positive and negative amplification. PCRedux is unique as it goes beyond traditional qPCR analysis to capture curvature properties that improve the characterization and classification of amplification curves. The calculation of the features is reproducible and objective, since R is used as a controllable working environment. PCRedux is not a black box, but open source software following on the principle of mathematically interpretable features. These can be combined with user-defined labels for automatic multi-category classification and regression in machine learning. Availabilityhttps://cran.r-project.org/package=PCRedux. Web server: http://shtest.evrogen.net/PCRedux-app/. Documentation: https://PCRuniversum.github.io/PCRedux/.

bioinformatics↗

A multiplex microchamber diffusion assay for the antibody-based detection of microRNAs on randomly ordered microbeads

BackgroundMicroRNAs (miRNAs) are small, conserved, noncoding RNAs regulating gene expression that functions in RNA silencing and post-transcriptional regulation of gene expression. Altered miRNA profiles have been implicated in many human diseases, and due to their circulating abilities, they have excited great interest in their use as clinical biomarkers. The development of innovative methods for miRNA detection has become of high scientific and clinical interest. MethodsWe developed a diffusion-driven microbead assay and combined it with an antibody-based miRNA detection. The diffusion process was carried out in two different approaches a) co-diffusion of miRNA and antibodies (termed diffusion approach I, DAI) and b) diffusion of miRNA in an antibody-saturated environment (DAII). In both approaches, neutravidin-coated microbeads were loaded with specific biotinylated DNA capture probes, which targets either miR-21-5p, miR-30a-3p or miR-93-5p. The miRNAs were time- and dose-dependently detected in a diffusion microchamber by primary anti-DNA:RNA hybrid and fluorescence-labeled secondary antibodies using our in-house developed inverse fluorescence microscope imaging platform VideoScan. ResultsOur assay offers the advantage that several target molecules can be detected simultaneously and in real-time in one reaction environment (multiplex), without any amplification steps. We recorded the diffusion process over a period of 24 h and found that the reaction was almost completed after 2 h. The specificity of the assay was 96.7 % for DAI and 92.3 % for DAII. The detection limits were in a concentration range of 0.03-0.43 nM for DAI and 0.14-1.09 nM for DAII, depending on the miRNA. ConclusionThe miRNAs are successively exposed to the capture probe-loaded randomly ordered microbeads (p value of CSR 0.23-0.96), which leads to microbeads that become saturated with the target molecules first in front rows. Non-bonded miRNAs continue to diffuse further and can therefore subsequently bind to the microbeads with free binding sites. Our detection principle differs from other microbead assays, in which all microbeads are simultaneously mixed with the sample solution, so that all target molecules bind equally distributed to the microbeads, resulting in an averaged signal intensity.

biochemistry↗