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Open AccessArticle

A Post-Processing Algorithm for miRNA Microarray Data

1
Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Leninskie Gory 1, 119991 Moscow, Russia
2
P.A. Hertsen Moscow Oncology Research Center, Branch of National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Second Botkinsky lane 3, 125284 Moscow, Russia
3
National Medical Research Radiological Center, Ministry of Health of the Russian Federation, 249036 Obninks, Russia
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School of Biomedicine, Far Eastern Federal University, 690922 Vladivostok, Russia
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Molecular Epidemiology C080, German Cancer Research Center, 69120 Heidelberg, Germany
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SciBerg e.Kfm, 68309 Mannheim, Germany
7
University Hospital Heidelberg, 69120 Heidelberg, Germany
8
Faculty of Biology and Biotechnologies, Higher School of Economics, 117312 Moscow, Russia
9
Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, 117997 Moscow, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(4), 1228; https://doi.org/10.3390/ijms21041228
Received: 30 December 2019 / Revised: 4 February 2020 / Accepted: 10 February 2020 / Published: 12 February 2020
(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)
One of the main disadvantages of using DNA microarrays for miRNA expression profiling is the inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e., false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA, and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data. Additionally, we obtained possible reasons why miRNA can appear as a false positive in microarray study using paired miRNA sequencing and array data. The use of DNA microarrays for estimating miRNA expression profile is limited by several factors. One of them consists of problems with comparing expression values of different miRNAs. In this work, we show that situation can be significantly improved if some additional information is taken into consideration in a comparison. View Full-Text
Keywords: miRNA microarrays; miRNome of breast cancer; TCGA miRNA microarrays; miRNome of breast cancer; TCGA
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Nersisyan, S.; Shkurnikov, M.; Poloznikov, A.; Turchinovich, A.; Burwinkel, B.; Anisimov, N.; Tonevitsky, A. A Post-Processing Algorithm for miRNA Microarray Data. Int. J. Mol. Sci. 2020, 21, 1228.

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