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Article

Cleaning the Medicago Microarray Database to Improve Gene Function Analysis

1
Department of Environmental Science and Policy, University of Milan, Via Celoria 10, 20133 Milano, Italy
2
Department of Biosciences, University of Milan, Via Celoria 26, 20133 Milano, Italy
*
Author to whom correspondence should be addressed.
Present Address: AEB (Shanghai) Trading Co., LTD., Room 301, F6, 600 Jianchuan Road Minhang District, Shanghai 200241, China.
Academic Editors: Ornella Calderini, Andrea Porceddu and Francesco Panara
Plants 2021, 10(6), 1240; https://doi.org/10.3390/plants10061240
Received: 12 March 2021 / Revised: 30 April 2021 / Accepted: 11 May 2021 / Published: 18 June 2021
(This article belongs to the Special Issue Molecular Analysis of Medicago Spp.)
Transcriptomics studies have been facilitated by the development of microarray and RNA-Seq technologies, with thousands of expression datasets available for many species. However, the quality of data can be highly variable, making the combined analysis of different datasets difficult and unreliable. Most of the microarray data for Medicago truncatula, the barrel medic, have been stored and made publicly accessible on the web database Medicago truncatula Gene Expression atlas (MtGEA). The aim of this work is to ameliorate the quality of the MtGEA database through a general method based on logical and statistical relationships among parameters and conditions. The initial 716 columns available in the dataset were reduced to 607 by evaluating the quality of data through the sum of the expression levels over the entire transcriptome probes and Pearson correlation among hybridizations. The reduced dataset shows great improvements in the consistency of the data, with a reduction in both false positives and false negatives resulting from Pearson correlation and GO enrichment analysis among genes. The approach we used is of general validity and our intent is to extend the analysis to other plant microarray databases. View Full-Text
Keywords: Medicago; MtGEA; transcriptomics; functional genomics; microarray; R programming; correlation analysis Medicago; MtGEA; transcriptomics; functional genomics; microarray; R programming; correlation analysis
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MDPI and ACS Style

Marzorati, F.; Wang, C.; Pavesi, G.; Mizzi, L.; Morandini, P. Cleaning the Medicago Microarray Database to Improve Gene Function Analysis. Plants 2021, 10, 1240. https://doi.org/10.3390/plants10061240

AMA Style

Marzorati F, Wang C, Pavesi G, Mizzi L, Morandini P. Cleaning the Medicago Microarray Database to Improve Gene Function Analysis. Plants. 2021; 10(6):1240. https://doi.org/10.3390/plants10061240

Chicago/Turabian Style

Marzorati, Francesca, Chu Wang, Giulio Pavesi, Luca Mizzi, and Piero Morandini. 2021. "Cleaning the Medicago Microarray Database to Improve Gene Function Analysis" Plants 10, no. 6: 1240. https://doi.org/10.3390/plants10061240

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