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Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects
 
 
Review

Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data

1
Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland
2
BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
3
Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea
4
Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
5
Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
6
Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
7
Division of Toxicology, Misvik Biology, 20520 Turku, Finland
8
School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
9
National Institute for Occupational Health, Johannesburg 30333, South Africa
10
Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
11
QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland
12
Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
13
Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus
14
Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Nanomaterials 2020, 10(5), 903; https://doi.org/10.3390/nano10050903
Received: 10 March 2020 / Revised: 29 April 2020 / Accepted: 4 May 2020 / Published: 8 May 2020
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics. View Full-Text
Keywords: toxicogenomics; transcriptomics; RNA-Seq; scRNA-Seq; microarray; data preprocessing; quality check; normalization; batch effect; differential expression toxicogenomics; transcriptomics; RNA-Seq; scRNA-Seq; microarray; data preprocessing; quality check; normalization; batch effect; differential expression
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MDPI and ACS Style

Federico, A.; Serra, A.; Ha, M.K.; Kohonen, P.; Choi, J.-S.; Liampa, I.; Nymark, P.; Sanabria, N.; Cattelani, L.; Fratello, M.; Kinaret, P.A.S.; Jagiello, K.; Puzyn, T.; Melagraki, G.; Gulumian, M.; Afantitis, A.; Sarimveis, H.; Yoon, T.-H.; Grafström, R.; Greco, D. Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data. Nanomaterials 2020, 10, 903. https://doi.org/10.3390/nano10050903

AMA Style

Federico A, Serra A, Ha MK, Kohonen P, Choi J-S, Liampa I, Nymark P, Sanabria N, Cattelani L, Fratello M, Kinaret PAS, Jagiello K, Puzyn T, Melagraki G, Gulumian M, Afantitis A, Sarimveis H, Yoon T-H, Grafström R, Greco D. Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data. Nanomaterials. 2020; 10(5):903. https://doi.org/10.3390/nano10050903

Chicago/Turabian Style

Federico, Antonio, Angela Serra, My Kieu Ha, Pekka Kohonen, Jang-Sik Choi, Irene Liampa, Penny Nymark, Natasha Sanabria, Luca Cattelani, Michele Fratello, Pia Anneli Sofia Kinaret, Karolina Jagiello, Tomasz Puzyn, Georgia Melagraki, Mary Gulumian, Antreas Afantitis, Haralambos Sarimveis, Tae-Hyun Yoon, Roland Grafström, and Dario Greco. 2020. "Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data" Nanomaterials 10, no. 5: 903. https://doi.org/10.3390/nano10050903

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