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

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          Abstract

          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.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              Gene Ontology: tool for the unification of biology

              Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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                Author and article information

                Journal
                Nanomaterials (Basel)
                Nanomaterials (Basel)
                nanomaterials
                Nanomaterials
                MDPI
                2079-4991
                08 May 2020
                May 2020
                : 10
                : 5
                : 903
                Affiliations
                [1 ]Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; antonio.federico@ 123456tuni.fi (A.F.); angela.serra@ 123456tuni.fi (A.S.); luca.cattelani@ 123456tuni.fi (L.C.); michele.fratello@ 123456tuni.fi (M.F.); pia.kinaret@ 123456helsinki.fi (P.A.S.K.)
                [2 ]BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
                [3 ]Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; hakieumy12@ 123456gmail.com (M.K.H.); gksakdma0529@ 123456gmail.com (J.-S.C.); taeyoon@ 123456hanyang.ac.kr (T.-H.Y.)
                [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; pkpekka@ 123456gmail.com (P.K.); penny.nymark@ 123456ki.se (P.N.); grafstromrc@ 123456gmail.com (R.G.)
                [7 ]Division of Toxicology, Misvik Biology, 20520 Turku, Finland
                [8 ]School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; irini.liampa@ 123456gmail.com (I.L.); hsarimv@ 123456central.ntua.gr (H.S.)
                [9 ]National Institute for Occupational Health, Johannesburg 30333, South Africa; natashaS@ 123456nioh.ac.za (N.S.); maryG@ 123456nioh.ac.za (M.G.)
                [10 ]Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
                [11 ]QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; k.jagiello@ 123456qsarlab.com (K.J.); t.puzyn@ 123456qsarlab.com (T.P.)
                [12 ]Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
                [13 ]Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; melagraki@ 123456novamechanics.com (G.M.); afantitis@ 123456novamechanics.com (A.A.)
                [14 ]Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa
                Author notes
                [* ]Correspondence: dario.greco@ 123456tuni.fi
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-2554-9879
                https://orcid.org/0000-0002-3374-1492
                https://orcid.org/0000-0002-7845-3611
                https://orcid.org/0000-0002-6350-6776
                https://orcid.org/0000-0002-3435-7775
                https://orcid.org/0000-0002-1613-6178
                https://orcid.org/0000-0003-4852-2310
                https://orcid.org/0000-0002-3997-2339
                https://orcid.org/0000-0002-3312-5331
                https://orcid.org/0000-0002-2730-6873
                https://orcid.org/0000-0002-0977-8180
                https://orcid.org/0000-0002-2743-6360
                https://orcid.org/0000-0001-9195-9003
                Article
                nanomaterials-10-00903
                10.3390/nano10050903
                7279140
                32397130
                4b0aaed9-a0c6-455c-a249-2872fbc154ea
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 March 2020
                : 04 May 2020
                Categories
                Review

                toxicogenomics,transcriptomics,rna-seq,scrna-seq,microarray,data preprocessing,quality check,normalization,batch effect,differential expression

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