ReviewNanomaterials (Basel, Switzerland)2020
Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data.
Review in Nanomaterials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed.
- Safe and sustainable by design (SSbD) for nanotechnology: scientific foundations, global policy perspectives, and industrial practice.Nano convergence · 2026Review
- AOP-Based Analysis of Curated Transcriptomic Data Reveals a Context-Dependent Core Mechanism of PFAS-Induced Liver Steatosis.Environmental science & technology · 2026Article
- New approach methodologies for next-generation risk assessment of nanomaterials and nano-enabled products.Nano convergence · 2026Review
- Discrimination of Steatotic and Non-Steatotic Chemicals Through Transcriptome Analysis in Primary Human Hepatocytes.International journal of molecular sciences · 2026Article
- Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.Environmental science & technology · 2026Review
- CHIASMA: Advancing chemicals and materials safety and sustainability assessments through innovative integration of in vitro and in silico (new approach) methodologies.Computational and structural biotechnology journal · 2025Article
- A curated gene and biological system annotation of adverse outcome pathways related to human health.Scientific data · 2023Article
- Transcriptomic Analysis of Diethylstilbestrol in Daphnia Magna: Energy Metabolism and Growth Inhibition.Toxics · 2023Article
- Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets.Computational and structural biotechnology journal · 2023Review
- Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction.Frontiers in toxicology · 2023Article
- Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration.Nanomaterials (Basel, Switzerland) · 2022Article
- Prospects and challenges for FAIR toxicogenomics data.Nature nanotechnology · 2022Article
- Comparative Toxicotranscriptomics of Single Cell RNA-Seq and Conventional RNA-Seq in TCDD-Exposed Testicular Tissue.Frontiers in toxicology · 2022Article
- Nextcast: A software suite to analyse and model toxicogenomics data.Computational and structural biotechnology journal · 2022Article
- The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.Computational and structural biotechnology journal · 2022Review
- Reply to: Prospects and challenges for FAIR toxicogenomics data.Nature nanotechnology · 2022Article
- Analysis of Nanotoxicity with Integrated Omics and Mechanobiology.Nanomaterials (Basel, Switzerland) · 2021Review
- Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials.Scientific data · 2021Article
- Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.International journal of molecular sciences · 2021Review
- Editorial for the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance.Nanomaterials (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
20 authors.
Funding
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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Registered trials
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