R-ODAF: Omics data analysis framework for regulatory application

Verheijen, Marcha; Meier, Matthew J.; Asensio, Juan Ochoteco; Gant, Timothy W.; Tong, Weida; Yauk, Carole L.; Caiment, Florian

doi:10.1016/j.yrtph.2022.105143

Cited by 32 publications

(16 citation statements)

References 43 publications

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“…A study-wide QC was applied to exclude samples of low quality based on established metrics . Finally, we applied the gene-level filtering criteria described in R-ODAF, which is designed to reduce false positive differentially expressed gene detection …”

Section: Methodsmentioning

confidence: 99%

“…Observations of cytotoxicity from the ATP assays were paired with read counts from TempO-Seq analyses to further identify cytotoxic concentrations and remove outliers from subsequent transcriptomic analyses. Overtly cytotoxic concentrations cause a large reduction in reads recovered during sequencing that are subsequently flagged in the Omics data analysis framework for regulatory application (R-ODAF) pipeline for removal during quality assurance and quality control (QA/QC) as described under Section …”

Section: Methodsmentioning

confidence: 99%

“…Overtly cytotoxic concentrations cause a large reduction in reads recovered during sequencing that are subsequently flagged in the Omics data analysis framework for regulatory application (R-ODAF) pipeline for removal during quality assurance and quality control (QA/QC) as described under Section 2.5. 28 2.4. TempO-Seq Assay: Library Building for Next-Generation Sequencing.…”

Section: Cell Viability Measurementmentioning

confidence: 99%

“…29 Finally, we applied the gene-level filtering criteria described in R-ODAF, which is designed to reduce false positive differentially expressed gene detection. 28 2.6. Gene Expression Biomarker Analysis: GENOMARK and TGx-DDI Classification.…”

Section: Cell Viability Measurementmentioning

confidence: 99%

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Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals

Thienpont,

Cho,

Williams

et al. 2024

Chem. Res. Toxicol.

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To modernize genotoxicity assessment and reduce reliance on experimental animals, new approach methodologies (NAMs) that provide human-relevant dose−response data are needed. Two transcriptomic biomarkers, GENOMARK and TGx-DDI, have shown a high classification accuracy for genotoxicity. As these biomarkers were extracted from different training sets, we investigated whether combining the two biomarkers in a human-derived metabolically competent cell line (i.e., HepaRG) provides complementary information for the classification of genotoxic hazard identification and potency ranking. First, the applicability of GENOMARK to TempO-Seq, a high-throughput transcriptomic technology, was evaluated. HepaRG cells were exposed for 72 h to increasing concentrations of 10 chemicals (i.e., eight known in vivo genotoxicants and two in vivo nongenotoxicants). Gene expression data were generated using the TempO-Seq technology. We found a prediction performance of 100%, confirming the applicability of GENOMARK to TempO-Seq. Classification using TGx-DDI was then compared to GENOMARK. For the chemicals identified as genotoxic, benchmark concentration modeling was conducted to perform potency ranking. The high concordance observed for both hazard classification and potency ranking by GENOMARK and TGx-DDI highlights the value of integrating these NAMs in a weight of evidence evaluation of genotoxicity.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Cell Viability Measurementmentioning

confidence: 99%

Section: Cell Viability Measurementmentioning

confidence: 99%

See 2 more Smart Citations

Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals

Thienpont,

Cho,

Williams

et al. 2024

Chem. Res. Toxicol.

Self Cite

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“…TempO-seq has emerged as a targeted alternative to traditional RNA-seq that is more amenable to high-throughput transcriptomics, 1 particularly for applications in Toxicogenomics. 2,3 TempO-seq relies on next-generation sequencing platforms and ultimately generates short read data which must be processed and quality assessed much like other technologies using this platform, including critical analysis steps such as read alignment and normalization of raw read counts across samples of varying depth and quality. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data.…”

Section: Introductionmentioning

confidence: 99%

Impact of Aligner, Normalization Method, and Sequencing Depth on TempO-seq Accuracy

Everett

Mav

Phadke

et al. 2022

Bioinform Biol Insights

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High-throughput transcriptomics has advanced through the introduction of TempO-seq, a targeted alternative to traditional RNA-seq. TempO-seq platforms use 50 nucleotide probes, each specifically designed to target a known transcript, thus allowing for reduced sequencing depth per sample compared with RNA-seq without compromising the accuracy of results. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data. However, these tools were originally designed for other data types. While they have been used for processing of early TempO-seq data, they have not been systematically assessed for accuracy or compared to determine an optimal framework for processing and analyzing TempO-seq data. In this work, we re-analyze several publicly available TempO-seq data sets covering a range of experimental designs and use corresponding RNA-seq data sets as a gold standard to rigorously assess accuracy at multiple levels. We compare 6 aligners and 5 normalization methods across various accuracy and performance metrics. Our results demonstrate the overall robust accuracy of the TempO-seq platform, independent of data processing methods. Complex aligners and advanced normalization methods do not appear to have any general advantage over simpler methods when it comes to analyzing TempO-seq data. The reduced complexity of the sequencing space, and the fact that TempO-seq probes are all equal length, appears to reduce the need for elaborate bioinformatic or statistical methods used to address these factors in RNA-seq data.

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Outcome of IWGT workshop on transcriptomic biomarkers for genotoxicity: Key considerations for bioinformatics

Meier,

Caiment,

Corton

et al. 2024

Environ and Mol Mutagen

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As a part of the International Workshop on Genotoxicity Testing (IWGT) in 2022, a workgroup was formed to evaluate the level of validation and regulatory acceptance of transcriptomic biomarkers that identify genotoxic substances. Several such biomarkers have been developed using various molecular techniques and computational approaches. Within the IWGT workgroup on transcriptomic biomarkers, bioinformatics was a central topic of discussion, focusing on the current approaches used to process the underlying molecular data to distill a reliable predictive signal; that is, a gene set that is indicative of genotoxicity and can then be used in later studies to predict potential DNA damaging properties for uncharacterized chemicals. While early studies used microarray data, a technological shift occurred in the past decade to incorporate modern transcriptome measuring techniques such as high‐throughput transcriptomics, which in turn is based on high‐throughput sequencing. Herein, we present the workgroup's review of the current bioinformatic approaches to identify genes comprising transcriptomic biomarkers. Within the context of regulatory toxicology, the reproducibility of a given analysis is critical. Therefore, the workgroup provides consensus recommendations on how to facilitate sufficient reporting of experimental parameters for the analytical procedures used in a transcriptomic biomarker study, including the recommendation to develop a biomarker‐specific reporting module within the OECD Omics Reporting Framework.

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R-ODAF: Omics data analysis framework for regulatory application

Cited by 32 publications

References 43 publications

Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals

Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals

Impact of Aligner, Normalization Method, and Sequencing Depth on TempO-seq Accuracy

Outcome of IWGT workshop on transcriptomic biomarkers for genotoxicity: Key considerations for bioinformatics

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