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A generic Transcriptomics Reporting Framework (TRF) for ‘Omics Data Processing and Analysis
Citation:
Gant, T., U. Sauer, S. Zhang, B. Chorley, J. Hackermüller, S. Perdichizzi, K. Tollefsen, T. Tralau, B. van Ravenzwaay, C. Yauk, W. Tong, AND A. Poole. A generic Transcriptomics Reporting Framework (TRF) for ‘Omics Data Processing and Analysis. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, 12(91):s36-s45, (2017). https://doi.org/10.1016/j.yrtph.2017.11.001
Impact/Purpose:
For each of the steps of an ‘omics study, multiple methodologies and approaches are available, many of which are incorporated into commercially or freely available software tools. However, the different methodologies are inconsistently applied by different users and data analysts. The lack of standardisation and validation of ‘omics studies is a major obstacle preventing their regulatory (i.e., EPA and other regulatory Agency) applicability and use. Against this background, the establishment of a framework of best practices for the processing and analysis of ‘omics data was one of the key objectives of the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) workshop Applying ‘omics technologies in chemical risk assessment, that took place on 10-12 October 2016 in Madrid, Spain. The recommendations from the ECETOC workshop yielded this article that presents a generic Transcriptomics Reporting Framework (TRF) for ‘omics data processing and analysis. Given the difficulties of comparing even the same data when subject to differing bioinformatics procedures, the TRF includes a Reference Baseline Analysis (RBA) method that indicates specific steps for data processing and statistical analysis from raw data to differentially expressed genes (DEGs). The formal establishment of a set of performance standards and their widespread use in ‘omics studies conducted for regulatory purposes (as well as their inclusion in the TRF as essential study element) can form an important pillar for the quality control of ‘omics studies and enhance the reproducibility and comparability of ‘omics data. When ‘omics studies are performed for regulatory purposes, such reference samples could also be used for benchmark dose modeling.
Description:
A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters to take into consideration in designing and reporting ‘omics studies in a regulatory context. The TRF takes the example of microarray studies for transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) that is ready for interpretation. It encompasses the selection of raw data; data normalisation; recognition of outliers; and statistical analysis, but its principles are also applicable to sequencing data (or to other ‘omics). The TRF does not dictate the methodology for data processing and analysis since different study objectives require different study designs. However, the TRF does include a reference baseline analysis specifying a simple data processing and analysis methodology that is suggested as a comparison point for other approaches. By providing transparency on the methodologies applied during ‘omics data processing and analysis, the TRF facilitates the comparability of different data sets, thereby increasing confidence in the interpretation of ‘omics data, and, hence, their regulatory use. Applicability of the TRF is ensured by its simplicity and generic set up. It can be applied to all types of regulatory ‘omics studies, and it can be executed using different commonly available software tools.
URLs/Downloads:
DOI: A generic Transcriptomics Reporting Framework (TRF) for ‘Omics Data Processing and Analysis