ArticleGenomics, proteomics & bioinformatics2010
Quality assessment of transcriptome data using intrinsic statistical properties.
Article in Genomics, proteomics & bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Gas Chromatography-Mass Spectrometry (GC-MS) in the Plant Metabolomics Toolbox: GC-MS in Multi-Platform Metabolomics and Integrated Multi-Omics Research.International journal of molecular sciences · 2026Review
- Retinoic acid receptor α as a novel contributor to adrenal cortex structure and function through interactions with Wnt and Vegfa signalling.Scientific reports · 2019Article
- BIM and NOXA are mitochondrial effectors of TAF6δ-driven apoptosis.Cell death & disease · 2018Article
- Article
- Latent HIV-1 TAR Regulates 7SK-responsive P-TEFb Target Genes and Targets Cellular Immune Responses in the Absence of Tat.Genomics, proteomics & bioinformatics · 2017Article
- HIC1 controls cellular- and HIV-1- gene transcription via interactions with CTIP2 and HMGA1.Scientific reports · 2016Article
- HMGA1 recruits CTIP2-repressed P-TEFb to the HIV-1 and cellular target promoters.Nucleic acids research · 2014Article
- A TDG/CBP/RARα ternary complex mediates the retinoic acid-dependent expression of DNA methylation-sensitive genes.Genomics, proteomics & bioinformatics · 2014Article
- Alternative splicing of TAF6: downstream transcriptome impacts and upstream RNA splice control elements.PloS one · 2014Article
- CTIP2 is a negative regulator of P-TEFb.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- CDS: a fold-change based statistical test for concomitant identification of distinctness and similarity in gene expression analysis.Genomics, proteomics & bioinformatics · 2012Article
- A network perspective on metabolic inconsistency.BMC systems biology · 2012Article
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Authors and funding
4 authors.
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Abstract
In view of potential application to biomedical diagnosis, tight transcriptome data quality control is compulsory. Usually, quality control is achieved using labeling and hybridization controls added at different stages throughout the processing of the biologic RNA samples. These control measures, however, only reflect the performance of the individual technical manipulations during the entire process and have no bearing as to the continued integrity of the RNA sample itself. Here we demonstrate that intrinsic statistical properties of the resulting transcriptome data signal and signal-variance distributions and their invariance can be identified independently of the animal species studied and the labeling protocol used. From these invariant properties we have developed a data model, the parameters of which can be estimated from individual experiments and used to compute relative quality measures based on similarity with large reference datasets. These quality measures add supplementary, non-redundant information to standard quality control estimates based on spike-in and hybridization controls, and are exploitable in data analysis. A software application for analyzing datasets as well as a reference dataset for AB1700 arrays are provided. They should allow AB1700 users to easily integrate this method into their analysis pipeline, and might instigate similar developments for other transcriptome platforms.
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