Dataset Information

Identification of sample annotation errors in gene expression datasets.

ABSTRACT: The comprehensive transcriptomic analysis of clinically annotated human tissue has found widespread use in oncology, cell biology, immunology, and toxicology. In cancer research, microarray-based gene expression profiling has successfully been applied to subclassify disease entities, predict therapy response, and identify cellular mechanisms. Public accessibility of raw data, together with corresponding information on clinicopathological parameters, offers the opportunity to reuse previously analyzed data and to gain statistical power by combining multiple datasets. However, results and conclusions obviously depend on the reliability of the available information. Here, we propose gene expression-based methods for identifying sample misannotations in public transcriptomic datasets. Sample mix-up can be detected by a classifier that differentiates between samples from male and female patients. Correlation analysis identifies multiple measurements of material from the same sample. The analysis of 45 datasets (including 4913 patients) revealed that erroneous sample annotation, affecting 40 % of the analyzed datasets, may be a more widespread phenomenon than previously thought. Removal of erroneously labelled samples may influence the results of the statistical evaluation in some datasets. Our methods may help to identify individual datasets that contain numerous discrepancies and could be routinely included into the statistical analysis of clinical gene expression data.

SUBMITTER: Lohr M

PROVIDER: S-EPMC4673097 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Identification of sample annotation errors in gene expression datasets.

Lohr Miriam M Hellwig Birte B Edlund Karolina K Mattsson Johanna S M JS Botling Johan J Schmidt Marcus M Hengstler Jan G JG Micke Patrick P Rahnenführer Jörg J

Archives of toxicology 20151125 12

The comprehensive transcriptomic analysis of clinically annotated human tissue has found widespread use in oncology, cell biology, immunology, and toxicology. In cancer research, microarray-based gene expression profiling has successfully been applied to subclassify disease entities, predict therapy response, and identify cellular mechanisms. Public accessibility of raw data, together with corresponding information on clinicopathological parameters, offers the opportunity to reuse previously ana ...[more]

PMID: 26608184

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Project description:BackgroundThe genome sequence data of more than 65985 species are publicly available as of October 2021 within the National Center for Biotechnology Information (NCBI) database alone and additional genome sequences are available in other databases and also continue to accumulate at a rapid pace. However, an error-free functional annotation of these genome is essential for the research communities to fully utilize these data in an optimum and efficient manner.ResultsAn analysis of proteome sequence data of 689 fungal species (7.15 million protein sequences) was conducted to identify the presence of functional annotation errors. Proteins associated with calcium signaling events, including calcium dependent protein kinases (CDPKs), calmodulins (CaM), calmodulin-like (CML) proteins, WRKY transcription factors, selenoproteins, and proteins associated with the terpene biosynthesis pathway, were targeted in the analysis. Gene associated with CDPKs and selenoproteins are known to be absent in fungal genomes. Our analysis, however, revealed the presence of proteins that were functionally annotated as CDPK proteins. However, InterproScan analysis indicated that none of the protein sequences annotated as "calcium dependent protein kinase" were found to encode calcium binding EF-hands at the regulatory domain. Similarly, none of a protein sequences annotated as a "selenocysteine" were found to contain a Sec (U) amino acid. Proteins annotated as CaM and CMLs also had significant discrepancies. CaM proteins should contain four calcium binding EF-hands, however, a range of 2-4 calcium binding EF-hands were present in the fungal proteins that were annotated as CaM proteins. Similarly, CMLs should possess four calcium binding EF-hands, but some of the CML annotated fungal proteins possessed either three or four calcium binding EF-hands. WRKY transcription factors are characterized by the presence of a WRKY domain and are confined to the plant kingdom. Several fungal proteins, however, were annotated as WRKY transcription factors, even though they did not contain a WRKY domain.ConclusionThe presence of functional annotation errors in fungal genome and proteome databases is of considerable concern and needs to be addressed in a timely manner.

Dataset Information

Identification of sample annotation errors in gene expression datasets.

Publications

Identification of sample annotation errors in gene expression datasets.

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