Identification and correction of abnormal, incomplete and mispredicted proteins in public databases

Alinda Nagy, H. Hegyi, Krisztina Farkas, H. Tordai, Evelin Kozma, L. Bányai, L. Patthy

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

Background: Despite significant improvements in computational annotation of genomes, sequences of abnormal, incomplete or incorrectly predicted genes and proteins remain abundant in public databases. Since the majority of incomplete, abnormal or mispredicted entries are not annotated as such, these errors seriously affect the reliability of these databases. Here we describe the MisPred approach that may provide an efficient means for the quality control of databases. The current version of the MisPred approach uses five distinct routines for identifying abnormal, incomplete or mispredicted entries based on the principle that a sequence is likely to be incorrect if some of its features conflict with our current knowledge about protein-coding genes and proteins: (i) conflict between the predicted subcellular localization of proteins and the absence of the corresponding sequence signals; (ii) presence of extracellular and cytoplasmic domains and the absence of transmembrane segments; (iii) co-occurrence of extracellular and nuclear domains; (iv) violation of domain integrity; (v) chimeras encoded by two or more genes located on different chromosomes. Results: Analyses of predicted EnsEMBL protein sequences of nine deuterostome (Homo sapiens, Mus musculus, Rattus norvegicus, Monodelphis domestica, Gallus gallus, Xenopus tropicalis, Fugu rubripes, Danio rerio and Ciona intestinalis) and two protostome species (Caenorhabditis elegans and Drosophila melanogaster) have revealed that the absence of expected signal peptides and violation of domain integrity account for the majority of mispredictions. Analyses of sequences predicted by NCBI's GNOMON annotation pipeline show that the rates of mispredictions are comparable to those of EnsEMBL. Interestingly, even the manually curated UniProtKB/Swiss-Prot dataset is contaminated with mispredicted or abnormal proteins, although to a much lesser extent than UniProtKB/ TrEMBL or the EnsEMBL or GNOMON-predicted entries. Conclusion: MisPred works efficiently in identifying errors in predictions generated by the most reliable gene prediction tools such as the EnsEMBL and NCBI's GNOMON pipelines and also guides the correction of errors. We suggest that application of the MisPred approach will significantly improve the quality of gene predictions and the associated databases.

Original languageEnglish
Article number353
JournalBMC Bioinformatics
Volume9
DOIs
Publication statusPublished - Aug 27 2008

Fingerprint

Genes
Databases
Gene
Proteins
Protein
Integrity
Annotation
Protein Sorting Signals
Prediction
Monodelphis
Pipelines
Takifugu
Ciona intestinalis
Drosophilidae
Protein Sequence
Quality Control
Peptides
Caenorhabditis elegans
Chromosome
Zebrafish

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

Cite this

Identification and correction of abnormal, incomplete and mispredicted proteins in public databases. / Nagy, Alinda; Hegyi, H.; Farkas, Krisztina; Tordai, H.; Kozma, Evelin; Bányai, L.; Patthy, L.

In: BMC Bioinformatics, Vol. 9, 353, 27.08.2008.

Research output: Contribution to journalArticle

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