Dataset Information

MisPred: a resource for identification of erroneous protein sequences in public databases.

ABSTRACT: Correct prediction of the structure of protein-coding genes of higher eukaryotes is still a difficult task; therefore, public databases are heavily contaminated with mispredicted sequences. The high rate of misprediction has serious consequences because it significantly affects the conclusions that may be drawn from genome-scale sequence analyses of eukaryotic genomes. Here we present the MisPred database and computational pipeline that provide efficient means for the identification of erroneous sequences in public databases. The MisPred database contains a collection of abnormal, incomplete and mispredicted protein sequences from 19 metazoan species identified as erroneous by MisPred quality control tools in the UniProtKB/Swiss-Prot, UniProtKB/TrEMBL, NCBI/RefSeq and EnsEMBL databases. Major releases of the database are automatically generated and updated regularly. The database (http://www.mispred.com) is easily accessible through a simple web interface coupled to a powerful query engine and a standard web service. The content is completely or partially downloadable in a variety of formats. DATABASE URL: http://www.mispred.com.

SUBMITTER: Nagy A

PROVIDER: S-EPMC3713709 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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MisPred: a resource for identification of erroneous protein sequences in public databases.

Nagy Alinda A Patthy László L

Database : the journal of biological databases and curation 20130717

Correct prediction of the structure of protein-coding genes of higher eukaryotes is still a difficult task; therefore, public databases are heavily contaminated with mispredicted sequences. The high rate of misprediction has serious consequences because it significantly affects the conclusions that may be drawn from genome-scale sequence analyses of eukaryotic genomes. Here we present the MisPred database and computational pipeline that provide efficient means for the identification of erroneous ...[more]

PMID: 23864220

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Project description:BackgroundTEM beta-lactamases are the main cause for resistance against beta-lactam antibiotics. Sequence information about TEM beta-lactamases is mainly found in the NCBI peptide database and TEM mutation table at http://www.lahey.org/Studies/temtable.asp. While the TEM mutation table is manually curated by experts in the lactamase field, who guarantee reliable and consistent information, the rapidly growing sequence and annotation information from the NCBI peptide database is sometimes inconsistent. Therefore, the Lactamase Engineering Database has been developed to collect the TEM beta-lactamase sequences from the NCBI peptide database and the TEM mutation table, systematically compare sequence information and naming, identify inconsistencies, and thus provide a versatile tool for reconciliation of data and for an investigation of the sequence-function relationship.DescriptionThe LacED currently provides 2399 sequence entries and 37 structure entries. Sequence information on 150 different TEM beta-lactamases was derived from the TEM mutation table which provides a unique number to each protein classified as TEM beta-lactamase. 293 TEM-like proteins were found in the NCBI protein database, but only 113 TEM beta-lactamase were common to both data sets. The 180 TEM beta-lactamases from the NCBI protein database which have not yet been assigned to a TEM number fall in three classes: (1) 89 proteins from microbial organisms and 35 proteins from cloning or expression vectors had a new mutation profile; (2) 55 proteins had inconsistent annotation in terms of TEM assignment or reported mutation profile; (3) 39 proteins are fragments. The LacED is web accessible at http://www.LacED.uni-stuttgart.de and contains multisequence alignments, structure information and reconciled annotation of TEM beta-lactamases. The LacED is weekly updated and supplies all data for download.ConclusionThe Lactamase Engineering Database enables a systematic analysis of TEM beta-lactamase sequence and annotation data from different data sources, and thus provides a valuable tool to identify inconsistencies in sequences from the NCBI peptide database, to detect TEM beta-lactamases with a novel mutation profile, and to identify new amino acid positions at which mutations can occur.

Project description:In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequency > 1% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics.Based on real-life data, we demonstrate that the quantile-based approach outperforms the absence-approach in terms of false negatives. At the same time, this quantile-based approach deals more appropriately with the variable allele frequencies of disease-causing alleles in variant databases relative to the 1%-approach and as such allows a better control of the number of false positives. We also introduce an alternative application for variant database usage and the quantile-based approach. If disease-causing variants in variant databases deviate substantially from theoretical expectancies calculated with the quantile-based approach, their association between genotype and phenotype had to be reconsidered in 12 out of 13 cases.We developed a novel method and demonstrated that this so-called quantile-based approach is a highly suitable method for variant filtering. In addition, the quantile-based approach can also be used for variant flagging. For user friendliness, lookup tables and easy-to-use R calculators are provided.

Project description:BackgroundDespite 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.ResultsAnalyses 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.ConclusionMisPred 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.

Dataset Information

MisPred: a resource for identification of erroneous protein sequences in public databases.

Publications

MisPred: a resource for identification of erroneous protein sequences in public databases.

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