Dataset Information

Misannotations of rRNA can now generate 90% false positive protein matches in metatranscriptomic studies.

ABSTRACT: In the course of analyzing 9,522,746 pyrosequencing reads from 23 stations in the Southwestern Pacific and equatorial Atlantic oceans, it came to our attention that misannotations of rRNA as proteins is now so widespread that false positive matching of rRNA pyrosequencing reads to the National Center for Biotechnology Information (NCBI) non-redundant protein database approaches 90%. One conserved portion of 23S rRNA was consistently misannotated often enough to prompt curators at Pfam to create a spurious protein family. Detailed examination of the annotation history of each seed sequence in the spurious Pfam protein family (PF10695, 'Cw-hydrolase') uncovered issues in the standard operating procedures and quality assurance programs of major sequencing centers, and other issues relating to the curation practices of those managing public databases such as GenBank and SwissProt. We offer recommendations for all these issues, and recommend as well that workers in the field of metatranscriptomics take extra care to avoid including false positive matches in their datasets.

SUBMITTER: Tripp HJ

PROVIDER: S-EPMC3203614 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Misannotations of rRNA can now generate 90% false positive protein matches in metatranscriptomic studies.

Tripp H James HJ Hewson Ian I Boyarsky Sam S Stuart Joshua M JM Zehr Jonathan P JP

Nucleic acids research 20110719 20

In the course of analyzing 9,522,746 pyrosequencing reads from 23 stations in the Southwestern Pacific and equatorial Atlantic oceans, it came to our attention that misannotations of rRNA as proteins is now so widespread that false positive matching of rRNA pyrosequencing reads to the National Center for Biotechnology Information (NCBI) non-redundant protein database approaches 90%. One conserved portion of 23S rRNA was consistently misannotated often enough to prompt curators at Pfam to create ...[more]

PMID: 21771858

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Project description:BackgroundMany crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.ResultsGene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets.ConclusionGene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives from predicted datasets increases the true positive fractions of the datasets and improves the robustness of predicted pairs as compared to random protein pairing, and eventually results in better overlap with experimental results.

Project description:Many proteins work together with others in groups called complexes in order to achieve a specific function. Discovering protein complexes is important for understanding biological processes and predict protein functions in living organisms. Large-scale and throughput techniques have made possible to compile protein-protein interaction networks (PPI networks), which have been used in several computational approaches for detecting protein complexes. Those predictions might guide future biologic experimental research. Some approaches are topology-based, where highly connected proteins are predicted to be complexes; some propose different clustering algorithms using partitioning, overlaps among clusters for networks modeled with unweighted or weighted graphs; and others use density of clusters and information based on protein functionality. However, some schemes still require much processing time or the quality of their results can be improved. Furthermore, most of the results obtained with computational tools are not accompanied by an analysis of false positives. We propose an effective and efficient mining algorithm for discovering highly connected subgraphs, which is our base for defining protein complexes. Our representation is based on transforming the PPI network into a directed acyclic graph that reduces the number of represented edges and the search space for discovering subgraphs. Our approach considers weighted and unweighted PPI networks. We compare our best alternative using PPI networks from Saccharomyces cerevisiae (yeast) and Homo sapiens (human) with state-of-the-art approaches in terms of clustering, biological metrics and execution times, as well as three gold standards for yeast and two for human. Furthermore, we analyze false positive predicted complexes searching the PDBe (Protein Data Bank in Europe) database in order to identify matching protein complexes that have been purified and structurally characterized. Our analysis shows that more than 50 yeast protein complexes and more than 300 human protein complexes found to be false positives according to our prediction method, i.e., not described in the gold standard complex databases, in fact contain protein complexes that have been characterized structurally and documented in PDBe. We also found that some of these protein complexes have recently been classified as part of a Periodic Table of Protein Complexes. The latest version of our software is publicly available at http://doi.org/10.6084/m9.figshare.5297314.v1.

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Dataset Information

Misannotations of rRNA can now generate 90% false positive protein matches in metatranscriptomic studies.

Publications

Misannotations of rRNA can now generate 90% false positive protein matches in metatranscriptomic studies.

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