RESUMO
BACKGROUND: Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear subfamily hallmarks that would allow pattern-based function assignation by which this task is mostly achieved based on the similarity principle. This is hampered by the lack of a score cut-off that is both sensitive and specific. RESULTS: HMMER Cut-off Threshold Tool (HMMERCTTER) adds a reliable cut-off threshold to the popular HMMER. Using a high quality superfamily phylogeny, it clusters a set of training sequences such that the cluster-specific HMMER profiles show cluster or subfamily member detection with 100% precision and recall (P&R), thereby generating a specific threshold as inclusion cut-off. Profiles and thresholds are then used as classifiers to screen a target dataset. Iterative inclusion of novel sequences to groups and the corresponding HMMER profiles results in high sensitivity while specificity is maintained by imposing 100% P&R self detection. In three presented case studies of protein superfamilies, classification of large datasets with 100% precision was achieved with over 95% recall. Limits and caveats are presented and explained. CONCLUSIONS: HMMERCTTER is a promising protein superfamily sequence classifier provided high quality training datasets are used. It provides a decision support system that aids in the difficult task of sequence function assignation in the twilight zone of sequence similarity. All relevant data and source codes are available from the Github repository at the following URL: https://github.com/BBCMdP/HMMERCTTER.
Assuntos
Biologia Computacional/métodos , Proteínas/química , Aprendizado de Máquina Supervisionado , Sequência de Aminoácidos , Análise por Conglomerados , ProteômicaRESUMO
Here, we describe the identification of three human genes with altered expression in thyroid diseases. One of them corresponds to insulin-like growth factor binding protein 5 (IGFBP5), which has already been described as over expressed in other cancers and, for the first time, is identified as overexpressed in thyroid tumors. The other genes, named 44 and 199, are ESTs with yet unknown function and were mapped on human chromosomes seven and four, respectively. We determined by RT-PCR the expression level of these genes in ten samples of disease-free thyroid, ten of goiter, nine of papillary carcinoma, ten of adenoma and seven of follicular carcinoma and the significance of observed differences was statistically determined. IGFBP-5 and gene 44 were significantly overexpressed in papillary carcinoma when compared to normal and goiter. Genes 44 and 199 were differentially expressed in follicular carcinoma and adenoma when compared to normal thyroid tissue.