Evidence for association between the HLA-DQA locus and abdominal aortic aneurysms in the Belgian population: a case control study

BACKGROUND: Chronic inflammation and autoimmunity likely contribute to the pathogenesis of abdominal aortic aneurysms (AAAs). The aim of this study was to investigate the role of autoimmunity in the etiology of AAAs using a genetic association study approach with HLA polymorphisms. METHODS: HLA-DQA1, -DQB1, -DRB1 and -DRB3-5 alleles were determined in 387 AAA cases (180 Belgian and 207 Canadian) and 426 controls (269 Belgian and 157 Canadian) by a PCR and single-strand oligonucleotide probe hybridization assay. RESULTS: We observed a potential association with the HLA-DQA1 locus among Belgian males (empirical p = 0.027, asymptotic p = 0.071). Specifically, there was a significant difference in the HLA-DQA1*0102 allele frequencies between AAA cases (67/322 alleles, 20.8%) and controls (44/356 alleles, 12.4%) in Belgian males (empirical p = 0.019, asymptotic p = 0.003). In haplotype analyses, marginally significant association was found between AAA and haplotype HLA-DQA1-DRB1 (p = 0.049 with global score statistics and p = 0.002 with haplotype-specific score statistics). CONCLUSION: This study showed potential evidence that the HLA-DQA1 locus harbors a genetic risk factor for AAAs suggesting that autoimmunity plays a role in the pathogenesis of AAAs

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

Polyketide synthase genes and the natural products potential of Dictyostelium discoideum

MOTIVATION: The genome of the social amoeba Dictyostelium discoideum contains an unusually large number of polyketide synthase (PKS) genes. An analysis of the genes is a first step towards understanding the biological roles of their products and exploiting novel products. RESULTS: A total of 45 Type I iterative PKS genes were found, 5 of which are probably pseudogenes. Catalytic domains that are homologous with known PKS sequences as well as possible novel domains were identified. The genes often occurred in clusters of 2-5 genes, where members of the cluster had very similar sequences. The D.discoideum PKS genes formed a clade distinct from fungal and bacterial genes. All nine genes examined by RT-PCR were expressed, although at different developmental stages. The promoters of PKS genes were much more divergent than the structural genes, although we have identified motifs that are unique to some PKS gene promoters