GOPred: GO Molecular Function Prediction by Combined Classifiers

Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred)

The Impact of Multifunctional Genes on "Guilt by Association" Analysis

Many previous studies have shown that by using variants of “guilt-by-association”, gene function predictions can be made with very high statistical confidence. In these studies, it is assumed that the “associations” in the data (e.g., protein interaction partners) of a gene are necessary in establishing “guilt”. In this paper we show that multifunctionality, rather than association, is a primary driver of gene function prediction. We first show that knowledge of the degree of multifunctionality alone can produce astonishingly strong performance when used as a predictor of gene function. We then demonstrate how multifunctionality is encoded in gene interaction data (such as protein interactions and coexpression networks) and how this can feed forward into gene function prediction algorithms. We find that high-quality gene function predictions can be made using data that possesses no information on which gene interacts with which. By examining a wide range of networks from mouse, human and yeast, as well as multiple prediction methods and evaluation metrics, we provide evidence that this problem is pervasive and does not reflect the failings of any particular algorithm or data type. We propose computational controls that can be used to provide more meaningful control when estimating gene function prediction performance. We suggest that this source of bias due to multifunctionality is important to control for, with widespread implications for the interpretation of genomics studies

Manutenção da qualidade de rosas cortadas cv. Vega em soluções conservantes Keeping quality of cut roses cv. Vega in holding solutions

A rosa é considerada uma das flores de corte mais tradicionais e requisitadas no mercado mundial. Entretanto, após o corte, essas flores tornam-se altamente perecíveis devido à elevada atividade metabólica que conduz aos processos de senescência. Assim, o experimento foi realizado para verificar o efeito do uso de soluções conservantes na fisiologia pós-colheita de rosas vermelhas cortadas cultivar Vega. Utilizou-se o delineamento inteiramente casualizado, em esquema fatorial composto por dois fatores: sete tratamentos pós-colheita e quatro datas de avaliação. As flores permaneceram nas seguintes soluções de manutenção: 1) Água destilada; 2) 8-hidroxiquinolina (200 mg L-1); 3) 8-hidroxiquinolina (200 mg L-1) + Sacarose (20 mg L-1); 4) Ácido cítrico (75 mg L-1); 5) Ácido cítrico (75 mg L-1) + Sacarose (20 mg L-1); 6) 6-benziladenina (60 mg L-1); 7) 6 benziladenina (60 mg L-1) + Sacarose (20 mg L-1). Foram realizadas análises qualitativas e fisiológicas ao longo do período de avaliação. O tratamento com 8-hidroxiquinolina e ácido cítrico revelaram-se mais promissores para manter a qualidade e prolongar a vida de vaso das flores, o que foi evidenciado pela menor perda de massa fresca, atividade respiratória e conteúdo relativo de água; melhor manutenção do conteúdo de carboidratos solúveis e redutores, antocianina e coloração.<br>The rose is considered one of the most traditional and required cut flowers in the world market. However, after cutting, these flowers become highly perishable, because the high metabolic activity leads to senescence processes. The experiment was accomplished to verify the effect of holding solutions on the postharvest physiology of cut red roses, cv. Vega. A randomized design in a factorial scheme composed by two factors was used: seven postharvest treatments and four evaluation dates. The flowers remained in the following holding solutions: 1) Distilled water; 2) 8-hydroxyquinoline citrate (200 mg L-1); 3) 8-hydroxyquinoline citrate (200 mg L-1) + Sucrose (20 mg L-1); 4) Citric acid (75 mg L-1); 5) Citric acid (75 mg L-1) + Sucrose (20 mg L-1); 6) 6-benzyladenine (60 mg L-1); 7) 6-benzyladenine (60 mg L-1) + Sucrose (20 mg L-1). Qualitative and physiologic analyses were carried out during the evaluation period. The treatment with 8-hydroxyquinoline citrate and citric acid were the most promising to keep the quality and extend the vase life of flowers content; better maintenance of soluble and reducing carbohydrates, anthocyanin and color