Assigning protein function from domain-function associations using DomFun

Background Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions. Results We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer’s method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of $$F_{max}$$ F max and $$S_{min}$$ S min We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer’s method led to the top performance in almost all scenarios. Conclusions DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer’s method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun. Code maintained at https://github.com/ElenaRojano/DomFun. Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project.

Assigning Protein Function from Domain-Function Associations Using DomFun

Background: Protein function prediction remains a key challenge. Domain composition is key to understanding protein function, and domain-based prediction methods consistently perform well in challenges such as CAFA. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions. Results: We analysed 14 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the Gene Ontology, KEGG, Reactome and the Human Phenotype Ontology. We validated the results using the CAFA 2 benchmark platform for GO and HPO annotation, finding Simpson's index combined with Stouffer's method led to the best performance in almost all scenarios. We also found that FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. Results were similar to other high-performing domain-based methods in CAFA 2. We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 2 for GO, moreover we found good performance for the other annotation sources. As with CAFA 2, Simpson's index with Stouffer's method led to the top performance in most scenarios. Conclusions: DomFun shows comparable performance to other methods evaluated in CAFA 2 when predicting human proteins function with GO. Through our own benchmark procedure, PPP we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson's index derived domain-function associations combined using Stouffer's method. The tool has been implemented so that it could be easily adapted to incorporate other protein features, such as domain data from other sources. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun and its code is available at https://github.com/ElenaRojano/DomFun .

GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis

Motivation Recent advances in biomedical research have made massive amount of transcriptomic data available in public repositories from different sources. Due to the heterogeneity present in the individual experiments, identifying reproducible biomarkers for a given disease from multiple independent studies has become a major challenge. The widely used meta-analysis approaches, such as Fisher’s method, Stouffer’s method, minP and maxP, have at least two major limitations: (i) they are sensitive to outliers, and (ii) they perform only one statistical test for each individual study, and hence do not fully utilize the potential sample size to gain statistical power. Results Here, we propose a gene-level meta-analysis framework that overcomes these limitations and identifies a gene signature that is reliable and reproducible across multiple independent studies of a given disease. The approach provides a comprehensive global signature that can be used to understand the underlying biological phenomena, and a smaller test signature that can be used to classify future samples of a given disease. We demonstrate the utility of the framework by constructing disease signatures for influenza and Alzheimer’s disease using nine datasets including 1108 individuals. These signatures are then validated on 12 independent datasets including 912 individuals. The results indicate that the proposed approach performs better than the majority of the existing meta-analysis approaches in terms of both sensitivity as well as specificity. The proposed signatures could be further used in diagnosis, prognosis and identification of therapeutic targets. Supplementary information Supplementary data are available at Bioinformatics online.

Utility Of The Immature Platelet Fraction In Predicting Recovery With Or Without Treatment In Pediatric Immune Thrombocytopenia

Background The Immature Platelet Fraction (IPF) is a novel parameter available on the Sysmex XE-Series Hematology analyzers. It can be expressed as both IPF percent (IPF %) and absolute IPF count (AIPF#). It has been shown that IPF% is higher in Immune Thrombocytopenia (ITP) than in the normal population, and correlates with bone marrow platelet production or thrombopoiesis. Objectives To evaluates the utility of the IPF parameters to predict treatment response or recovery in pediatric ITP patients. Method This is a retrospective, single institution study performed at a 165 bed tertiary care pediatric hospital (Children's Hospital of Eastern Ontario, Ottawa, Canada). We reviewed the medical charts and electronic databases of all patients with ITP who had measured IPF parameters between June 2011 and June 2013. The standard definitions and terminology of the International Working Group were used. Patient age, phase of ITP at the time of the study (acute, persistent, and chronic), initial platelet count, IPF%, AIPF#, and type of therapy given (including observation alone) were recorded. For patients who responded to treatment or had spontaneous recovery without treatment, we analyzed the platelet count, IPF% and AIPF# on subsequent CBCs, then looked at the course of IPF% during platelet count recovery. Comparisons between groups were performed using Kruskal-Wallis tests or Mann-Whitney tests, as appropriate. Association between measured blood parameters was measured using the Spearman correlation coefficient. Within-patient Spearman correlations were also used to study the association between IPF% and platelet counts during recovery. The combined significance of these associations was determined using Stouffer’s method. Results Conclusion IPF measurements are easily available parameters that are useful in the management of ITP in pediatric patients. This study suggests the use of AIPF# as a predictive factor for recovery in pediatric ITP patients with and without treatment, and IPF% as a predictive factor for early recovery. Disclosures: No relevant conflicts of interest to declare.