An integrative approach for a network based meta-analysis of viral RNAi screens.

BACKGROUND: Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization. RESULTS: To address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules. CONCLUSIONS: We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13015-015-0035-7) contains supplementary material, which is available to authorized users

Characterising Daphnia magna as a model for ageing research

Daphnia species are gaining interest as a model for ageing research due to characteristics such as easy generation of large clonal populations and short lifespan. Most interestingly, genetically identical female and male Daphnia have evolved different average lifespans, providing a unique opportunity for investigating sex differences in longevity to provide insight into the underlying mechanisms of ageing and regulation of lifespan. Data presented here begins to delineate these mechanisms. Significant differences between sexes in markers such as lifespan, growth rate, heart rate and swimming speed in addition to lipid peroxidation product accumulation, thiol content decline and age-dependent decline in DNA damage repair efficiency are reported. Furthermore, lipids play a significant role in regulation of health and disease. Here, dynamic changes in lipid composition as a function of age and sex are presented such as statistically significant age-related changes in triglycerides, diglycerides, phosphatidylcholine, phosphatidylethanolamine, ceramide and sphingomyelin lipid groups. Most interestingly, the rate and direction of change can differ between sexes, which could partly be the cause and/or the consequence of the different average lifespans between them. Transcriptome data also revealed rate and directional differences between sexes with age. Finally, evolutionary theories of ageing focus on genetic inheritance, but many observations suggest non-genetic inheritance also influences ageing phenotype. Here, findings show maternal age-effect on offspring. Importantly, the maternal age-effects can in part be recovered if subsequent generations are produced from younger mothers. Overall, this thesis supports that investigating sex differences in longevity in the clonal organism Daphnia under controlled laboratory conditions can provide insight into principal mechanisms of ageing and lifespan regulation. Appendix 2 can be accessed on the University of Birmingham eData repository at: https://doi.org/10.25500/edata.bham.0000072

DOI 10.1186/s13015-015-0035-7

An integrative approach for a network based meta-analysis of viral RNAi screens Sandeep S Amberkar1,2,3 and Lars Kaderali1,2* Background: Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization. Results: To address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules. Conclusions: We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies