Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse.

BACKGROUND: Genomic regions with recurrent DNA copy number variations (CNVs) are generally believed to encode oncogenes and tumor suppressor genes (TSGs) that drive cancer growth. However, it remains a challenge to delineate the key cancer driver genes from the regions encoding a large number of genes. RESULTS: In this study, we developed a new approach to CNV analysis based on spectral decomposition of CNV profiles into focal CNVs and broad CNVs. We performed an analysis of CNV data of 587 serous ovarian cancer samples on multiple platforms. We identified a number of novel focal regions, such as focal gain of ESR1, focal loss of LSAMP, prognostic site at 3q26.2 and losses of sub-telomere regions in multiple chromosomes. Furthermore, we performed network modularity analysis to examine the relationships among genes encoded in the focal CNV regions. Our results also showed that the recurrent focal gains were significantly associated with the known oncogenes and recurrent losses associated with TSGs and the CNVs had a greater effect on the mRNA expression of the driver genes than that of the non-driver genes. CONCLUSIONS: Our results demonstrate that spectral decomposition of CNV profiles offers a new way of understanding the role of CNVs in cancer.

MOTIVATION: Data quality is a critical issue in the analyses of DNA copy number alterations obtained from microarrays. It is commonly assumed that copy number alteration data can be modeled as piecewise constant and the measurement errors of different probes are independent. However, these assumptions do not always hold in practice. In some published datasets, we find that measurement errors are highly correlated between probes that interrogate nearby genomic loci, and the piecewise-constant model does not fit the data well. The correlated errors cause problems in downstream analysis, leading to a large number of DNA segments falsely identified as having copy number gains and losses. METHOD: We developed a simple tool, called autocorrelation scanning profile, to assess the dependence of measurement error between neighboring probes. RESULTS: Autocorrelation scanning profile can be used to check data quality and refine the analysis of DNA copy number data, which we demonstrate in some typical datasets. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions. RESULTS: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods. CONCLUSIONS: Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

Abstract Motivation: Complex diseases are generally thought to be under the influence of one or more mutated risk genes as well as genetic and environmental factors. Many traditional methods have been developed to identify susceptibility genes assuming a single-gene disease model (‘single-locus methods’). Pathway-based approaches, combined with traditional methods, consider the joint effects of genetic factor and biologic network context. With the accumulation of high-throughput SNP datasets and human biologic pathways, it becomes feasible to search for risk pathways associated with complex diseases using bioinformatics methods. By analyzing the contribution of genetic factor and biologic network context in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, we proposed an approach to prioritize risk pathways for complex diseases: Prioritizing Risk Pathways fusing SNPs and pathways (PRP). A risk-scoring (RS) measurement was used to prioritize risk biologic pathways. This could help to demonstrate the pathogenesis of complex diseases from a new perspective and provide new hypotheses. We introduced this approach to five complex diseases and found that these five diseases not only share common risk pathways, but also have their specific risk pathways, which is verified by literature retrieval. Availability: Genotype frequencies of five case–control samples were downloaded from the WTCCC online system and the address is https://www.wtccc.org.uk/info/access_to_data_samples.shtml Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.