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The protein-protein interactions (PPIs) between human and viruses play important roles in viral infection and host immune responses. Rapid accumulation of experimentally validated human-virus PPIs provides an unprecedented opportunity to investigate the regulatory pattern of viral infection. However, we are still lack of knowledge about the regulatory patterns of human-virus interactions. We collected 27,293 experimentally validated human-virus PPIs, covering 8 virus families, 140 viral proteins and 6059 human proteins. Functional enrichment analysis revealed that the viral interacting proteins were likely to be enriched in cell cycle and immune-related pathways. Moreover, we analysed the topological features of the viral interacting proteins and found that they were likely to locate in central regions of human PPI network. Based on network proximity analyses of diseases genes and human-virus interactions in the human interactome, we revealed the associations between complex diseases and viral infections. Network analysis also implicated potential antiviral drugs that were further validated by text mining. Finally, we presented the Human-Virus Protein-Protein Interaction database (HVPPI, http://bio-bigdata.hrbmu.edu.cn/HVPPI), that provides experimentally validated human-virus PPIs as well as seamlessly integrates online functional analysis tools. In summary, comprehensive understanding the regulatory pattern of human-virus interactome will provide novel insights into fundamental infectious mechanism discovery and new antiviral therapy development.

Chromatin regulators (CRs) regulate the gene transcription process through combinatorial patterns, which currently remain obscure for pan-cancer. This study identified the interaction of CRs and constructed CR-CR interaction networks across five tumor cell lines. The global interaction analysis revealed that CRs tend to function in synergistically. In addition, common and specific CRs in interaction networks were identified, and the epigenetic processes of these CRs in regulating gene transcription were analyzed. Common CRs have conserved binding sites but cooperate with different partners in multiple tumor cell lines. They also participate in gene transcription regulation, through mediation of different histone modifications (HMs). Specific CRs, ATF2 and PRDM10 were found to distinguish liver cancer samples with different prognosis. PRDM10 participates in gene transcription regulation, by exertion of influence on the DNA methylation level of liver cancer. Through analysis of the edges in the CR-CR interaction networks, it was found EP300-TAF1 has genome-wide distinct signaling patterns, which exhibit different effects on downstream targets. This analysis provides novel insights for the understanding of synergistic mechanism of CRs function, as controllers of gene transcription across cancer types.

N6-methyladenosine (mThe relationship between lncRNAs and 21 mA substantial number of positive correlation events across 33 cancer types were found. Moreover, cancer-specific lncRNAs were associated with tissue specificity, and cancer-common lncRNAs were conserved in cancer-related biological function. In particular, the mIn summary, the results from this paper will provide a valuable resource that guides both mechanistic and therapeutic roles of m

Noncoding RNAs (ncRNAs), especially microRNA (miRNA) and long noncoding RNA (lncRNA), have an impact on a variety of important biological processes during colon adenocarcinoma (COAD) progression. This includes chromatin organization, transcriptional and posttranscriptional regulation, and cell-cell signaling. The aim of this study is to identify the ncRNA-regulated modules that accompany the progression of COAD and to analyze their mechanisms, in order to screen the potential prognostic biomarkers for COAD. An integrative molecular analysis was carried out to identify the crosstalks of gene modules between different COAD stages, as well as the essential ncRNAs in the posttranscriptional regulation of these modules. 31 ncRNA regulatory modules were found to be significantly associated with overall survival in COAD patients. 17 out of the 31 modules (in which ncRNAs played essential roles) had improved the predictive ability for COAD patient survival compared to only the mRNAs of those modules, which were enriched in the core cancer hallmark pathways with closer interactions. These suggest that the ncRNAs’ regulatory modules not only exhibit close relation to COAD progression but also reflect the dynamic significant crosstalk of genes in the modules to the different malignant extent of COAD.

Human papillomavirus (HPV) is a common virus, and about 5% of all cancers worldwide is caused by persistent high-risk HPV infections. Here, we reported a comprehensive analysis of the molecular features for HPV-related cancer types using TCGA (The Cancer Genome Atlas) data with HPV status. We found that the HPV-positive cancer patients had a unique oncogenic process, tumor microenvironment, and drug response compared with HPV-negative patients. In addition, HPV improved overall survival for the four cancer types, namely, cervical squamous cell carcinoma (CESC), head and neck squamous cell carcinoma (HNSC), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC). The stronger activity of cell-cycle pathways and lower driver gene mutation rates were observed in HPV-positive patients, which implied the different carcinogenic processes between HPV-positive and HPV-negative groups. The increased activities of immune cells and differences in metabolic pathways helped explain the heterogeneity of prognosis between the two groups. Furthermore, we constructed HPV prediction models for different cancers by the virus infection score (VIS) which was linearly correlated with HPV load and found that VIS was associated with drug response. Altogether, our study reveals that HPV-positive cancer patients have unique molecular characteristics which help the development of precision medicine in HPV-positive cancers.

Pediatric central nervous system (CNS) tumors are the second most common cancer diagnosis among children. Long noncoding RNAs (lncRNAs) emerge as critical regulators of gene expression, and they play fundamental roles in immune regulation. However, knowledge on epigenetic changes in lncRNAs in diverse types of pediatric CNS tumors is lacking. Here, we integrated the DNA methylation profiles of 2,257 pediatric CNS tumors across 61 subtypes with lncRNA annotations and presented the epigenetically regulated landscape of lncRNAs. We revealed the prevalent lncRNA methylation heterogeneity across pediatric pan-CNS tumors. Based on lncRNA methylation profiles, we refined 14 lncRNA methylation clusters with distinct immune microenvironment patterns. Moreover, we found that lncRNA methylations were significantly correlated with immune cell infiltrations in diverse tumor subtypes. Immune-related lncRNAs were further identified by investigating their correlation with immune cell infiltrations and potentially regulated target genes. LncRNA with methylation perturbations potentially regulate the genes in immune-related pathways. We finally identified several candidate immune-related lncRNA biomarkers (i.e., SSTR5-AS1, CNTN4-AS1, and OSTM1-AS1) in pediatric cancer for further functional validation. In summary, our study represents a comprehensive repertoire of epigenetically regulated immune-related lncRNAs in pediatric pan-CNS tumors, and will facilitate the development of immunotherapeutic targets.

Drug–target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug–target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8–5.5 times target genes and 1.4–27.7 times drug–target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt

Cystatin E/M (CST6), a representative cysteine protease inhibitor, plays both tumor-promoting and tumor-suppressing functions and is pursued as an epigenetically therapeutic target in special cancer types. However, a comprehensive and systematic analysis for CST6 in pan-cancer level is still lacking. In the present study, we explored the expression pattern of CST6 in multiple cancer types across ∼10,000 samples from TCGA (The Cancer Genome Atlas) and ∼8,000 samples from MMDs (Merged Microarray-acquired Datasets). We found that the dynamic expression alteration of CST6 was consistent with dual function in different types of cancer. In addition, we observed that the expression of CST6 was globally regulated by the DNA methylation in its promoter region. CST6 expression was positively correlated with the epithelial cell infiltration involved in epithelial-to-mesenchymal transition (EMT) and proliferation. The relationship between CST6 and tumor microenvironment was also explored. In particular, we found that CST6 serves a protective function in the process of melanoma metastasis. Finally, the clinical association analysis further revealed the dual function of CST6 in cancer, and a combination of the epithelial cell infiltration and CST6 expression could predict the prognosis for SKCM patients. In summary, this first CST6 pan-cancer study improves the understanding of the dual functional effects on CST6 in different types of human cancer.

Melioidosis is a serious infectious disease caused by the environmental Gram-negative bacillus Burkholderia pseudomallei. It has been shown that the host immune system, mainly comprising various types of immune cells, fights against the disease. The present study was to specify correlation between septicemic melioidosis and the levels of multiple immune cells. First, the genes with differential expression patterns between patients with septicemic melioidosis (B. pseudomallei) and health donors (control/healthy) were identified. These genes being related to cytokine binding, cell adhesion molecule binding, and MHC relevant proteins may influence immune response. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed 23 enriched immune response pathways. We further leveraged the microarray data to investigate the relationship between immune response and septicemic melioidosis, using the CIBERSORT analysis. Comparison of the percentages of 22 immune cell types in B. pseudomallei vs. control/healthy revealed that those of CD4 memory resting cells, CD8+ T cells, B memory cells, and CD4 memory activated cells were low, whereas those of M0 macrophages, neutrophils, and gamma delta T cells were high. The multivariate logistic regression analysis further revealed that CD8+ T cells, M0 macrophages, neutrophils, and naive CD4+ cells were strongly associated with the onset of septicemic melioidosis, and M2 macrophages and neutrophils were associated with the survival in septicemic melioidosis. Taken together, these data point to a complex role of immune cells on the development and progression of melioidosis.

Differential co-expression-based pathway analysis is still limited and not widely used. In most current methods, the pathways were considered as gene sets, but the gene regulation relationships were not considered, and the computational speed was slow. In this article, we proposed a novel Dysregulated Pathway Identification Analysis (DysPIA) method to overcome these shortcomings. We adopted the idea of Correlation by Individual Level Product into analysis and performed a fast enrichment analysis. We constructed a combined gene-pair background which was much more sufficient than the background used in Edge Set Enrichment Analysis. In simulation study, DysPIA was able to identify the causal pathways with high AUC (0.9584 to 0.9896). In p53 mutation data, DysPIA obtained better performance than other methods. It obtained more potential dysregulated pathways that could be literature verified, and it ran much faster (∼1,700–8,000 times faster than other methods when 10,000 permutations). DysPIA was also applied to breast cancer relapse dataset and breast cancer subtype dataset. The results show that DysPIA is effective and has a great biological significance. R packages “DysPIA” and “DysPIAData” are constructed and freely available on R CRAN (https://cran.r-project.org/web/packages/DysPIA/index.htmlandhttps://cran.r-project.org/web/packages/DysPIAData/index.html), and on GitHub (https://github.com/lemonwang2020).

Numerous studies have demonstrated that lncRNAs could compete with other RNAs to bind miRNAs, as competing endogenous RNAs (ceRNAs), to regulate each other. On the other hand, ceRNAs were found to be recurrently dysregulated in cancer status. However, limited studies considered the upstream epigenetic regulatory factors that disrupted the normal competing mechanism. In the present study, we constructed the lncRNA-associated dysregulated ceRNA networks across eight cancer types. lncRNAs in the individual dysregulated network and pan-cancer core dysregulated ceRNA subnetwork were found to play more important roles than mRNAs. Integrating lncRNA methylation profiles, we identified 49 epigenetically related (ER) lncRNAs involved in the dysregulated ceRNA networks, including 18 epigenetically activated (EA) lncRNAs, 18 epigenetically silenced (ES) lncRNAs, and 13 rewired ER lncRNAs across eight cancer types. Furthermore, we evaluated the epigenetic regulating patterns of these lncRNAs and screened nine pan-cancer ER lncRNAs (six EA and three ES lncRNAs). The nine lncRNAs were found to regulate the cancer hallmarks by competing with mRNAs. Moreover, we found that integrating the expression and methylation profiles of the nine lncRNAs could predict cancer incidence in eight cancer types robustly and the cancer outcome of several cancer types. These results provide an improved understanding of methylation regulation to ceRNA and offer novel potential molecular therapeutic targets for the diagnosis and prognosis across different cancer types.

Autophagy is a self-degradation process that maintains homeostasis against stress in cells. Autophagy dysfunction plays a central role in the development of tumors, such as colorectal cancer (CRC). In this study, autophagy-related differentially expressed genes, their downstream functions, and upstream regulatory factors including RNA-binding proteins (RBP) involved in programmed cell death in the CRC were investigated. Transcription factors (TFs) and miRNAs have been shown to mainly regulate autophagy genes. Interestingly, we found that some of the RBP in the CRC, such as DDX17, SETDB1, and POLR3A, play an important regulatory role in maintaining autophagy at a basal level during growth by acting as TFs that regulate autophagy. Promoter methylations showed negative regulations on differentially expressed autophagy gene (DEAG), while copy number variations revealed a positive role in them. A proportional hazards regression analysis indicated that using autophagy-related prognostic signature can divide patients into high-risk and low-risk groups. Autophagy associated FDA-approved drugs were studied by a prognostic network. This would contribute to the identifications of new potential molecular therapeutic targets for CRC.

Recent evidence has suggested that competitive endogenous RNAs (ceRNAs) are important regulatory molecules in clear cell kidney carcinoma (KIRC) and their dysregulation may contribute to cancer pathogenesis. However, the critical roles of dysregulated ceRNAs in KIRC remain unknown. In the present study, a KIRC dysregulated ceRNA‑ceRNA network (KDCCNet) was constructed based on the 'ceRNA hypothesis' by integrating microRNA regulation and expression profiles in cancerous and normal tissues. Two dysregulated patterns of ceRNAs interaction (gain and loss) exist in KDCCNet. The two modules, which are 95% loss interactions and 97% gain interactions, were demonstrated to be able to distinguish normal samples from cancer samples. Two long non‑coding (lnc)‑RNAs (glucuronidase β pseudogene 11 and LIFR antisense RNA 1) demonstrated significant associations with KIRC prognosis. The present study of the KDCCNet revealed a novel biological mechanism for KIRC and provides novel lncRNAs as candidate prognostic biomarkers.

Autophagy is crucial in cellular homeostasis and has been implicated in the development of malignant tumors. However, the regulatory function of autophagy in cancer remains to be fully elucidated. In the present study, the autophagy-mediated competing endogenous RNA (ceRNA)‑ceRNA interaction networks in colorectal cancer (CRC) were constructed by integrating systematically expression profiles of long non‑coding RNAs and mRNAs. It was found that a large proportion of autophagy genes were inclined to target hub nodes, including a fraction of autophagy genes, by comparing with other genes within ceRNA networks, and showed preferential interaction with themselves. The present study also revealed that autophagy genes may be used as prognostic markers for cancer therapy. A risk score model based on multivariable Cox regression analysis was then used to capture novel biomarkers in connection with lncRNA for the prognosis of CRC. These biomarkers were confirmed in the test dataset and an additional independent dataset. Furthermore, the prognostic value of biomarkers is independent of conventional clinical factors. These results provide improved understanding of autophagy-mediated ceRNA regulatory mechanisms in CRC and provide novel potential molecular therapeutic targets for the diagnosis and treatment of CRC.

Transcriptomic analyses have revealed an unexpected complexity in the eukaryote transcriptome, which includes not only protein-coding transcripts but also an expanding catalog of noncoding RNAs (ncRNAs). Diverse coding and noncoding RNAs (ncRNAs) perform functions through interaction with each other in various cellular processes. In this project, we have developed RAID (http://www.rna-society.org/raid), an RNA-associated (RNA–RNA/RNA–protein) interaction database. RAID intends to provide the scientific community with all-in-one resources for efficient browsing and extraction of the RNA-associated interactions in human. This version of RAID contains more than 6100 RNA-associated interactions obtained by manually reviewing more than 2100 published papers, including 4493 RNA–RNA interactions and 1619 RNA–protein interactions. Each entry contains detailed information on an RNA-associated interaction, including RAID ID, RNA/protein symbol, RNA/protein categories, validated method, expressing tissue, literature references (Pubmed IDs), and detailed functional description. Users can query, browse, analyze, and manipulate RNA-associated (RNA–RNA/RNA–protein) interaction. RAID provides a comprehensive resource of human RNA-associated (RNA–RNA/RNA–protein) interaction network. Furthermore, this resource will help in uncovering the generic organizing principles of cellular function network.

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.

For gene expression in non-cancerous complex diseases, we systemically evaluated the sensitivities of biological discoveries to violation of the common normalization assumption. Our results indicated that gene expression may be widely up-regulated in digestive system and musculoskeletal diseases. However, global signal intensities showed little difference in other four disease types.

Cell death is a critical biological process, serving many important functions within multicellular organisms. Aberrations in cell death can contribute to the pathology of human diseases. Significant progress made in the research area enormously speeds up our understanding of the biochemical and molecular mechanisms of cell death. According to the distinct morphological and biochemical characteristics, cell death can be triggered by extrinsic or intrinsic apoptosis, regulated necrosis, autophagic cell death, and mitotic catastrophe. Nevertheless, the realization that all of these efforts seek to pursue an effective treatment and cure for the disease has spurred a significant interest in the development of promising biomarkers of cell death to early diagnose disease and accurately predict disease progression and outcome. In this review, we summarize recent knowledge about cell death, survey current and emerging biomarkers of cell death, and discuss the relationship with human diseases.

Increasing evidence has revealed that RNA subcellular localization is a very important feature for deeply understanding RNA's biological functions after being transported into intra- or extra-cellular regions. RNALocate is a web-accessible database that aims to provide a high-quality RNA subcellular localization resource and facilitate future researches on RNA function or structure. The current version of RNALocate documents more than 37 700 manually curated RNA subcellular localization entries with experimental evidence, involving more than 21 800 RNAs with 42 subcellular localizations in 65 species, mainly including Homo sapiens, Mus musculus and Saccharomyces cerevisiae etc. Besides, RNA homology, sequence and interaction data have also been integrated into RNALocate. Users can access these data through online search, browse, blast and visualization tools. In conclusion, RNALocate will be of help in elucidating the entirety of RNA subcellular localization, and developing new prediction methods. The database is available at http://www.rna-society.org/rnalocate/.

Inflammation contributing to the underlying progression of diverse human cancers has been generally appreciated, however, explorations into the molecular links between inflammation and cancer in esophagus are still at its early stage. In our study, we presented a functional module-based approach, in combination with multiple data resource (gene expression, protein-protein interactions (PPI), transcriptional and post-transcriptional regulations) to decipher the underlying links. Via mapping differentially expressed disease genes, functional disease modules were identified. As indicated, those common genes and interactions tended to play important roles in linking inflammation and cancer. Based on crosstalk analysis, we demonstrated that, although most disease genes were not shared by both kinds of modules, they might act through participating in the same or similar functions to complete the molecular links. Additionally, we applied pivot analysis to extract significant regulators for per significant crosstalk module pair. As shown, pivot regulators might manipulate vital parts of the module subnetworks and then work together to bridge inflammation and cancer in esophagus. Collectively, based on our functional module analysis, we demonstrated that shared genes or interactions, significant crosstalk modules and those significant pivot regulators were served as different functional parts underlying the molecular links between inflammation and cancer in esophagus.

Programmed cell death (PCD) is a critical biological process involved in many important processes, and defects in PCD have been linked with numerous human diseases. In recent years, the protein architecture in different PCD subroutines has been explored, but our understanding of the global network organization of the noncoding RNA (ncRNA)-mediated cell death system is limited and ambiguous. Hence, we developed the comprehensive bioinformatics resource (ncRDeathDB, www.rna-society.org/ncrdeathdb ) to archive ncRNA-associated cell death interactions. The current version of ncRDeathDB documents a total of more than 4600 ncRNA-mediated PCD entries in 12 species. ncRDeathDB provides a user-friendly interface to query, browse and manipulate these ncRNA-associated cell death interactions. Furthermore, this resource will help to visualize and navigate current knowledge of the noncoding RNA component of cell death and autophagy, to uncover the generic organizing principles of ncRNA-associated cell death systems, and to generate valuable biological hypotheses.

Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be bridged. In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma. Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes. More importantly, these overlapping genes tightly clustered together pointing to the module, deciphering the crosstalk between network-based survival-associated module and cell death in ovarian cancer.

Synthetic biologists have developed DNA/molecular modules that perform genetic logic operations in living cells to track key moments in a cell's life or change the fate of a cell. Increasing evidence has also revealed that diverse genetic logic gates capable of generating a Boolean function play critically important roles in synthetic biology. Basic genetic logic gates have been designed to combine biological science with digital logic. SynBioLGDB (http://bioinformatics.ac.cn/synbiolgdb/) aims to provide the synthetic biology community with a useful resource for efficient browsing and visualization of genetic logic gates. The current version of SynBioLGDB documents more than 189 genetic logic gates with experimental evidence involving 80 AND gates and 16 NOR gates, etc. in three species (Human, Escherichia coli and Bacillus clausii). SynBioLGDB provides a user-friendly interface through which conveniently to query and browse detailed information about these genetic logic gates. SynBioLGDB will enable more comprehensive understanding of the connection of genetic logic gates to execute complex cellular functions in living cells.

Normalization of array data relies on the assumption that most genes are not altered, which means that the signals for different samples should be scaled to have similar median or average values. However, accumulating evidence suggests that gene expression could be widely up-regulated in cancers. Our previous results and subsequent findings have shown that violation of the assumption led to erroneous interpretation of microarray data. To decipher the global signal features of microarray data from cancer samples, we empirically evaluated a large collection of gene and miRNA expression profiles and copy-number variation arrays. Our results showed that, at the transcriptomic level, genes and miRNAs are widely over-expressed in a large proportion of cancers. In contrast, at the genomic level, global raw signal intensities for methylation and copy number variation show negligible differences between cancer and normal samples. These results force us to re-evaluate the proper use of normalization procedures under different experimental conditions and for different array platforms.

Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms.

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.

Background Functional modules are basic units of cell function, and exploring them is important for understanding the organization, regulation and execution of cell processes. Functional modules in single biological networks (e.g., the protein-protein interaction network), have been the focus of recent studies. Functional modules in the integrated network are composite functional modules, which imply the complex relationships involving multiple biological interaction types, and detect them will help us understand the complexity of cell processes. Results We aimed to detect composite functional modules containing co-transcriptional regulation interaction, and protein-protein interaction, in our pre-constructed integrated network of Saccharomyces cerevisiae. We computationally extracted 15 composite functional modules, and found structural consistency between co-transcriptional regulation interaction sub-network and protein-protein interaction sub-network that was well correlated with their functional hierarchy. This type of composite functional modules was compact in structure, and was found to participate in essential cell processes such as oxidative phosphorylation and RNA splicing. Conclusions The structure of composite functional modules containing co-transcriptional regulation interaction, and protein-protein interaction reflected the cooperation of transcriptional regulation and protein function implementation, and was indicative of their important roles in essential cell functions. In addition, their structural and functional characteristics were closely related, and suggesting the complexity of the cell regulatory system.