GeCoNet-Tool: a software package for gene co-expression network construction and analysis.

BACKGROUND: Network analysis is a powerful tool for studying gene regulation and identifying biological processes associated with gene function. However, constructing gene co-expression networks can be a challenging task, particularly when dealing with a large number of missing values. RESULTS: We introduce GeCoNet-Tool, an integrated gene co-expression network construction and analysis tool. The tool comprises two main parts: network construction and network analysis. In the network construction part, GeCoNet-Tool offers users various options for processing gene co-expression data derived from diverse technologies. The output of the tool is an edge list with the option of weights associated with each link. In network analysis part, the user can produce a table that includes several network properties such as communities, cores, and centrality measures. With GeCoNet-Tool, users can explore and gain insights into the complex interactions between genes.

A global [Formula: see text] gene co-expression network constructed from hundreds of experimental conditions with missing values.

BACKGROUND: Gene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze co-expression relations between genes, especially when there are missing values arising for experimental reasons. Networks are a helpful tool for studying gene co-expression, where nodes represent genes and edges represent co-expression of pairs of genes. RESULTS: In this paper, we establish a method for constructing a gene co-expression network for the Anopheles gambiae transcriptome from 257 unique studies obtained with different methodologies and experimental designs. We introduce the sliding threshold approach to select node pairs with high Pearson correlation coefficients. The resulting network, which we name AgGCN1.0, is robust to random removal of conditions and has similar characteristics to small-world and scale-free networks. Analysis of network sub-graphs revealed that the core is largely comprised of genes that encode components of the mitochondrial respiratory chain and the ribosome, while different communities are enriched for genes involved in distinct biological processes. CONCLUSION: Analysis of the network reveals that both the architecture of the core sub-network and the network communities are based on gene function, supporting the power of the proposed method for GCN construction. Application of network science methodology reveals that the overall network structure is driven to maximize the integration of essential cellular functions, possibly allowing the flexibility to add novel functions.

CLIPB4 is a central node in the protease network that regulates humoral immunity in Anopheles gambiae mosquitoes.

Insect humoral immune responses are regulated in part by protease cascades, whose components circulate as zymogens in the hemolymph. In mosquitoes, these cascades consist of clip domain serine proteases (cSPs) and/or their non-catalytic homologs (cSPHs), which form a complex network, whose molecular make-up is not fully understood. Using a systems biology approach, based on a co-expression network of gene family members that function in melanization and co-immunoprecipitation using the serine protease inhibitor (SRPN)2, a key negative regulator of the melanization response in mosquitoes, we identify the cSP CLIPB4 from the African malaria mosquito Anopheles gambiae as a central node in this protease network. CLIPB4 is tightly co-expressed with SRPN2 and forms protein complexes with SRPN2 in the hemolymph of immune-challenged female mosquitoes. Genetic and biochemical approaches validate our network analysis and show that CLIPB4 is required for melanization and antibacterial immunity, acting as a prophenoloxidase (proPO)-activating protease, which is inhibited by SRPN2. In addition, we provide novel insight into the structural organization of the cSP network in An. gambiae, by demonstrating that CLIPB4 is able to activate proCLIPB8, a cSP upstream of the proPO-activating protease CLIPB9. These data provide the first evidence that, in mosquitoes, cSPs provide branching points in immune protease networks and deliver positive reinforcement in proPO activation cascades.

CLIPB4 Is a Central Node in the Protease Network that Regulates Humoral Immunity in Anopheles gambiae Mosquitoes.

Insect humoral immune responses are regulated in part by protease cascades, whose components circulate as zymogens in the hemolymph. In mosquitoes, these cascades consist of clip-domain serine proteases (cSPs) and/or their non-catalytic homologs, which form a complex network, whose molecular make-up is not fully understood. Using a systems biology approach, based on a co-expression network of gene family members that function in melanization and co-immunoprecipitation using the serine protease inhibitor (SRPN)2, a key negative regulator of the melanization response in mosquitoes, we identify the cSP CLIPB4 from the African malaria mosquito Anopheles gambiae as a central node in this protease network. CLIPB4 is tightly co-expressed with SRPN2 and forms protein complexes with SRPN2 in the hemolymph of immune-challenged female mosquitoes. Genetic and biochemical approaches validate our network analysis and show that CLIPB4 is required for melanization and antibacterial immunity, acting as a prophenoloxidase (proPO)-activating protease, which is inhibited by SRPN2. In addition, we provide novel insight into the structural organization of the cSP network in An. gambiae, by demonstrating that CLIPB4 is able to activate proCLIPB8, a cSP upstream of the proPO-activating protease CLIPB9. These data provide the first evidence that, in mosquitoes, cSPs provide branching points in immune protease networks and deliver positive reinforcement in proPO activation cascades.

Feature learning and network structure from noisy node activity data.

In the studies of network structures, much attention has been devoted to developing approaches to reconstruct networks and predict missing links when edge-related information is given. However, such approaches are not applicable when we are only given noisy node activity data with missing values. This work presents an unsupervised learning framework to learn node vectors and construct networks from such node activity data. First, we design a scheme to generate random node sequences from node context sets, which are generated from node activity data. Then, a three-layer neural network is adopted training the node sequences to obtain node vectors, which allow us to construct networks and capture nodes with synergistic roles. Furthermore, we present an entropy-based approach to select the most meaningful neighbors for each node in the resulting network. Finally, the effectiveness of the method is validated through both synthetic and real data.

Layer reconstruction and missing link prediction of a multilayer network with maximum a posteriori estimation.

From social networks to biological networks, different types of interactions among the same set of nodes characterize distinct layers, which are termed multilayer networks. Within a multilayer network, some layers, confirmed through different experiments, could be structurally similar and interdependent. In this paper, we propose a maximum a posteriori-based method to study and reconstruct the structure of a target layer in a multilayer network. Nodes within the target layer are characterized by vectors, which are employed to compute edge weights. Further, to detect structurally similar layers, we propose a method for comparing networks based on the eigenvector centrality. Using similar layers, we obtain the parameters of the conjugate prior. With this maximum a posteriori algorithm, we can reconstruct the target layer and predict missing links. We test the method on two real multilayer networks, and the results show that the maximum a posteriori estimation is promising in reconstructing the target layer even when a large number of links is missing.