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Software and data for comparative analysis of weighted gene co-expression networks in human and mouse

ABSTRACT: Overview -------- This submission contains networks and code used for the s-core+ decomposition analyses presented in the manuscript "Comparative analysis of weighted gene co-expression networks in human and mouse", published in PLoS One, 2017. An example of how to use the perl script `s-core_plus.pl` is given below, under `s-core+`. In short: `perl s-core_plus.pl ` It should be noted that the code is not optimized for speed or memory efficiency. It is provided to make the s-core / s-core+ method more transparent to the community. Weighted topological overlap (wTO) networks ------------------------------------------- There are 4 networks, 2 from mouse data, and 2 from human data. And one denoted by `all`, and one by `cns`, for both species. `all` means that the network is generated comparing gene-expression profiles across all tissue types in the data. `cns` means that only the central nervous system (CNS) and brain tissue types are included in the network. The `all` networks are a lot larger than the `cns` networks, since they contain >10,000 nodes, while the `cns` only contain transcription factors, and thus around 1,000 nodes. The networks are very dense undirected, weighted networks given as link lists. The naming convention is: `hs`: Homo Sapiens (Human) `mm`: Mus Musculus (Mouse) `all`: Network constructed from all tissue types `cns`: Network constructed from brain and CNS tissue types `cutX`: Cutoff value (`cut4`: cutoff = 0.4, `cut3`: cutoff = 0.3) s-core+ ------- The s-core method works by peeling off peripheral nodes in an undirected, weighted network. For unweighted networks, the s-core is equal to the k-core network decomposition method. s-core eventually peels the network down to an innermost core, and s-core+ commences. s-core+ works by removing the weakest links in the network until a new, stable innermost s-core can be found. In general, s-core is a relatively quick method, while s-core+ is slow. For instance, `s-core_plus.pl` decomposes the entire `all` networks into its s-cores in <30 minutes, while s-core+ takes a few days to complete. The `cns` networks are quick to run, both for s-core and s-core+, due to their smaller size and denseness. To, for instance, obtain the results for the human `cns` network, run: `perl s-core_plus.pl hs_wTO_cns_cut4.txt results.txt` The first argument of `s-core_plus.pl` is the . The second argument of `s-core_plus.pl` is the . The output is in the shape of 4 tab-delimited columns: `#Node HighestCore StrengthThreshold CoreSize` `Node`: Node ID (gene name) `HighestCore`: The innermost core `Node` is a member of `StrengthThreshold`: The strength threshold value for the given s-core(+) `CoreSize`: Number of nodes in core number `HighestCore` If only the s-core results are needed, and not the s-core+ results, adjust the parameter `$networkSizeForFallback` so that it is smaller than the innermost s-core (e.g. by setting it to 1). There is also an option for verbosity. Set `$ifVerbose = "yes"` if you want insight into the progress for every s-core iteration.

SUBMITTER: Eivind Almaas

PROVIDER: S-BSST57 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-level investigation of genotype-phenotype relationships in a wide range of systems. Since mouse is an important model organism for biomedical research on human disease, it is of great interest to identify similarities and differences in the functional roles of human and mouse orthologous genes. Here, we develop a novel network comparison approach which we demonstrate by comparing two gene-expression data sets from a large number of human and mouse tissues. The method uses weighted topological overlap alongside the recently developed network-decomposition method of s-core analysis, which is suitable for making gene-centrality rankings for weighted networks. The aim is to identify globally central genes separately in the human and mouse networks. By comparing the ranked gene lists, we identify genes that display conserved or diverged centrality-characteristics across the networks. This framework only assumes a single threshold value that is chosen from a statistical analysis, and it may be applied to arbitrary network structures and edge-weight distributions, also outside the context of biology. When conducting the comparative network analysis, both within and across the two species, we find a clear pattern of enrichment of transcription factors, for the homeobox domain in particular, among the globally central genes. We also perform gene-ontology term enrichment analysis and look at disease-related genes for the separate networks as well as the network comparisons. We find that gene ontology terms related to regulation and development are generally enriched across the networks. In particular, the genes FOXE3, RHO, RUNX2, ALX3 and RARA, which are disease genes in either human or mouse, are on the top-10 list of globally central genes in the human and mouse networks.

Project description:ObjectiveVaricose veins are a common problem worldwide and can cause significant impairments in health-related quality of life, but the etiology and pathogenesis remain not well defined. This study aims to elucidate transcriptomic regulations of varicose veins by detecting differentially expressed genes, pathways and regulator genes.MethodsWe harvested great saphenous veins (GSV) from patients who underwent coronary artery bypass grafting (CABG) and varicose veins from conventional stripping surgery. RNA-Sequencing (RNA-Seq) technique was used to obtain the complete transcriptomic data of both GSVs from CABG patients and varicose veins. Weighted Gene Co-expression network analysis (WGCNA) and further analyses were then carried out with the aim to elucidate transcriptomic regulations of varicose veins by detecting differentially expressed genes, pathways and regulator genes.ResultsFrom January 2015 to December 2016, 7 GSVs from CABG patients and 13 varicose veins were obtained. WGCNA identified 4 modules. In the brown module, gene ontology (GO) analysis showed that the biological processes were focused on response to stimulus, immune response and inflammatory response, etc. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that the biological processes were focused on cytokine-cytokine receptor interaction and TNF signaling pathway, etc. In the gray module, GO analysis showed that the biological processes were skeletal myofibril assembly related. The immunohistochemistry staining showed that the expression of ASC, Caspase-1 and NLRP3 were increased in GSVs from CABG patients compared with varicose veins. Histopathological analysis showed that in the varicose veins group, the thickness of vascular wall, tunica intima, tunica media and collagen/smooth muscle ratio were significantly increased, and that the elastic fiber/internal elastic lamina ratio was decreased.ConclusionThis study shows that there are clear differences in transcriptomic information between varicose veins and GSVs from CABG patients. Some inflammatory RNAs are down-regulated in varicose veins compared with GSVs from CABG patients. Skeletal myofibril assembly pathway may play a crucial role in the pathogenesis of varicose veins. Characterization of these RNAs may provide new targets for understanding varicose veins diagnosis, progression, and treatment.

Project description:BackgroundFusarium head blight (FHB) resistance in the durum wheat breeding gene pool is rarely reported. Triticum turgidum ssp. carthlicum line Blackbird is a tetraploid relative of durum wheat that offers partial FHB resistance. Resistance QTL were identified for the durum wheat cv. Strongfield × Blackbird population on chromosomes 1A, 2A, 2B, 3A, 6A, 6B and 7B in a previous study. The objective of this study was to identify the defense mechanisms underlying the resistance of Blackbird and report candidate regulator defense genes and single nucleotide polymorphism (SNP) markers within these genes for high-resolution mapping of resistance QTL reported for the durum wheat cv. Strongfield/Blackbird population.ResultsGene network analysis identified five networks significantly (P < 0.05) associated with the resistance to FHB spread (Type II FHB resistance) one of which showed significant correlation with both plant height and relative maturity traits. Two gene networks showed subtle differences between Fusarium graminearum-inoculated and mock-inoculated plants, supporting their involvement in constitutive defense. The candidate regulator genes have been implicated in various layers of plant defense including pathogen recognition (mainly Nucleotide-binding Leucine-rich Repeat proteins), signaling pathways including the abscisic acid and mitogen activated protein (MAP) kinase, and downstream defense genes activation including transcription factors (mostly with dual roles in defense and development), and cell death regulator and cell wall reinforcement genes. The expression of five candidate genes measured by quantitative real-time PCR was correlated with that of RNA-seq, corroborating the technical and analytical accuracy of RNA-sequencing.ConclusionsGene network analysis allowed identification of candidate regulator genes and genes associated with constitutive resistance, those that will not be detected using traditional differential expression analysis. This study also shed light on the association of developmental traits with FHB resistance and partially explained the co-localization of FHB resistance with plant height and maturity QTL reported in several previous studies. It also allowed the identification of candidate hub genes within the interval of three previously reported FHB resistance QTL for the Strongfield/Blackbird population and associated SNPs for future high resolution mapping studies.

Project description:BACKGROUND:For more than a decade, gene expression data sets have been used as basis for the construction of co-expression networks used in systems biology investigations, leading to many important discoveries in a wide range of subjects spanning human disease to evolution and the development of organisms. A commonly encountered challenge in such investigations is first that of detecting, then subsequently removing, spurious correlations (i.e. links) in these networks. While access to a large number of measurements per gene would reduce this problem, often only a small number of measurements are available. The weighted Topological Overlap (wTO) measure, which incorporates information from the shared network-neighborhood of a given gene-pair into a single score, is a metric that is frequently used with the implicit expectation of producing higher-quality networks. However, the actual extent to which wTO improves on the accuracy of a co-expression analysis has not been quantified. RESULTS:Here, we used a large-sample biological data set containing 338 gene-expression measurements per gene as a reference system. From these data, we generated ensembles consisting of 10, 20 and 50 randomly selected measurements to emulate low-quality data sets, finding that the wTO measure consistently generates more robust scores than what results from simple correlation calculations. Furthermore, for the data sets consisting of only 10 and 20 samples per gene, we find that wTO serves as a better predictor of the correlation scores generated from the full data set. However, we find that using wTO as a score for network building substantially alters several topographical aspects of the resulting networks, with no conclusive evidence that the resulting structure is more accurate. Importantly, we find that the much used approach of applying a soft-threshold modifier to link weights prior to computing the wTO substantially decreases the robustness of the resulting wTO network, but increases the predictive power of wTO networks with regards to the reference correlation (soft threshold) network, particularly as the size of the data sets increases. CONCLUSION:Our analysis demonstrates that, in agreement with previous assumptions, the wTO approach is capable of significantly improving the fidelity of co-expression networks, and that this effect is especially evident for cases of low-sample number gene-expression data sets.

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Software and data for comparative analysis of weighted gene co-expression networks in human and mouse

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