Identifying key genes for diabetic kidney disease by bioinformatics analysis.

BACKGROUND: There are no reliable molecular targets for early diagnosis and effective treatment in the clinical management of diabetic kidney disease (DKD). To identify novel gene factors underlying the progression of DKD. METHODS: The public transcriptomic datasets of the alloxan-induced DKD model and the streptozotocin-induced DKD model were retrieved to perform an integrative bioinformatic analysis of differentially expressed genes (DEGs) shared by two experimental animal models. The dominant biological processes and pathways associated with DEGs were identified through enrichment analysis. The expression changes of the key DEGs were validated in the classic db/db DKD mouse model. RESULTS: The downregulated and upregulated genes in DKD models were uncovered from GSE139317 and GSE131221 microarray datasets. Enrichment analysis revealed that metabolic process, extracellular exosomes, and hydrolase activity are shared biological processes and molecular activity is altered in the DEGs. Importantly, Hmgcs2, angptl4, and Slco1a1 displayed a consistent expression pattern across the two DKD models. In the classic db/db DKD mice, Hmgcs2 and angptl4 were also found to be upregulated while Slco1a1 was downregulated in comparison to the control animals. CONCLUSIONS: In summary, we identified the common biological processes and molecular activity being altered in two DKD experimental models, as well as the novel gene factors (Hmgcs2, Angptl4, and Slco1a1) which may be implicated in DKD. Future works are warranted to decipher the biological role of these genes in the pathogenesis of DKD.

Screening of possible biomarkers and therapeutic targets in kidney renal clear cell carcinoma: Evidence from bioinformatic analysis.

Renal cell carcinoma (RCC), as one of the most common urological malignancies, has many histologic and molecular subtypes, among which clear cell renal cell carcinoma (ccRCC) is one of the most common causes of tumor-related deaths. However, the molecular mechanism of ccRCC remains unclear. In order to identify the candidate genes that may exist in the occurrence and development of ccRCC, microarray datasets GSE6344, GSE16441, GSE36895, GSE53757 and GSE76351 had been downloaded from Gene Expression Omnibus (GEO) database. Apart from that, the differentially expressed genes (DEGs) were screened through Bioinformatics & Evolutionary Genomics. In addition, the protein-protein interaction network (PPI) was constructed, and the module analysis was performed using STRING and Cytoscape. By virtue of DAVID online database, GO/KEGG enrichment analysis of DEGs was performed. Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes. The plug-in MCODE of Cytoscape was adopted to analyze the most significant modules of DEGs. What's more, the genes with degree greater than 10 in DEGs were selected as the hub genes. The overall survival (OS) and disease progression free survival (DFS) of 9 hub genes were analyzed through GEPIA2 online platform. As shown by the survival analysis, SLC34A1, SLC12A3, SLC12A1, PLG, and ENO2 were closely related to the OS of ccRCC, whereas SLC34A1 and LOX were closely related to DFS. Among 11 SLC members, 6 SLC members were highly expressed in non-cancerous tissues (SLC5A2, SLC12A1, SLC12A3, SLC34A1, SLC34A2, SLC34A3). Besides, SLC12A5 and SLC12A7 were highly expressed in ccRCC. Furthermore, SLC12A1-A7, SLC34A1 and SLC34A3 were closely related to OS, whereas SLC12A2/A4/A6/A7 and SLC34A1/A3 were closely related to DFS. In addition, 5 algorithms were used to analyze hub genes, the overlapping genes were AQP2 and KCNJ1. To sum up, hub gene can help us understand the molecular mechanism of the occurrence and development of ccRCC, thereby providing a theoretical basis for the diagnosis and targeted therapy of ccRCC.

Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages.

Background : Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and progression using bioinformatics and meta-analysis methods. Methods: Eligible microarray datasets were downloaded and integrated using the meta-analysis method. According to the tumor stage, GC gene chips were classified into three groups. Thereafter, the three groups' differentially expressed genes (DEGs) were identified by comparing the gene data of the tumor groups with those of matched normal specimens. Enrichment analyses were conducted based on common DEGs among the three groups. Then protein-protein interaction (PPI) networks were constructed to identify relevant hub genes and subnetworks. The effects of significant DEGs and hub genes were verified and explored in other datasets. In addition, the analysis of mutated genes was also conducted using gene data from The Cancer Genome Atlas database. Results: After integration of six microarray datasets, 1,229 common DEGs consisting of 1,065 upregulated and 164 downregulated genes were identified. Alpha-2 collagen type I (COL1A2), tissue inhibitor matrix metalloproteinase 1 (TIMP1), thymus cell antigen 1 (THY1), and biglycan (BGN) were selected as significant DEGs throughout GC development. The low expression of ghrelin (GHRL) is associated with a high lymph node ratio (LNR) and poor survival outcomes. Thereafter, we constructed a PPI network of all identified DEGs and gained 39 subnetworks and the top 20 hub genes. Enrichment analyses were performed for common DEGs, the most related subnetwork, and the top 20 hub genes. We also selected 61 metabolic DEGs to construct PPI networks and acquired the relevant hub genes. Centrosomal protein 55 (CEP55) and POLR1A were identified as hub genes associated with survival outcomes. Conclusion: The DEGs, hub genes, and enrichment analysis for GC with different stages were comprehensively investigated, which contribute to exploring the new biomarkers and therapeutic targets.

Identification of Differential Expression Genes between Volume and Pressure Overloaded Hearts Based on Bioinformatics Analysis.

Volume overload (VO) and pressure overload (PO) are two common pathophysiological conditions associated with cardiac disease. VO, in particular, often occurs in a number of diseases, and no clinically meaningful molecular marker has yet been established. We intend to find the main differential gene expression using bioinformatics analysis. GSE97363 and GSE52796 are the two gene expression array datasets related with VO and PO, respectively. The LIMMA algorithm was used to identify differentially expressed genes (DEGs) of VO and PO. The DEGs were divided into three groups and subjected to functional enrichment analysis, which comprised GO analysis, KEGG analysis, and the protein-protein interaction (PPI) network. To validate the sequencing data, cardiomyocytes from AR and TAC mouse models were used to extract RNA for qRT-PCR. The three genes with random absolute values of LogFC and indicators of heart failure (natriuretic peptide B, NPPB) were detected: carboxylesterase 1D (CES1D), whirlin (WHRN), and WNK lysine deficient protein kinase 2 (WNK2). The DEGs in VO and PO were determined to be 2761 and 1093, respectively, in this study. Following the intersection, 305 genes were obtained, 255 of which expressed the opposing regulation and 50 of which expressed the same regulation. According to the GO and pathway enrichment studies, DEGs with opposing regulation are mostly common in fatty acid degradation, propanoate metabolism, and other signaling pathways. Finally, we used Cytoscape's three techniques to identify six hub genes by intersecting 255 with the opposite expression and constructing a PPI network. Peroxisome proliferator-activated receptor (PPARα), acyl-CoA dehydrogenase medium chain (ACADM), patatin-like phospholipase domain containing 2 (PNPLA2), isocitrate dehydrogenase 3 (IDH3), heat shock protein family D member 1 (HSPD1), and dihydrolipoamide S-acetyltransferase (DLAT) were identified as six potential genes. Furthermore, we predict that the hub genes PPARα, ACADM, and PNPLA2 regulate VO myocardial changes via fatty acid metabolism and acyl-Coa dehydrogenase activity, and that these genes could be employed as basic biomarkers for VO diagnosis and treatment.

P3H4 and PLOD1 expression associates with poor prognosis in bladder cancer.

PURPOSE: The prolyl 3-hydroxylase family member 4 gene (P3H4) is involved in the development of human cancers. The association of P3H4 with bladder cancer (BC) prognosis is unclear. This study aimed to analyze the association of P3H4 with BC prognosis. METHODS: RNA-Seq data were downloaded from The Cancer Genome Atlas project and BC microarray datasets (GSE13507, GSE31684, and GSE32548) were downloaded from the Gene Expression Omnibus database. We analyzed the differences in P3H4 expression levels between BC tumors and non-tumor tissues and between samples with different clinical information. The association of P3H4 and P3H4-related genes with BC prognosis and the possibility of using P3H4 expression as a prognostic biomarker in BC patients were also analyzed. RevMan was used to perform the meta-analysis. RESULTS: P3H4 was upregulated in BC tissues compared with the adjacent non-tumor tissues (p = 4.06e-08). Univariate Cox regression analysis and meta-analysis showed that high P3H4 expression level contributed to a poor BC prognosis (Hazard ratio, HR = 1.348, 95% CI 1.140-1.594, p = 4.89e-04; meta-analysis: HR = 1.45, 95% CI 1.10-1.91; p = 9.00e-03). Among the genes related to P3H4, the PLOD1 gene was closely associated with P3H4 expression (r = 0.620, p = 2.49e-44). Also, a meta-analysis showed that PLOD1 expression was associated with a poor prognosis in BC patients (HR = 1.77, 95% CI 1.31-2.38; p = 2.00e-04). CONCLUSIONS: The P3H4 and PLOD1 genes might be used as reliable prognostic biomarkers for BC.

A Potential circRNA-miRNA-mRNA Regulatory Network in Asthmatic Airway Epithelial Cells Identified by Integrated Analysis of Microarray Datasets.

Background: Asthma is one of the most prevalent chronic respiratory diseases worldwide. Bronchial epithelial cells play a critical role in the pathogenesis of asthma. Circular RNAs (circRNAs) act as microRNA (miRNA) sponges to regulate downstream gene expression. However, the role of epithelial circRNAs in asthma remains to be investigated. This study aims to explore the potential circRNA-miRNA-messenger RNA (mRNA) regulatory network in asthma by integrated analysis of publicly available microarray datasets. Methods: Five mRNA microarray datasets derived from bronchial brushing samples from asthma patients and control subjects were downloaded from the Gene Expression Omnibus (GEO) database. The robust rank aggregation (RRA) method was used to identify robust differentially expressed genes (DEGs) in bronchial epithelial cells between asthma patients and controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to annotate the functions of the DEGs. Protein-protein interaction (PPI) analysis was performed to identify hub genes. Three miRNA databases (Targetscan, miRDB, and miRWalk) were used to predict the miRNAs which potentially target the hub genes. A miRNA microarray dataset derived from bronchial brushings was used to validate the miRNA-mRNA relationships. Finally, a circRNA-miRNA-mRNA network was constructed via the ENCORI database. Results: A total of 127 robust DEGs in bronchial epithelial cells between steroid-naïve asthma patients (n = 272) and healthy controls (n = 165) were identified from five mRNA microarray datasets. Enrichment analyses showed that DEGs were mainly enriched in several biological processes related to asthma, including humoral immune response, salivary secretion, and IL-17 signaling pathway. Nineteen hub genes were identified and were used to construct a potential epithelial circRNA-miRNA-mRNA network. The top 10 competing endogenous RNAs were hsa_circ_0001585, hsa_circ_0078031, hsa_circ_0000552, hsa-miR-30a-3p, hsa-miR-30d-3p, KIT, CD69, ADRA2A, BPIFA1, and GGH. Conclusion: Our study reveals a potential role for epithelial circRNA-miRNA-mRNA network in the pathogenesis of asthma.

Genome-scale meta-analysis of breast cancer datasets identifies promising targets for drug development.

BACKGROUND: Because of the highly heterogeneous nature of breast cancer, each subtype differs in response to several treatment regimens. This has limited the therapeutic options for metastatic breast cancer disease requiring exploration of diverse therapeutic models to target tumor specific biomarkers. METHODS: Differentially expressed breast cancer genes identified through extensive data mapping were studied for their interaction with other target proteins involved in breast cancer progression. The molecular mechanisms by which these signature genes are involved in breast cancer metastasis were also studied through pathway analysis. The potential drug targets for these genes were also identified. RESULTS: From 50 DEGs, 20 genes were identified based on fold change and p-value and the data curation of these genes helped in shortlisting 8 potential gene signatures that can be used as potential candidates for breast cancer. Their network and pathway analysis clarified the role of these genes in breast cancer and their interaction with other signaling pathways involved in the progression of disease metastasis. The miRNA targets identified through miRDB predictor provided potential miRNA targets for these genes that can be involved in breast cancer progression. Several FDA approved drug targets were identified for the signature genes easing the therapeutic options for breast cancer treatment. CONCLUSION: The study provides a more clarified role of signature genes, their interaction with other genes as well as signaling pathways. The miRNA prediction and the potential drugs identified will aid in assessing the role of these targets in breast cancer.

Screening and identification of key biomarkers in alimentary tract cancers: A bioinformatic analysis.

BACKGROUND: Alimentary tract cancers (ATCs) are the most malignant cancers in the world. Numerous studies have revealed the tumorigenesis, diagnosis and treatment of ATCs, but many mechanisms remain to be explored. METHODS: To identify the key genes of ATCs, microarray datasets of oesophageal cancer, gastric cancer and colorectal cancer were obtained from the Gene Expression Omnibus (GEO) database. In total, 207 differentially expressed genes (DEGs) were screened. KEGG and GO function enrichment analyses were conducted, and a protein-protein interaction (PPI) network was generated and gene modules analysis was performed using STRING and Cytoscape. RESULTS: Five hub genes were screened, and the associated biological processes indicated that these genes were mainly enriched in cellular processes, protein binding and metabolic processes. Clinical survival analysis showed that COL10A1 and KIF14 may be significantly associated with the tumorigenesis or pathology grade of ATCs. In addition, relative human ATC cell lines along with blood samples and tumour tissues of ATC patients were obtained. The data proved that high expression of COL10A1 and KIF14 was associated with tumorigenesis and could be detected in blood. CONCLUSION: In conclusion, the identification of hub genes in the present study helped us to elucidate the molecular mechanisms of tumorigenesis and identify potential diagnostic indicators and targeted treatment for ATCs.

Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm.

Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose, we propose a novel feature selection strategy in this paper, called IG-MBKH. A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. When searching for feature subsets using MBKH, a hyperbolic tangent function, an adaptive transfer factor, and a chaos memory weight factor are introduced to facilitate a better searching the possible feature subsets. The results indicates that the IG-MBKH algorithm can achieve improvement in convergence, the number of features and classification accuracy when compared to the BKH, MBKH, and several newest algorithms. Furthermore, we evaluate the impact of different classifiers on the performance of the strategy we propose.

Screening and authentication of molecular markers in malignant glioblastoma based on gene expression profiles.

Glioblastoma (GBM) is a malignant tumor of the central nervous system with high mortality rates. Gene expression profiling may determine the chemosensitivity of GBMs. However, the molecular mechanisms underlying GBM remain to be determined. To screen the novel key genes in its occurrence and development, two glioma databases, GSE122498 and GSE104291, were analyzed in the present study. Bioinformatics analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, the Search Tool for the Retrieval of Interacting Genes, Cytoscape, cBioPortal, and Gene Expression Profiling Interactive Analysis softwares. Patients with recurrent GBM showed worse overall survival rate. Overall, 341 differentially expressed genes (DEGs) were authenticated based on two microarray datasets, which were primarily enriched in 'cell division', 'mitotic nuclear division', 'DNA replication', 'nucleoplasm', 'cytosol, nucleus', 'protein binding', 'ATP binding', 'protein C-terminus binding', 'the cell cycle', 'DNA replication', 'oocyte meiosis' and 'valine'. The protein-protein interaction network was composed of 1,799 edges and 237 nodes. Its significant module had 10 hub genes, and CDK1, BUB1B, NDC80, NCAPG, BUB1, CCNB1, TOP2A, DLGAP5, ASPM and MELK were significantly associated with carcinogenesis and the development of GBM. The present study indicated that the DEGs and hub genes, identified based on bioinformatics analyses, had significant diagnostic value for patients with GBM.

A Novel Feature Selection Method for High-Dimensional Biomedical Data Based on an Improved Binary Clonal Flower Pollination Algorithm.

In the biomedical field, large amounts of biological and clinical data have been accumulated rapidly, which can be analyzed to emphasize the assessment of at-risk patients and improve diagnosis. However, a major challenge encountered associated with biomedical data analysis is the so-called "curse of dimensionality." For this issue, a novel feature selection method based on an improved binary clonal flower pollination algorithm is proposed to eliminate unnecessary features and ensure a highly accurate classification of disease. The absolute balance group strategy and adaptive Gaussian mutation are adopted, which can increase the diversity of the population and improve the search performance. The KNN classifier is used to evaluate the classification accuracy. Extensive experimental results in six, publicly available, high-dimensional, biomedical datasets show that the proposed method can obtain high classification accuracy and outperforms other state-of-the-art methods.

Identification of candidate genes of nasopharyngeal carcinoma by bioinformatical analysis.

OBJECTIVE: This study aimed to identify candidate genes as potential biomarkers in nasopharyngeal carcinoma (NPC) by bioinformatical analysis. METHODS: Three microarray datasets: GSE32906, GSE15170, GSE53819 were download from public database and analyzed to identify the differentially expressed genes (DEGs) between NPC and normal samples. Functional and pathway enrichment analysis of the DEGs were performed. Protein-protein interaction network and gene-transcription factor regulatory network of DEGs were constructed. And the expression of hub genes in NPC was also validated based on the public database. RESULTS: A total of 16 up-regulated and 27 down-regulated genes were screened out from the microarray datasets. Functional and pathway enrichment analysis showed that DEGs were mostly enriched in positive regulation of angiogenesis, mesenchymal cell proliferation, cell surface and DNA binding, ECM-receptor interaction pathway, PI3K-Akt signaling pathway, and pathways in cancer. Five hub genes JUN, VEGFA, FOXM1, MYB, and WNT5A were identified from the protein-protein interaction network. Subsequently, the hub gene-transcription factor regulatory network revealed that STAT3, MYC, SOX2, RUNX2 present key relations with hub genes. The expression of these five hub genes were also validated to be differentially expressed among NPC and normal samples. CONCLUSIONS: The current study indicated that the hub DEGs JUN, VEGFA, FOXM1, MYB, and WNT5A we identified might be potential therapeutic biomarkers of NPC.

Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma.

AIM: Anaplastic thyroid carcinoma (ATC) is the most lethal thyroid malignancy. Identification of novel drug targets is urgently needed. MATERIALS & METHODS: We re-analyzed several GEO datasets by systematic retrieval and data merging. Differentially expressed genes (DEGs) were filtered out. We also performed pathway enrichment analysis to interpret the data. We predicted key genes based on protein-protein interaction networks, weighted gene co-expression network analysis and genes' cancer/testis expression pattern. We also further characterized these genes using data from the Cancer Genome Atlas (TCGA) project and gene ontology annotation. RESULTS: Cell cycle-related pathways were significantly enriched in upregulated genes in ATC. We identified TRIP13, DLGAP5, HJURP, CDKN3, NEK2, KIF15, TTK, KIF2C, AURKA and TPX2 as cell cycle-related key genes with cancer/testis expression pattern. We further uncovered that most of these putative key genes were critical components during chromosome segregation. CONCLUSION: We predicted several key genes harboring potential therapeutic value in ATC. Cell cycle-related processes, especially chromosome segregation, may be the key to tumorigenesis and treatment of ATC.

CCFS: A cooperating coevolution technique for large scale feature selection on microarray datasets.

Recently, advances in bioinformatics lead to microarray high dimensional datasets. These kinds of datasets are still challenging for researchers in the area of machine learning since they suffer from small sample size and extremely large number of features. Therefore, feature selection is the problem of interest in the learning process in this area. In this paper, a novel feature selection method based on a global search (by using the main concepts of divide and conquer technique) which is called CCFS, is proposed. The proposed CCFS algorithm divides vertically (on features) the dataset by random manner and utilizes the fundamental concepts of cooperation coevolution by using a filter criterion in the fitness function in order to search the solution space via binary gravitational search algorithm. For determining the effectiveness of the proposed method some experiments are carried out on seven binary microarray high dimensional datasets. The obtained results are compared with nine state-of-the-art feature selection algorithms including Interact (INT), and Maximum Relevancy Minimum Redundancy (MRMR). The average outcomes of the results are analyzed by a statistical non-parametric test and it reveals that the proposed method has a meaningful difference to the others in terms of accuracy, sensitivity, specificity and number of selected features.

Regulatory network of GATA3 in pediatric acute lymphoblastic leukemia.

GATA3 polymorphisms were reported to be significantly associated with susceptibility of pediatric B-lineage acute lymphoblastic leukemia (ALL), by impacting on GATA3 expression. We noticed that ALL-related GATA3 polymorphism located around in the tissue-specific enhancer, and significantly associated with GATA3 expression. Although the regulatory network of GATA3 has been well reported in T cells, the functional status of GATA3 is poorly understood in B-ALL. We thus conducted genome-wide gene expression association analyses to reveal expression associated genes and pathways in nine independent B-ALL patient cohorts. In B-ALL patients, 173 candidates were identified to be significantly associated with GATA3 expression, including some reported GATA3-related genes (e.g., ITM2A) and well-known tumor-related genes (e.g., STAT4). Some of the candidates exhibit tissue-specific and subtype-specific association with GATA3. Through overexpression and down-regulation of GATA3 in leukemia cell lines, several reported and novel GATA3 regulated genes were validated. Moreover, association of GATA3 expression and its targets can be impacted by SNPs (e.g., rs4894953), which locate in the potential GATA3 binding motif. Our findings suggest that GATA3 may be involved in multiple tumor-related pathways (e.g., STAT/JAK pathway) in B-ALL to impact leukemogenesis through epigenetic regulation.

Analyses of caspase-1-regulated transcriptomes in various tissues lead to identification of novel IL-1ß-, IL-18- and sirtuin-1-independent pathways.

BACKGROUND: It is well established that caspase-1 exerts its biological activities through its downstream targets such as IL-1ß, IL-18, and Sirt-1. The microarray datasets derived from various caspase-1 knockout tissues indicated that caspase-1 can significantly impact the transcriptome. However, it is not known whether all the effects exerted by caspase-1 on transcriptome are mediated only by its well-known substrates. Therefore, we hypothesized that the effects of caspase-1 on transcriptome may be partially independent from IL-1ß, IL-18, and Sirt-1. METHODS: To determine new global and tissue-specific gene regulatory effects of caspase-1, we took novel microarray data analysis approaches including Venn analysis, cooperation analysis, and meta-analysis methods. We used these statistical methods to integrate different microarray datasets conducted on different caspase-1 knockout tissues and datasets where caspase-1 downstream targets were manipulated. RESULTS: We made the following important findings: (1) Caspase-1 exerts its regulatory effects on the majority of genes in a tissue-specific manner; (2) Caspase-1 regulatory genes partially cooperates with genes regulated by sirtuin-1 during organ injury and inflammation in adipose tissue but not in the liver; (3) Caspase-1 cooperates with IL-1ß in regulating less than half of the genes involved in cardiovascular disease, organismal injury, and cancer in mouse liver; (4) The meta-analysis identifies 40 caspase-1 globally regulated genes across tissues, suggesting that caspase-1 globally regulates many novel pathways; and (5) The meta-analysis identified new cooperatively and non-cooperatively regulated genes in caspase-1, IL-1ß, IL-18, and Sirt-1 pathways. CONCLUSIONS: Our findings suggest that caspase-1 regulates many new signaling pathways potentially via its known substrates and also via transcription factors and other proteins that are yet to be identified.

Compendium of TCDD-mediated transcriptomic response datasets in mammalian model systems.

BACKGROUND: 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is the most potent congener of the dioxin class of environmental contaminants. Exposure to TCDD causes a wide range of toxic outcomes, ranging from chloracne to acute lethality. The severity of toxicity is highly dependent on the aryl hydrocarbon receptor (AHR). Binding of TCDD to the AHR leads to changes in transcription of numerous genes. Studies evaluating the transcriptional changes brought on by TCDD may provide valuable insight into the role of the AHR in human health and disease. We therefore compiled a collection of transcriptomic datasets that can be used to aid the scientific community in better understanding the transcriptional effects of ligand-activated AHR. RESULTS: Specifically, we have created a datasets package - TCDD.Transcriptomics - for the R statistical environment, consisting of 63 unique experiments comprising 377 samples, including various combinations of 3 species (human derived cell lines, mouse and rat), 4 tissue types (liver, kidney, white adipose tissue and hypothalamus) and a wide range of TCDD exposure times and doses. These datasets have been fully standardized using consistent preprocessing and annotation packages (available as of September 14, 2015). To demonstrate the utility of this R package, a subset of "AHR-core" genes were evaluated across the included datasets. Ahrr, Nqo1 and members of the Cyp family were significantly induced following exposure to TCDD across the studies as expected while Aldh3a1 was induced specifically in rat liver. Inmt was altered only in liver tissue and primarily by rat-AHR. CONCLUSIONS: Analysis of the "AHR-core" genes demonstrates a continued need for studies surrounding the impact of AHR-activity on the transcriptome; genes believed to be consistently regulated by ligand-activated AHR show surprisingly little overlap across species and tissues. Until now, a comprehensive assessment of the transcriptome across these studies was challenging due to differences in array platforms, processing methods and annotation versions. We believe that this package, which is freely available for download ( http://labs.oicr.on.ca/boutros-lab/tcdd-transcriptomics ) will prove to be a highly beneficial resource to the scientific community evaluating the effects of TCDD exposure as well as the variety of functions of the AHR.

Mining significant high utility gene regulation sequential patterns.

BACKGROUND: Mining frequent gene regulation sequential patterns in time course microarray datasets is an important mining task in bioinformatics. Although finding such patterns are of paramount important for studying a disease, most existing work do not consider gene-disease association during gene regulation sequential pattern discovery. Moreover, they consider more absent/existence effects of genes during the mining process than taking the degrees of genes expression into account. Consequently, such techniques discover too many patterns which may not represent important information to biologists to investigate the relationships between the disease and underlying reasons hidden in gene regulation sequences. RESULTS: We propose a utility model by considering both the gene-disease association score and their degrees of expression levels under a biological investigation. We propose an efficient method called Top-HUGS, for discoverying significant high utility gene regulation sequential patterns from a time-course microarray dataset. CONCLUSIONS: In this study, the proposed methods were evaluated on a publicly available time course microarray dataset. The experimental results show higher accuracies compared to the baseline methods. Our proposed methods found that several new gene regulation sequential patterns involved in such patterns were useful for biologists and provided further insights into the mechanisms underpinning biological processes. To effectively work with the proposed method, a web interface is developed to our system using Java. To the best of our knowledge, this is the first demonstration for significant high utility gene regulation sequential pattern discovery.

Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data.

Techniques in molecular biology have permitted the gathering of an extremely large amount of information relating organisms and their genes. The current challenge is assigning a putative function to thousands of genes that have been detected in different organisms. One of the most informative types of genomic data to achieve a better knowledge of protein function is gene expression data. Based on gene expression data and assuming that genes involved in the same function should have a similar or correlated expression pattern, a function can be attributed to those genes with unknown functions when they appear to be linked in a gene co-expression network (GCN). Several tools for the construction of GCNs have been proposed and applied to plant gene expression data. Here, we review recent methodologies used for plant gene expression data and compare the results, advantages and disadvantages in order to help researchers in their choice of a method for the construction of GCNs.

Batch effect removal methods for microarray gene expression data integration: a survey.

Genomic data integration is a key goal to be achieved towards large-scale genomic data analysis. This process is very challenging due to the diverse sources of information resulting from genomics experiments. In this work, we review methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments. It has been acknowledged that the main source of variation between different MAGE datasets is due to the so-called 'batch effects'. The methods reviewed here perform data integration by removing (or more precisely attempting to remove) the unwanted variation associated with batch effects. They are presented in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process. We provide a systematic description of the MAGE data integration methodology together with some basic recommendation to help the users in choosing the appropriate tools to integrate MAGE data for large-scale analysis; and also how to evaluate them from different perspectives in order to quantify their efficiency. All genomic data used in this study for illustration purposes were retrieved from InSilicoDB http://insilico.ulb.ac.be.