Co-expressed Gene Sets Research Articles

TF2LncRNA: identifying common transcription factors for a list of lncRNA genes from ChIP-Seq data.

High-throughput genomic technologies like lncRNA microarray and RNA-Seq often generate a set of lncRNAs of interest, yet little is known about the transcriptional regulation of the set of lncRNA genes. Here, based on ChIP-Seq peak lists of transcription factors (TFs) from ENCODE and annotated human lncRNAs from GENCODE, we developed a web-based interface titled “TF2lncRNA,” where TF peaks from each ChIP-Seq experiment are crossed with the genomic coordinates of a set of input lncRNAs, to identify which TFs present a statistically significant number of binding sites (peaks) within the regulatory region of the input lncRNA genes. The input can be a set of coexpressed lncRNA genes or any other cluster of lncRNA genes. Users can thus infer which TFs are likely to be common transcription regulators of the set of lncRNAs. In addition, users can retrieve all lncRNAs potentially regulated by a specific TF in a specific cell line of interest or retrieve all TFs that have one or more binding sites in the regulatory region of a given lncRNA in the specific cell line. TF2LncRNA is an efficient and easy-to-use web-based tool.

Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression

Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks.Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples.We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer's disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence.

BackgroundMany studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours.ResultsBiclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models.ConclusionsThe increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients.

I-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules

The field of regulatory genomics today is characterized by the generation of high-throughput data sets that capture genome-wide transcription factor (TF) binding, histone modifications, or DNAseI hypersensitive regions across many cell types and conditions. In this context, a critical question is how to make optimal use of these publicly available datasets when studying transcriptional regulation. Here, we address this question in Drosophila melanogaster for which a large number of high-throughput regulatory datasets are available. We developed i-cisTarget (where the ‘i’ stands for integrative), for the first time enabling the discovery of different types of enriched ‘regulatory features’ in a set of co-regulated sequences in one analysis, being either TF motifs or ‘in vivo’ chromatin features, or combinations thereof. We have validated our approach on 15 co-expressed gene sets, 21 ChIP data sets, 628 curated gene sets and multiple individual case studies, and show that meaningful regulatory features can be confidently discovered; that bona fide enhancers can be identified, both by in vivo events and by TF motifs; and that combinations of in vivo events and TF motifs further increase the performance of enhancer prediction.

A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns.

ASCL1-coexpression profiling but not single gene expression profiling defines lung adenocarcinomas of neuroendocrine nature with poor prognosis

BackgroundLung adenocarcinoma is heterogeneous regarding histology, etiology and prognosis. Although there have been several attempts to find a subgroup with poor prognosis, it is unclear whether or not adenocarcinoma with neuroendocrine (NE) nature has unfavorable prognosis. Materials and methodsTo elucidate whether a subtype of adenocarcinoma with NE nature has poor prognosis, we performed gene expression profiling by cDNA microarray for 262 Japanese lung cancer and 30 normal lung samples, including 171 adenocarcinomas, 56 squamous cell carcinomas and 35 NE tumors. A co-expression gene set with ASCL1, an NE master gene, was utilized to classify tumors by non-negative matrix factorization, followed by validation using an ASCL1 knock-down gene set in DMS79 cells as well as an independent cohort (n=139) derived from public microarray databases as a test set. ResultsThe co-expression gene set classified the adenocarcinomas into alveolar cell (AL), squamoid, and NE subtypes. The NE subtype, which clustered together almost all the NE tumors, had significantly poorer prognosis than the AL subtype that clustered with normal lung samples (p=0.0075). The knock-down gene set also classified the 171 adenocarcinomas into three subtypes and this NE subtype also had the poorest prognosis. The co-expression gene set classified the independent database-derived American cohort into two subtypes, with the NE subtype having poorer prognosis. None of the single NE gene expression was found to be linked to survival difference. ConclusionCo-expression gene set with ASCL1, rather than single NE gene expression, successfully identifies an NE subtype of lung adenocarcinoma with poor prognosis.

A mutation degree model for the identification of transcriptional regulatory elements

BackgroundCurrent approaches for identifying transcriptional regulatory elements are mainly via the combination of two properties, the evolutionary conservation and the overrepresentation of functional elements in the promoters of co-regulated genes. Despite the development of many motif detection algorithms, the discovery of conserved motifs in a wide range of phylogenetically related promoters is still a challenge, especially for the short motifs embedded in distantly related gene promoters or very closely related promoters, or in the situation that there are not enough orthologous genes available.ResultsA mutation degree model is proposed and a new word counting method is developed for the identification of transcriptional regulatory elements from a set of co-expressed genes. The new method comprises two parts: 1) identifying overrepresented oligo-nucleotides in promoters of co-expressed genes, 2) estimating the conservation of the oligo-nucleotides in promoters of phylogenetically related genes by the mutation degree model. Compared with the performance of other algorithms, our method shows the advantages of low false positive rate and higher specificity, especially the robustness to noisy data. Applying the method to co-expressed gene sets from Arabidopsis, most of known cis-elements were successfully detected. The tool and example are available at http://mcube.nju.edu.cn/jwang/lab/soft/ocw/OCW.html.ConclusionsThe mutation degree model proposed in this paper is adapted to phylogenetic data of different qualities, and to a wide range of evolutionary distances. The new word-counting method based on this model has the advantage of better performance in detecting short sequence of cis-elements from co-expressed genes of eukaryotes and is robust to less complete phylogenetic data.

Transcriptional Profiling of Diabetic Neuropathy in the BKS db/db Mouse

OBJECTIVEA better understanding of the molecular mechanisms underlying the development and progression of diabetic neuropathy (DN) is essential for the design of mechanism-based therapies. We examined changes in global gene expression to define pathways regulated by diabetes in peripheral nerve.RESEARCH DESIGN AND METHODSMicroarray data for 24-week-old BKS db/db and db/+ mouse sciatic nerve were analyzed to define significantly differentially expressed genes (DEGs); DEGs were further analyzed to identify regulated biological processes and pathways. Expression profile clustering was performed to identify coexpressed DEGs. A set of coexpressed lipid metabolism genes was used for promoter sequence analysis.RESULTSGene expression changes are consistent with structural changes of axonal degeneration. Pathways regulated in the db/db nerve include lipid metabolism, carbohydrate metabolism, energy metabolism, peroxisome proliferator–activated receptor signaling, apoptosis, and axon guidance. Promoter sequences of lipid metabolism–related genes exhibit evidence of coregulation of lipid metabolism and nervous system development genes.CONCLUSIONSOur data support existing hypotheses regarding hyperglycemia-mediated nerve damage in DN. Moreover, our analyses revealed a possible coregulation mechanism connecting hyperlipidemia and axonal degeneration.

COXPRESdb: a database to compare gene coexpression in seven model animals

Publicly available databases of coexpressed gene sets are a valuable resource for a wide variety of experimental studies, including gene targeting for functional identification, and for investigations of regulatory mechanisms or protein–protein interaction networks. Although coexpressed gene databases are becoming more and more popular in the field of plant biology, those with animal data are rather limited, possibly due to the lower reliability of the coexpression data. The original COXPRESdb (coexpressed gene database) (http://coxpresdb.jp) represented the coexpression relationship for human and mouse. Here, we report updates of this database that especially focus on the enhancement of the reliability of gene coexpression data in animals. For this purpose, we implemented a new comparable coexpression measure, Mutual Rank, included five other animal species, rat, chicken, zebrafish, fly and nematoda, to assess the conservation of coexpression, and added different layers of omics data into the integrated network of genes. Comparison of coexpression is a key concept to enhance the reliability of gene coexpression, and the integration of different information can reduce the noise inherent in the information. With the functions for gene network representation, COXPRESdb can help researchers to clarify the functional and regulatory networks of genes in a broad array of animal species.

The odontode explosion: The origin of tooth‐like structures in vertebrates

Essentially we show recent data to shed new light on the thorny controversy of how teeth arose in evolution. Essentially we show (a) how teeth can form equally from any epithelium, be it endoderm, ectoderm or a combination of the two and (b) that the gene expression programs of oral versus pharyngeal teeth are remarkably similar. Classic theories suggest that (i) skin denticles evolved first and odontode-inductive surface ectoderm merged inside the oral cavity to form teeth (the 'outside-in' hypothesis) or that (ii) patterned odontodes evolved first from endoderm deep inside the pharyngeal cavity (the 'inside-out' hypothesis). We propose a new perspective that views odontodes as structures sharing a deep molecular homology, united by sets of co-expressed genes defining a competent thickened epithelium and a collaborative neural crest-derived ectomesenchyme. Simply put, odontodes develop 'inside and out', wherever and whenever these co-expressed gene sets signal to one another. Our perspective complements the classic theories and highlights an agenda for specific experimental manipulations in model and non-model organisms.

The Association of Multiple Interacting Genes with Specific Phenotypes in Rice Using Gene Coexpression Networks

Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes.

A whole genome transcriptional analysis of the early immune response induced by live attenuated and inactivated influenza vaccines in young children

The protective mechanisms of influenza vaccines in young children are not completely understood. A phase 2 clinical study was conducted in 85 children 12–35 months of age to describe and compare the immune responses to live attenuated influenza vaccine (LAIV) with trivalent inactivated influenza vaccine (TIV). To better understand the biology of vaccine effects, oligonucleotide microarrays were employed to measure the genome-wide changes in transcript profiles in whole blood at approximately 7 days after 1 dose of LAIV or TIV. Of the total 265 differentially expressed genes identified in this study, 6 clusters of genes were identified to be tightly coexpressed, many of which are likely modulated by cytokines including type 1 interferons (IFNs) and granulocyte–macrophage colony-stimulating factor. Additional functional analyses revealed that the type 1 IFN pathway and cell cycle regulation-related genes are enriched in the 6 coexpressed gene sets. Promoter characterization of these coexpressed genes also supported this conclusion. Moreover, it is suggested that the IFN-stimulated response element is likely to be a potential bidirectional promoter, and the CCAAT/enhancer-binding protein might cooperate with the E2F transcription factor family in the regulation of the cell cycle in the early immune response induced by the influenza vaccine. Overall, our study clearly indicates that the expression profile changes induced by LAIV are significantly different from those induced by TIV. These results suggest that the pattern of overexpression of type 1 IFN-stimulated genes can potentially be used as a biomarker to identify the early vaccination response of LAIV and may also explain, to a certain extent, previous clinical study observations of LAIV-induced protection against influenza-like illness in the first 2 weeks after administration.

Significance analysis and improved discovery of disease-specific Differentially Co-expressed Gene Sets in microarray data

Kostka and Spang proposed a statistic (KS-statistic) and an algorithm (KS algorithm) to elicit Differentially Co-expressed Gene Sets (DCEGSs) by minimising KS-statistic. We prove that the statistical distributions of KS-statistic under null hypothesis in variance un-normalised and normalised data settings are central and doubly non-central F-distributions, respectively. Based on this analysis, we propose two alternative but equivalent statistics whose null distributions are easier to evaluate. Further, we propose to improve the algorithm by objectively setting the search parameters via maximising the statistical significance of the resultant gene set and pre-filtering the genes by Friendly Neighbours (FNs) algorithm.

An integrative ChIP-chip and gene expression profiling to model SMAD regulatory modules

BackgroundThe TGF-β/SMAD pathway is part of a broader signaling network in which crosstalk between pathways occurs. While the molecular mechanisms of TGF-β/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context.ResultsTo address this question and develop a computational model for SMAD modules, we simultaneously performed chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and mRNA expression profiling to identify TGF-β/SMAD regulated and synchronously coexpressed gene sets in ovarian surface epithelium. Intersecting the ChIP-chip and gene expression data yielded 150 direct targets, of which 141 were grouped into 3 co-expressed gene sets (sustained up-regulated, transient up-regulated and down-regulated), based on their temporal changes in expression after TGF-β activation. We developed a data-mining method driven by the Random Forest algorithm to model SMAD transcriptional modules in the target sequences. The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1).ConclusionTogether, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules.

Identifying set-wise differential co-expression in gene expression microarray data

BackgroundPrevious differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions.ResultsdCoxS is a two-step analysis method. In each condition, dCoxS measures the interaction score (IS), which represents the expression similarity between two gene sets using Renyi relative entropy. When estimating the relative entropy, multivariate kernel density estimation was used to model gene-gene correlation structure. Statistical tests for the conditional difference between the ISs determined the significance of differential coexpression of the gene set pair. Simulation studies supported that the IS is a representative measure of similarity between gene expression matrices. Single gene coexpression analysis of two publicly available microarray datasets detected no significant results. However, the dCoxS analysis of the datasets revealed differentially coexpressed gene set pairs related to the biological conditions of the datasets.ConclusiondCoxS identified differentially coexpressed gene set pairs not found by single gene analysis. The results indicate that set-wise differential coexpression analysis is useful for understanding biological processes induced by conditional changes.

Identifying functional modules using expression profiles and confidence-scored protein interactions

Microarray-based gene expression studies have great potential but are frequently difficult to interpret due to their overwhelming dimensions. Recent studies have shown that the analysis of expression data can be improved by its integration with protein interaction networks, but the performance of these analyses has been hampered by the uneven quality of the interaction data. We present Co-Expression Zone ANalysis using NEtworks (CEZANNE), a novel confidence-based method for extraction of functionally coherent co-expressed gene sets. CEZANNE uses probabilities for individual interactions, which can be computed by any available method. We propose a probabilistic model and a weighting scheme in which the likelihood of the connectivity of a subnetwork is related to the weight of its minimum cut. Applying CEZANNE to an expression dataset of DNA damage response in Saccharomyces cerevisiae, we recover both known and novel modules and predict novel protein functions. We show that CEZANNE outperforms previous methods for analysis of expression and interaction data. CEZANNE is available as part of the MATISSE software at http://acgt.cs.tau.ac.il/matisse. Supplementary data are available at Bioinformatics online.

Gene set-based module discovery in the breast cancer transcriptome

BackgroundAlthough microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data.ResultsIn order to decode oncogenic regulatory programs in cancer cells, we developed a novel module discovery method termed EEM by extending a previously reported module discovery method, and applied it to breast cancer expression data. Starting from seed gene sets prepared based on cis-regulatory elements, ChIP-chip data, and gene locus information, EEM identified 10 principal expression modules in breast cancer based on their expression coherence. Moreover, EEM depicted their activity profiles, which predict regulatory programs in each subtypes of breast tumors. For example, our analysis revealed that the expression module regulated by the Polycomb repressive complex 2 (PRC2) is downregulated in triple negative breast cancers, suggesting similarity of transcriptional programs between stem cells and aggressive breast cancer cells. We also found that the activity of the PRC2 expression module is negatively correlated to the expression of EZH2, a component of PRC2 which belongs to the E2F expression module. E2F-driven EZH2 overexpression may be responsible for the repression of the PRC2 expression modules in triple negative tumors. Furthermore, our network analysis predicts regulatory circuits in breast cancer cells.ConclusionThese results demonstrate that the gene set-based module discovery approach is a powerful tool to decode regulatory programs in cancer cells.

FORCA, a promoter element that responds to crosstalk between defense and light signaling

BackgroundRecognition of pathogenic microorganisms triggers in plants comprehensive transcriptional reprogramming. In order to identify transcriptome-level control elements required for plant immune responses we are examining several sets of genes found by microarray experiments to be co-activated in Arabidopsis thaliana (Arabidopsis) seedlings infected with the oomycete Hyaloperonospora parasitica. Promoter motifs conserved in clusters of co-expressed genes may be involved in mediating coordinated gene activity patterns. Although numerous studies identified such conserved promoter motifs in co-expressed gene sets, reports confirming their function as regulatory elements are rare.ResultsFORCA is a hexameric promoter motif that is conserved in clusters of Arabidopsis genes co-expressed in response to fungal or oomycete pathogens as well as defined light treatments. FORCA is generally more frequently present in Arabidopsis promoter regions than statistically expected. It constitutively interacts in a DNA-sequence specific manner with nuclear Arabidopsis proteins. These interactions are suppressed by defense-related stimuli and enhanced by prolonged exposure to constant light. Furthermore FORCA mediates constitutive reporter gene expression in transiently transformed Nicotiana benthamiana leaves as well as in stably transformed Arabidopsis plants. Its responsiveness to defense-stimuli is modulated by the duration of light exposure. In plants grown under normal light conditions or constant darkness defense-related stimuli result in suppression of FORCA-mediated reporter gene expression, while in plants grown under constant light exposure, defense-induction results in enhanced FORCA-mediated expression. In addition, we found plants subjected to constant light exposure to exhibit reduced susceptibility to virulent H. parasitica.ConclusionWe propose that FORCA is a regulatory cis-element that is present in a wide variety of Arabidopsis promoters. It integrates light- and defense-related signals and participates in adjusting the transcriptome to changes in environmental conditions.

COXPRESdb: a database of coexpressed gene networks in mammals

A database of coexpressed gene sets can provide valuable information for a wide variety of experimental designs, such as targeting of genes for functional identification, gene regulation and/or protein–protein interactions. Coexpressed gene databases derived from publicly available GeneChip data are widely used in Arabidopsis research, but platforms that examine coexpression for higher mammals are rather limited. Therefore, we have constructed a new database, COXPRESdb (coexpressed gene database) (http://coxpresdb.hgc.jp), for coexpressed gene lists and networks in human and mouse. Coexpression data could be calculated for 19 777 and 21 036 genes in human and mouse, respectively, by using the GeneChip data in NCBI GEO. COXPRESdb enables analysis of the four types of coexpression networks: (i) highly coexpressed genes for every gene, (ii) genes with the same GO annotation, (iii) genes expressed in the same tissue and (iv) user-defined gene sets. When the networks became too big for the static picture on the web in GO networks or in tissue networks, we used Google Maps API to visualize them interactively. COXPRESdb also provides a view to compare the human and mouse coexpression patterns to estimate the conservation between the two species.

ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis

Publicly available database of co-expressed gene sets would be a valuable tool for a wide variety of experimental designs, including targeting of genes for functional identification or for regulatory investigation. Here, we report the construction of an Arabidopsis thaliana trans-factor and cis-element prediction database (ATTED-II) that provides co-regulated gene relationships based on co-expressed genes deduced from microarray data and the predicted cis elements. ATTED-II () includes the following features: (i) lists and networks of co-expressed genes calculated from 58 publicly available experimental series, which are composed of 1388 GeneChip data in A.thaliana; (ii) prediction of cis-regulatory elements in the 200 bp region upstream of the transcription start site to predict co-regulated genes amongst the co-expressed genes; and (iii) visual representation of expression patterns for individual genes. ATTED-II can thus help researchers to clarify the function and regulation of particular genes and gene networks.