Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana.

Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.

Single nucleotide polymorphisms associated with colorectal cancer susceptibility and loss of heterozygosity in a Taiwanese population.

Given the significant racial and ethnic diversity in genetic variation, we are intrigued to find out whether the single nucleotide polymorphisms (SNPs) identified in genome-wide association studies of colorectal cancer (CRC) susceptibility in East Asian populations are also relevant to the population of Taiwan. Moreover, loss of heterozygosity (LOH) may provide insight into how variants alter CRC risk and how regulatory elements control gene expression. To investigate the racial and ethnic diversity of CRC-susceptibility genetic variants and their relevance to the Taiwanese population, we genotyped 705 CRC cases and 1,802 healthy controls (Taiwan Biobank) for fifteen previously reported East Asian CRC-susceptibility SNPs and four novel genetic variants identified by whole-exome sequencing. We found that rs10795668 in FLJ3802842 and rs4631962 in CCND2 were significantly associated with CRC risk in the Taiwanese population. The previously unreported rs1338565 was associated with a significant increased risk of CRC. In addition, we also genotyped tumor tissue and paired adjacent normal tissues of these 705 CRC cases to search for LOH, as well as risk-associated and protective alleles. LOH analysis revealed preferential retention of three SNPs, rs12657484, rs3802842, and rs4444235, in tumor tissues. rs4444235 has been recently reported to be a cis-acting regulator of BMP4 gene; in this study, the C allele was preferentially retained in tumor tissues (p = 0.0023). rs4631962 and rs10795668 contribute to CRC risk in the Taiwanese and East Asian populations, and the newly identified rs1338565 was specifically associated with CRC, supporting the ethnic diversity of CRC-susceptibility SNPs. LOH analysis suggested that the three CRC risk variants, rs12657484, rs3802842, and rs4444235, exhibited somatic allele-specific imbalance and might be critical during neoplastic progression.

The C-terminus of histone H2B is involved in chromatin compaction specifically at telomeres, independently of its monoubiquitylation at lysine 123.

Telomeric heterochromatin assembly in budding yeast propagates through the association of Silent Information Regulator (SIR) proteins with nucleosomes, and the nucleosome array has been assumed to fold into a compacted structure. It is believed that the level of compaction and gene repression within heterochromatic regions can be modulated by histone modifications, such as acetylation of H3 lysine 56 and H4 lysine 16, and monoubiquitylation of H2B lysine 123. However, it remains unclear as to whether or not gene silencing is a direct consequence of the compaction of chromatin. Here, by investigating the role of the carboxy-terminus of histone H2B in heterochromatin formation, we identify that the disorderly compaction of chromatin induced by a mutation at H2B T122 specifically hinders telomeric heterochromatin formation. H2B T122 is positioned within the highly conserved AVTKY motif of the αC helix of H2B. Heterochromatin containing the T122E substitution in H2B remains inaccessible to ectopic dam methylase with dramatically increased mobility in sucrose gradients, indicating a compacted chromatin structure. Genetic studies indicate that this unique phenotype is independent of H2B K123 ubiquitylation and Sir4. In addition, using ChIP analysis, we demonstrate that telomere structure in the mutant is further disrupted by a defect in Sir2/Sir3 binding and the resulting invasion of euchromatic histone marks. Thus, we have revealed that the compaction of chromatin per se is not sufficient for heterochromatin formation. Instead, these results suggest that an appropriately arrayed chromatin mediated by H2B C-terminus is required for SIR binding and the subsequent formation of telomeric chromatin in yeast, thereby identifying an intrinsic property of the nucleosome that is required for the establishment of telomeric heterochromatin. This requirement is also likely to exist in higher eukaryotes, as the AVTKY motif of H2B is evolutionarily conserved.

A novel regulatory event-based gene set analysis method for exploring global functional changes in heterogeneous genomic data sets.

BACKGROUND: Analyzing gene expression data by assessing the significance of pre-defined gene sets, rather than individual genes, has become a main approach in microarray data analysis and this has promisingly derive new biological interpretations of microarray data. However, the detection power of conventional gene list or gene set-based approaches is limited on highly heterogeneous samples, such as tumors. RESULTS: We developed a novel method, the regulatory event-based Gene Set Analysis (eGSA), which considers not only the consistently changed genes but also every gene regulation (event) of each sample to overcome the detection limit. In comparison with conventional methods, eGSA can detect functional changes in heterogeneous samples more precisely and robustly. Furthermore, by utilizing eGSA, we successfully revealed novel functional characteristics and potential mechanisms of very early hepatocellular carcinoma (HCC). CONCLUSION: Our study creates a novel scheme to directly target the major cellular functional changes in heterogeneous samples. All potential regulatory routines of a functional change can be further analyzed by the regulatory event frequency. We also provide a case study on early HCCs and reveal a novel insight at the initial stage of hepatocarcinogenesis. eGSA therefore accelerates and refines the interpretation of heterogeneous genomic data sets in the absence of gene-phenotype correlations.

easyExon--a Java-based GUI tool for processing and visualization of Affymetrix exon array data.

BACKGROUND: Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms. RESULTS: In this study we present a Java-based freeware, easyExon http://microarray.ym.edu.tw/easyexon, to process, filtrate and visualize exon array data with an analysis pipeline. This tool implements the most commonly used probeset summarization methods as well as AS-orientated filtration algorithms, e.g. MIDAS and PAC, for the detection of alternative splicing events. We include a biological filtration function according to GO terms, and provide a module to visualize and interpret the selected exons and transcripts. Furthermore, easyExon can integrate with other related programs, such as Integrate Genome Browser (IGB) and Affymetrix Power Tools (APT), to make the whole analysis more comprehensive. We applied easyExon on a public accessible colon cancer dataset as an example to illustrate the analysis pipeline of this tool. CONCLUSION: EasyExon can efficiently process and analyze the Affymetrix exon array data. The simplicity, flexibility and brevity of easyExon make it a valuable tool for AS event identification in genomic research.

A pattern recognition approach to infer time-lagged genetic interactions.

MOTIVATION: For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass. RESULTS: PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73% (77%), 46% (51%), and 52% (59%), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10% (10 and 14%), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted. AVAILABILITY: Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/~gshieh/pare.htm.

Functional network reconstruction reveals somatic stemness genetic maps and dedifferentiation-like transcriptome reprogramming induced by GATA2.

Somatic stem cell transplantation holds great promise in regenerative medicine. The best-characterized adult stem cells are mesenchymal stem cells (MSCs), neural stem cells (NSCs), and CD133(+) hematopoietic stem cells (HSCs). The applications of HSCs are hampered since these cells are difficult to maintain in an undifferentiated state in vitro. Understanding genes responsible for stem cell properties and their interactions will help on this issue. The construction of stem cell genetic networks will also help to develop rational strategies to revert somatic cells back to a stem-like state. We performed a systemic study on human CD133(+) HSCs, NSCs, MSCs, and embryonic stem cells and two different progenies of CD133(+) HSCs, microvascular endothelial cells (MVECs) and peripheral blood mononuclear cells. Genes abundant in each or in all three somatic stem cells were identified. We also observed complex genetic networks functioning in postnatal stem cells, in which several genes, such as PTPN11 and DHFR, acted as hubs to maintain the stability and connectivity of the whole genetic network. Eighty-seven HSC genes, including ANGPT1 and GATA2, were independently identified by comparing CD34(+)CD33(-)CD38(-) hematopoietic stem cells with CD34(+) precursors and various matured progenies. Introducing GATA2 into MVECs resulted in dedifferentiation-like transcriptome reprogramming, with HSC genes (such as ANGPT1) being up and endothelial genes (such as EPHB2) being down. This study provides a foundation for a more detailed understanding of human somatic stem cells. Expressing the newly discovered stem cell genes in matured cells might lead to a global reversion of somatic transcriptome to a stem-like status.

Signature Evaluation Tool (SET): a Java-based tool to evaluate and visualize the sample discrimination abilities of gene expression signatures.

BACKGROUND: The identification of specific gene expression signature for distinguishing sample groups is a dominant field in cancer research. Although a number of tools have been developed to identify optimal gene expression signatures, the number of signature genes obtained is often overly large to be applied clinically. Furthermore, experimental verification is sometimes limited by the availability of wet-lab materials such as antibodies and reagents. A tool to evaluate the discrimination power of candidate genes is therefore in high demand by clinical researchers. RESULTS: Signature Evaluation Tool (SET) is a Java-based tool adopting the Golub's weighted voting algorithm as well as incorporating the visual presentation of prediction strength for each array sample. SET provides a flexible and easy-to-follow platform to evaluate the discrimination power of a gene signature. Here, we demonstrated the application of SET for several purposes: (1) for signatures consisting of a large number of genes, SET offers the ability to rapidly narrow down the number of genes; (2) for a given signature (from third party analyses or user-defined), SET can re-evaluate and re-adjust its discrimination power by selecting/de-selecting genes repeatedly; (3) for multiple microarray datasets, SET can evaluate the classification capability of a signature among datasets; and (4) by providing a module to visualize the prediction strength for each sample, SET allows users to re-evaluate the discrimination power on mis-grouped or less-certain samples. Information obtained from the above applications could be useful in prognostic analyses or clinical management decisions. CONCLUSION: Here we present SET to evaluate and visualize the sample-discrimination ability of a given gene expression signature. This tool provides a filtration function for signature identification and lies between clinical analyses and class prediction (or feature selection) tools. The simplicity, flexibility and brevity of SET could make it an invaluable tool for marker identification in clinical research.

Conservation, convergence, and divergence of light-responsive, circadian-regulated, and tissue-specific expression patterns during evolution of the Arabidopsis GATA gene family.

In vitro analyses of plant GATA transcription factors have implicated some proteins in light-mediated and circadian-regulated gene expression, and, more recently, the analysis of mutants has uncovered further diverse roles for plant GATA factors. To facilitate function discovery for the 29 GATA genes in Arabidopsis (Arabidopsis thaliana), we have experimentally verified gene structures and determined expression patterns of all family members across adult tissues and suspension cell cultures, as well as in response to light and signals from the circadian clock. These analyses have identified two genes that are strongly developmentally light regulated, expressed predominantly in photosynthetic tissue, and with transcript abundance peaking before dawn. In contrast, several GATA factor genes are light down-regulated. The products of these light-regulated genes are candidates for those proteins previously implicated in light-regulated transcription. Coexpression of these genes with well-characterized light-responsive transcripts across a large microarray data set supports these predictions. Other genes show additional tissue-specific expression patterns suggesting novel and unpredicted roles. Genome-wide analysis using coexpression scatter plots for paralogous gene pairs reveals unexpected differences in cocorrelated gene expression profiles. Clustering the Arabidopsis GATA factor gene family by similarity of expression patterns reveals that genes of recent descent do not uniformly show conserved current expression profiles, yet some genes showing more distant evolutionary origins have acquired common expression patterns. In addition to defining developmental and environmental dynamics of GATA transcript abundance, these analyses offer new insights into the evolution of gene expression profiles following gene duplication events.

Arabidopsis Co-expression Tool (ACT): web server tools for microarray-based gene expression analysis.

The Arabidopsis Co-expression Tool, ACT, ranks the genes across a large microarray dataset according to how closely their expression follows the expression of a query gene. A database stores pre-calculated co-expression results for approximately 21,800 genes based on data from over 300 arrays. These results can be corroborated by calculation of co-expression results for user-defined sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique Finder (CF) identifies groups of genes which are consistently co-expressed with each other across a user-defined co-expression list. The parameters can be altered easily to adjust cluster size and the output examined for optimal inclusion of genes with known biological roles. Alternatively, a Scatter Plot tool displays the correlation coefficients for all genes against two user-selected queries on a scatter plot which can be useful for visual identification of clusters of genes with similar r-values. User-input groups of genes can be highlighted on the scatter plots. Inclusion of genes with known biology in sets of genes identified using CF and Scatter Plot tools allows inferences to be made about the roles of the other genes in the set and both tools can therefore be used to generate short lists of genes for further characterization. ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT.

The Arabidopsis co-expression tool (ACT): a WWW-based tool and database for microarray-based gene expression analysis.

We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel clique finder tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/

Natural antisense transcripts with coding capacity in Arabidopsis may have a regulatory role that is not linked to double-stranded RNA degradation.

BACKGROUND: Overlapping transcripts in antisense orientation have the potential to form double-stranded RNA (dsRNA), a substrate for a number of different RNA-modification pathways. One prominent route for dsRNA is its breakdown by Dicer enzyme complexes into small RNAs, a pathway that is widely exploited by RNA interference technology to inactivate defined genes in transgenic lines. The significance of this pathway for endogenous gene regulation remains unclear. RESULTS: We have examined transcription data for overlapping gene pairs in Arabidopsis thaliana. On the basis of an analysis of transcripts with coding regions, we find the majority of overlapping gene pairs to be convergently overlapping pairs (COPs), with the potential for dsRNA formation. In all tissues, COP transcripts are present at a higher frequency compared to the overall gene pool. The probability that both the sense and antisense copy of a COP are co-transcribed matches the theoretical value for coexpression under the assumption that the expression of one partner does not affect the expression of the other. Among COPs, we observe an over-representation of spliced (intron-containing) genes (90%) and of genes with alternatively spliced transcripts. For loci where antisense transcripts overlap with sense transcript introns, we also find a significant bias in favor of alternative splicing and variation of polyadenylation. CONCLUSION: The results argue against a predominant RNA degradation effect induced by dsRNA formation. Instead, our data support alternative roles for dsRNAs. They suggest that at least for a subgroup of COPs, antisense expression may induce alternative splicing or polyadenylation.

Application of SVM to predict membrane protein types.

As a continuous effort to develop automated methods for predicting membrane protein types that was initiated by Chou and Elrod (PROTEINS: Structure, Function, and Genetics, 1999, 34, 137-153), the support vector machine (SVM) is introduced. Results obtained through re-substitution, jackknife, and independent data set tests, respectively, have indicated that the SVM approach is quite a promising one, suggesting that the covariant discriminant algorithm (Chou and Elrod, Protein Eng. 12 (1999) 107) and SVM, if effectively complemented with each other, will become a powerful tool for predicting membrane protein types and the other protein attributes as well.