Genome-wide Gene Expression Data Research Articles

Comparison of Pattern Detection Methods in Microarray Time Series of the Segmentation Clock

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.

Impact of Medicare Cutbacks In DXA Reimbursement on Care of Patients At Risk For Fracture

Loss-of-function mutations in SGCE, which encodes ε-sarcoglycan (ε-SG), cause myoclonus-dystonia syndrome (OMIM159900, DYT11). A “major” ε-SG protein derived from CCDS5637.1 (NM_003919.2) and a “brain-specific” protein, that includes sequence derived from alternative exon 11b (CCDS47642.1, NM_001099400.1), are reportedly localized in post- and pre-synaptic membrane fractions, respectively. Moreover, deficiency of the “brain-specific” isoform and other isoforms derived from exon 11b may be central to the pathogenesis of DYT11. However, no animal model supports this hypothesis. Gene-trapped ES cells (CMHD-GT_148G1-3, intron 9 of NM_011360) were used to generate a novel Sgce mouse model (C57BL/6J background) with markedly reduced expression of isoforms derived from exons 3′ to exon 9 of NM_011360. Among those brain regions analyzed in adult (2 month-old) wild-type (WT) mice, cerebellum showed the highest relative expression of isoforms incorporating exon 11b. Homozygotes (SgceGt(148G1)Cmhd/Gt(148G1)Cmhd or SgceGt/Gt) and paternal heterozygotes (Sgcem+/pGt, m-maternal, p-paternal) showed 60 to 70% reductions in expression of total Sgce. Although expression of the major (NM_011360) and brain-specific (NM_001130189) isoforms was markedly reduced, expression of short isoforms was preserved and relatively small amounts of chimeric ε-SG/β-galactosidase fusion protein was produced by the Sgce gene-trap locus. Immunoaffinity purification followed by mass spectrometry assessments of Sgcem+/pGt mouse brain using pan- or brain-specific ε-SG antibodies revealed significant reductions of ε-SG and other interacting sarcoglycans. Genome-wide gene-expression data using RNA derived from adult Sgcem+/pGt mouse cerebellum showed that the top up-regulated genes were involved in cell cycle, cellular development, cell death and survival, while the top down-regulated genes were associated with protein synthesis, cellular development, and cell death and survival. In comparison to WT littermates, Sgcem+/pGt mice exhibited “tiptoe” gait and stimulus-induced appendicular posturing between Postnatal Days 14 to 16. Abnormalities noted in older Sgcem+/pGt mice included reduced body weight, altered gait dynamics, and reduced open-field activity. Overt spontaneous or stimulus-sensitive myoclonus was not apparent on the C57BL/6J background or mixed C57BL/6J-BALB/c and C57BL/6J-129S2 backgrounds. Our data confirm that mouse Sgce is a maternally imprinted gene and suggests that short Sgce isoforms may compensate, in part, for deficiency of major and brain-specific Sgce isoforms.

Genomics and genome-wide association studies: An integrative approach to expression QTL mapping

Expression QTL mapping by integrating genome-wide gene expression and genotype data is a promising approach to identifying functional genetic variation, but is hampered by the large number of multiple comparisons inherent in such studies. A novel approach to addressing multiple testing problems in genome-wide family-based association studies is screening candidate markers using heritability or conditional power. We apply these methods in settings in which microarray gene expression data are used as phenotypes, screening for SNPs near the expressed genes. We perform association analyses for phenotypes using a univariate approach. We also perform simulations on trios with large numbers of causal SNPs to determine the optimal number of markers to use in a screen. We demonstrate that our family-based screening approach performs well in the analysis of integrative genomic datasets and that screening using either heritability or conditional power produces similar, though not identical, results.

Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells.

BackgroundMicroarray experiments enable simultaneous measurement of the expression levels of virtually all transcripts present in cells, thereby providing a ‘molecular picture’ of the cell state. On the other hand, the genomic responses to a pharmacological or hormonal stimulus are dynamic molecular processes, where time influences gene activity and expression. The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal relationships between samples.ResultsWe compared here four different methods to analyze data derived from a time course mRNA expression profiling experiment which consisted in the study of the effects of estrogen on hormone-responsive human breast cancer cells. Gene expression was monitored with the innovative Illumina BeadArray platform, which includes an average of 30-40 replicates for each probe sequence randomly distributed on the chip surface. We present and discuss the results obtained by applying to these datasets different statistical methods for serial gene expression analysis. The influence of the normalization algorithm applied on data and of different parameter or threshold choices for the selection of differentially expressed transcripts has also been evaluated. In most cases, the selection was found fairly robust with respect to changes in parameters and type of normalization. We then identified which genes showed an expression profile significantly affected by the hormonal treatment over time. The final list of differentially expressed genes underwent cluster analysis of functional type, to identify groups of genes with similar regulation dynamics.ConclusionsSeveral methods for processing time series gene expression data are presented, including evaluation of benefits and drawbacks of the different methods applied. The resulting protocol for data analysis was applied to characterization of the gene expression changes induced by estrogen in human breast cancer ZR-75.1 cells over an entire cell cycle.

SNPExpress: integrated visualization of genome-wide genotypes, copy numbers and gene expression levels

BackgroundAccurate analyses of comprehensive genome-wide SNP genotyping and gene expression data sets is challenging for many researchers. In fact, obtaining an integrated view of both large scale SNP genotyping and gene expression is currently complicated since only a limited number of appropriate software tools are available.ResultsWe present SNPExpress, a software tool to accurately analyze Affymetrix and Illumina SNP genotype calls, copy numbers, polymorphic copy number variations (CNVs) and Affymetrix gene expression in a combinatorial and efficient way. In addition, SNPExpress allows concurrent interpretation of these items with Hidden-Markov Model (HMM) inferred Loss-of-Heterozygosity (LOH)- and copy number regions.ConclusionThe combined analyses with the easily accessible software tool SNPExpress will not only facilitate the recognition of recurrent genetic lesions, but also the identification of critical pathogenic genes.

Pharmacogenomics of Cardiovascular Pharmacology: Development of an Informatics System for Analysis of DNA Microarray Data With a Focus on Lipid Metabolism

Genome-wide gene-expression data from DNA-microarray technology and molecular-network data from computational text-mining have led to a paradigm shift in biological research. However, interpretation of the huge amount of data is a bottleneck. We have developed an informatics system, which we refer to as bioSpace Explorer, that can extract pathways and molecules of interest from genome-wide data and show the mutual relationships among these pathways and molecules. Differentiation of 3T3-L1 cells into adipocytes and the action of a peroxisome proliferator-activated receptor gamma (PPARγ) agonist or α-linolenic acid on this process was analyzed with bioSpace Explorer. The results suggested a biological basis for adipocyte differentiation and a strategy to enhance lipid oxidation in adipocytes. Clustered changes of molecules were apparent in the insulin, Wnt, and PPARγ signaling pathways and in the lipogenesis, lipid oxidation, and lipid transport pathways during cell differentiation. A PPARγ agonist enhanced lipid oxidation in adipocytes and α-linolenic acid gave similar results to the PPARγ agonist. An analysis of sex hormone and thyroid hormone, in addition to PPARγ signaling, suggested that these molecules are important for enhancement of lipid oxidation in adipocytes. The results indicate the utility of bioSpace Explorer for biological research on genome-wide molecular networks.

Comparative Analysis of Selenocysteine Machinery and Selenoproteome Gene Expression in Mouse Brain Identifies Neurons as Key Functional Sites of Selenium in Mammals

Although dietary selenium (Se) deficiency results in phenotypes associated with selenoprotein depletion in various organs, the brain is protected from Se loss. To address the basis for the critical role of Se in brain function, we carried out comparative gene expression analyses for the complete selenoproteome and associated biosynthetic factors. Using the Allen Brain Atlas, we evaluated 159 regions of adult mouse brain and provided experimental analyses of selected selenoproteins. All 24 selenoprotein mRNAs were expressed in the mouse brain. Most strikingly, neurons in olfactory bulb, hippocampus, cerebral cortex, and cerebellar cortex were exceptionally rich in selenoprotein gene expression, in particular in GPx4, SelK, SelM, SelW, and Sep15. Over half of the selenoprotein genes were also expressed in the choroid plexus. A unique expression pattern was observed for one of the highly expressed selenoprotein genes, SelP, which we suggest to provide neurons with Se. Cluster analysis of the expression data linked certain selenoproteins and selenocysteine machinery genes and suggested functional linkages among selenoproteins, such as that between SelM and Sep15. Overall, this study suggests that the main functions of selenium in mammals are confined to certain neurons in the brain.

DUX4 , a candidate gene of facioscapulohumeral muscular dystrophy, encodes a transcriptional activator of PITX1

Facioscapulohumeral muscular dystrophy (FSHD) is an autosomal dominant disorder linked to contractions of the D4Z4 repeat array in the subtelomeric region of chromosome 4q. By comparing genome-wide gene expression data from muscle biopsies of patients with FSHD to those of 11 other neuromuscular disorders, paired-like homeodomain transcription factor 1 (PITX1) was found specifically up-regulated in patients with FSHD. In addition, we showed that the double homeobox 4 gene (DUX4) that maps within the D4Z4 repeat unit was up-regulated in patient myoblasts at both mRNA and protein level. We further showed that the DUX4 protein could activate transient expression of a luciferase reporter gene fused to the Pitx1 promoter as well as the endogenous Pitx1 gene in transfected C2C12 cells. In EMSAs, DUX4 specifically interacted with a 30-bp sequence 5'-CGGATGCTGTCTTCTAATTAGTTTGGACCC-3' in the Pitx1 promoter. Mutations of the TAAT core affected Pitx1-LUC activation in C2C12 cells and DUX4 binding in vitro. Our results suggest that up-regulation of both DUX4 and PITX1 in FSHD muscles may play critical roles in the molecular mechanisms of the disease.

Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer

In cancer, chromosomal imbalances like amplifications and deletions, or changes in epigenetic mechanisms like DNA methylation influence the transcriptional activity. These alterations are often not limited to a single gene but affect several genes of the genomic region and may be relevant for the disease status. For example, the ERBB2 amplicon (17q21) in breast cancer is associated with poor patient prognosis. We present a general, unsupervised method for genome-wide gene expression data to systematically detect tumor patients with chromosomal regions of distinct transcriptional activity. The method aims to find expression patterns of adjacent genes with a consistently decreased or increased level of gene expression in tumor samples. Such patterns have been found to be associated with chromosomal aberrations and clinical parameters like tumor grading and thus can be useful for risk stratification or therapy. Our approach was applied to 12 independent human breast cancer microarray studies comprising 1422 tumor samples. We prioritized chromosomal regions and genes predominantly found across all studies. The result highlighted not only regions which are well known to be amplified like 17q21 and 11q13, but also others like 8q24 (distal to MYC) and 17q24-q25 which may harbor novel putative oncogenes. Since our approach can be applied to any microarray study it may become a valuable tool for the exploration of transcriptional changes in diverse disease types. The R source codes which implement the method and an exemplary analysis are available at http://www.dkfz.de/mga2/people/buness/CTP/. Supplementary data are available at Bioinformatics online.

Incorporating prior information via shrinkage: a combined analysis of genome‐wide location data and gene expression data

Transcriptional control is a critical step in regulation of gene expression. Understanding such a control on a genomic level involves deciphering the mechanisms and structures of regulatory programmes and networks. A difficulty arises due to the weak signal and high noise in various sources of data while most current approaches are limited to analysis of a single source of data. A natural alternative is to improve statistical efficiency and power by a combined analysis of multiple sources of data. Here we propose a shrinkage method to combine genome-wide location data and gene expression data to detect the binding sites or target genes of a transcription factor. Specifically, a prior 'non-target' gene list is generated by analysing the expression data, and then this information is incorporated into the subsequent binding data analysis via a shrinkage method. There is a Bayesian justification for this shrinkage method. Both simulated and real data were used to evaluate the proposed method and compare it with analysing binding data alone. In simulation studies, the proposed method gives higher sensitivity and lower false discovery rate (FDR) in detecting the target genes. In real data example, the proposed method can reduce the estimated FDR and increase the power to detect the previously known target genes of a broad transcription regulator, leucine responsive regulatory protein (Lrp) in Escherichia coli. This method can also be used to incorporate other information, such as gene ontology (GO), to microarray data analysis to detect differentially expressed genes.

Leukemic Transformation of Primary Human CD34+ Progenitor Cells by MLL-AF9 Recapitulates the Phenotype and Gene Expression Profile of MLL Rearranged AML.

The t(9;11)(p22;q23) results in the creation of an MLL-AF9 fusion gene and is observed in 27% of 11q23 leukemias. t(9;11) is commonly associated with an M5 monocytic leukemia and patients harboring this translocation are included in the intermediate or poor cytogenetic risk groups, with an average 42% overall survival at five years. Using primary human hematopoietic progenitor cells, we have developed a system that closely mimics the MLL-AF9 associated disease observed in human acute myeloid leukemia (AML). Injection of MLL-AF9 expressing cells into sublethally-irradiated NOD/SCID mice produced an aggressive FAB-M5-like leukemia that infiltrated the spleen and liver and induced death with an average latency of 8 weeks. Serial transplantation of leukemic cells recapitulated the disease with a similar latency. MLL-AF9 expression conferred the ability to grow indefinitely in culture, promoted an accumulation of cells resembling monoblasts and was associated with an increase in telomerase activity. Mono- or oligoclonal outgrowth arose in vitro as well as in vivo. Numeric karyotypic changes or normal karyotypes, in the absence of structural rearrangements, additions or deletions were detected in cultured cells as well as samples from leukemic mice. Principal Component Analysis of genome-wide gene expression data from MLL-AF9 expressing cultured cells revealed a gene expression signature that closely mirrors that observed in MLL-rearranged leukemic cells from patient samples. Furthermore, training of a (linear) Support Vector Machine with gene expression data from patient samples resulted in the correct classification of all MLL-AF9-expressing cell cultures as MLL-rearranged leukemia. We conclude that expression of MLL-AF9 in a normal human hematopoietic progenitor is sufficient to recapitulate many key aspects of the clinical disease. This model will yield valuable insights into the molecular pathogenesis of MLL fusion driven leukemia and will serve as a powerful in vivo model for testing much needed novel therapeutic targets.

A Modular Analysis of Breast Cancer Reveals a Novel Low-Grade Molecular Signature in Estrogen Receptor–Positive Tumors

Previous reports using genome-wide gene expression data to classify breast tumors have typically used standard unsupervised or supervised techniques, both of which have known limitations. We hypothesized that novel clinically relevant information could be revealed in these data sets by an alternative analytic approach. Using a recently described algorithm, signature analysis (SA), we identified "modules," comprising groups of tightly coexpressed genes that are conditionally linked to particular tumors, in a series of breast tumor gene expression profiles. The SA successfully identified multiple breast cancer modules specifically linked to distinct biological functions. We identified a novel module, TuM1, whose presence was not readily discernible by conventional clustering techniques. The TuM1 module is expressed in a subset of estrogen receptor (ER)-positive tumors and is significantly enriched with genes involved in apoptosis and cell death. Clinically, TuM1-expressing tumors are associated with low histopathologic grade, and this association is independent of the inherent ER status of a tumor. We confirmed the robustness and general applicability of TuM1 module by demonstrating its association with low tumor grade in multiple independent breast cancer data sets generated using different array technologies. In vitro, the TuM1 module is down-regulated in ER+ MCF7 cells upon treatment with tamoxifen, suggesting that TuM1 expression may be dependent on active signaling by ER. Initial data is also suggestive that TuM1 expression may be clinically associated with a patient's response to antihormonal therapy. Our results suggest that modular-based approaches toward gene expression data can prove useful in identifying novel, robust, and biologically relevant signatures even from data sets that have been the subject of substantial prior analysis.

Integrative genetic analysis of transcription modules: towards filling the gap between genetic loci and inherited traits

Genetic loci that regulate inherited traits are routinely identified using quantitative trait locus (QTL) mapping methods. However, the genotype-phenotype associations do not provide information on the gene expression program through which the genetic loci regulate the traits. Transcription modules are 'self-consistent regulatory units' and are closely related to the modular components of gene regulatory network [Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y. and Barkai, N. (2002) Revealing modular organization in the yeast transcriptional network. Nat. Genet., 31, 370-377; Segal, E., Shapira, M., Regev, A., Pe'er, D., Botstein, D., Koller, D. and Friedman, N. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet., 34, 166-176]. We used genome-wide genotype and gene expression data of a genetic reference population that consists of mice of 32 recombinant inbred strains to identify the transcription modules and the genetic loci regulating them. Twenty-nine transcription modules defined by genetic variations were identified. Statistically significant associations between the transcription modules and 18 classical physiological and behavioral traits were found. Genome-wide interval mapping showed that major QTLs regulating the transcription modules are often co-localized with the QTLs regulating the associated classical traits. The association and the possible co-regulation of the classical trait and transcription module indicate that the transcription module may be involved in the gene pathways connecting the QTL and the classical trait. Our results show that a transcription module may associate with multiple seemingly unrelated classical traits and a classical trait may associate with different modules. Literature mining results provided strong independent evidences for the relations among genes of the transcription modules, genes in the regions of the QTLs regulating the transcription modules and the keywords representing the classical traits.

Evolutionary Conservation of Expression Profiles Between Human and Mouse Orthologous Genes

Mouse models are often used to study human genes because it is believed that the expression and function are similar for the majority of orthologous genes between the two species. However, recent comparisons of microarray data from thousands of orthologous human and mouse genes suggested rapid evolution of gene expression profiles under minimal or no selective constraint. These findings appear to contradict non-array-based observations from many individual genes and imply the uselessness of mouse models for studying human genes. Because absolute levels of gene expression are not comparable between species when the data are generated by species-specific microarrays, use of relative mRNA abundance among tissues (RA) is preferred to that of absolute expression signals. We thus reanalyze human and mouse genome-wide gene expression data generated by oligonucleotide microarrays. We show that the mean correlation coefficient among expression profiles detected by different probe sets of the same gene is only 0.38 for humans and 0.28 for mice, indicating that current measures of expression divergence are flawed because the large estimation error (discrepancy in expression signal detected by different probe sets of the same gene) is mistakenly included in the between-species divergence. When this error is subtracted, 84% of human-mouse orthologous gene pairs show significantly lower expression divergence than that of random gene pairs. In contrast to a previous finding, but consistent with the common sense, expression profiles of orthologous tissues between species are more similar to each other than to those of nonorthologous tissues. Furthermore, the evolutionary rate of expression divergence and that of coding sequence divergence are found to be weakly, but significantly positively correlated, when RA and the Euclidean distance are used to measure expression-profile divergence. These results highlight the importance of proper consideration of various estimation errors in comparing the microarray data between species.

Probabilistic model of the human protein-protein interaction network

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

Regulatory motif finding by logic regression

Multiple transcription factors coordinately control transcriptional regulation of genes in eukaryotes. Although many computational methods consider the identification of individual transcription factor binding sites (TFBSs), very few focus on the interactions between these sites. We consider finding TFBSs and their context specific interactions using microarray gene expression data. We devise a hybrid approach called LogicMotif composed of a TFBS identification method combined with the new regression methodology logic regression. LogicMotif has two steps: First, potential binding sites are identified from transcription control regions of genes of interest. Various available methods can be used in this step when the genes of interest can be divided into groups such as up-and downregulated. For this step, we also develop a simple univariate regression and extension method MFURE to extract candidate TFBSs from a large number of genes in the availability of microarray gene expression data. MFURE provides an alternative method for this step when partitioning of the genes into disjoint groups is not preferred. This first step aims to identify individual sites within gene groups of interest or sites that are correlated with the gene expression outcome. In the second step, logic regression is used to build a predictive model of outcome of interest (either gene expression or up- and down-regulation) using these potential sites. This 2-fold approach creates a rich diverse set of potential binding sites in the first step and builds regression or classification models in the second step using logic regression that is particularly good at identifying complex interactions. LogicMotif is applied to two publicly available datasets. A genome-wide gene expression data set of Saccharomyces cerevisiae is used for validation. The regression models obtained are interpretable and the biological implications are in agreement with the known resuts. This analysis suggests that LogicMotif provides biologically more reasonable regression models than previous analysis of this dataset with standard linear regression methods. Another dataset of S.cerevisiae illustrates the use of LogicMotif in classification questions by building a model that discriminates between up- and down-regulated genes in iron copper deficiency. LogicMotif identifies an inductive and two repressor motifs in this dataset. The inductive motif matches the binding site of the transcription factor Aft1p that has a key role in regulation of the uptake process. One of the novel repressor sites is highly present in transcription control regions of FeS genes. This site could represent a TFBS for an unknown transcription factor involved in repression of genes encoding FeS proteins in iron deficiency. We establish the robustness of the method to the type of outcome variable used by considering both continuous and binary outcome variables for this dataset. Our results indicate that logic regression used in combination with cluster/group operating binding site identification methods or with our proposed method MFURE is a powerful and flexible alternative to linear regression based motif finding methods. Source code for logic regression is freely available as a package of the R programming language by Ruczinski et al. (2003) and can be downloaded at http://bear.fhcrc.org/~ingor/logic/download/download.html an R package for MFURE is available at http://www.stat.berkeley.edu/~sunduz/software.html

Identifying cooperativity among transcription factors controlling the cell cycle in yeast.

Transcription regulation in eukaryotes is known to occur through the coordinated action of multiple transcription factors (TFs). Recently, a few genome-wide transcription studies have begun to explore the combinatorial nature of TF interactions. We propose a novel approach that reveals how multiple TFs cooperate to regulate transcription in the yeast cell cycle. Our method integrates genome-wide gene expression data and chromatin immunoprecipitation (ChIP-chip) data to discover more biologically relevant synergistic interactions between different TFs and their target genes than previous studies. Given any pair of TFs A and B, we define a novel measure of cooperativity between the two TFs based on the expression patterns of sets of target genes of only A, only B, and both A and B. If the cooperativity measure is significant then there is reason to postulate that the presence of both TFs is needed to influence gene expression. Our results indicate that many cooperative TFs that were previously characterized experimentally indeed have high values of cooperativity measures in our analysis. In addition, we propose several novel, experimentally testable predictions of cooperative TFs that play a role in the cell cycle and other biological processes. Many of them hold interesting clues for cross talk between the cell cycle and other processes including metabolism, stress response and pseudohyphal differentiation. Finally, we have created a web tool where researchers can explore the exhaustive list of cooperative TFs and survey the graphical representation of the target genes' expression profiles. The interface includes a tool to dynamically draw a TF cooperativity network of 113 TFs with user-defined significance levels. This study is an example of how systematic combination of diverse data types along with new functional genomic approaches can provide a rigorous platform to map TF interactions more efficiently.

Computational approaches to identifying transcription factor binding sites in yeast genome

This chapter describes the basic ideas behind some of the algorithms. In the yeast genome, there are an estimated 500-600 transcription factors. The targets of these factors can range from a few to a few hundred genes. Typically the upstream region of a gene includes the binding sites of different transcription factors that control the expression of the gene under different conditions. Thus, on a genomic scale, transcriptional regulation has a huge combinatorial complexity and the identification of regulatory sites is a very challenging problem. Several approaches have been developed to tackle this problem. One commonly used approach has been to delineate as sharply as possible a group of coregulated genes and search for common sequence patterns in their upstream regulatory regions. The computational algorithms range from finding overrepresented substrings or regular expression patterns to multiple local sequence alignment. The prerequisite for this class of algorithms is a cleanly defined subset of genes that may share a few common motifs. The chapter describes a few representatives in this category. An alternative approach is to delineate combinatorial motifs from a large collection of regulatory sequences without the need for defining coregulated groups. The chapter also describes two algorithms in this category that have been developed. One algorithm is based on a mathematical model of probabilistic segmentation, a generalization of segmentation models used in statistical language processing. Another algorithm identifies sequence patterns in the promoter regions of genes that strongly correlate with the genome-wide gene expression data.

Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study

BackgroundA method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reduction of raw expression data and the feature selections necessary for, for example, classification of disease samples remains a challenge. To solve the problem we propose a two-level analysis. First self-organizing map (SOM) is used. SOM is a vector quantization method that simplifies and reduces the dimensionality of original measurements and visualizes individual tumor sample in a SOM component plane. Next, hierarchical clustering and K-means clustering is used to identify patterns of gene expression useful for classification of samples.ResultsWe tested the two-level analysis on public data from diffuse large B-cell lymphomas. The analysis easily distinguished major gene expression patterns without the need for supervision: a germinal center-related, a proliferation, an inflammatory and a plasma cell differentiation-related gene expression pattern. The first three patterns matched the patterns described in the original publication using supervised clustering analysis, whereas the fourth one was novel.ConclusionsOur study shows that by using SOM as an intermediate step to analyze genome-wide gene expression data, the gene expression patterns can more easily be revealed. The "expression display" by the SOM component plane summarises the complicated data in a way that allows the clinician to evaluate the classification options rather than giving a fixed diagnosis.

Metabolic engineering.

Metabolic engineering is the science that combines systematic analysis of metabolic and other pathways with molecular biological techniques to improve cellular properties by designing and implementing rational genetic modifications. As such, metabolic engineering deals with the measurement of metabolic fluxes and elucidation of their control as determinants of metabolic function and cell physiology. A novel aspect of metabolic engineering is that it departs from the traditional reductionist paradigm of cellular metabolism, taking instead a holistic view. In this sense, metabolic engineering is well suited as a framework for the analysis of genome-wide differential gene expression data, in combination with data on protein content and in vivo metabolic fluxes. The insights of the integrated view of metabolism generated by metabolic engineering will have profound implications in biotechnological applications, as well as in devising rational strategies for target selection for screening candidate drugs or designing gene therapies. In this article we review basic concepts of metabolic engineering and provide examples of applications in the production of primary and secondary metabolites, improving cellular properties, and biomedical engineering.