697573
UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets
BackgroundCollective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets.ResultsHere, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn.ConclusionsThe UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.
24509304
Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization.
Microarray profiling of gene expression is a powerful tool for discovery, but the ability to manage and compare the resulting data can be problematic. Biological, experimental, and technical variations between studies of the same phenotype/phenomena create substantial differences in results. The application of conventional meta-analysis to raw microarray data is complicated by differences in the type of microarray used, gene nomenclatures, species, and analytical methods. An alternative approach to combining multiple microarray studies is to compare the published gene lists which result from the investigators' analyses of the raw data, as implemented in Lists of Lists Annotated (LOLA: www.lola.gwu.edu) and L2L (depts.washington.edu/l2l/). The present review considers both the potential value and the limitations of databasing and enabling the comparison of results from different microarray studies. Further, a major impediment to cross-study comparisons is the absence of a standard for reporting microarray study results. We propose a reporting standard: standard microarray results template (SMART), which will facilitate the integration of microarray studies.
24695222
An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction
Gene coexpression relationships that are phylogenetically conserved between human and mouse have been shown to provide important clues about gene function that can be efficiently used to identify promising candidate genes for human hereditary disorders. In the past, such approaches have considered mostly generic gene expression profiles that cover multiple tissues and organs. The individual genes of multicellular organisms, however, can participate in different transcriptional programs, operating at scales as different as single-cell types, tissues, organs, body regions or the entire organism. Therefore, systematic analysis of tissue-specific coexpression could be, in principle, a very powerful strategy to dissect those functional relationships among genes that emerge only in particular tissues or organs. In this report, we show that, in fact, conserved coexpression as determined from tissue-specific and condition-specific data sets can predict many functional relationships that are not detected by analyzing heterogeneous microarray data sets. More importantly, we find that, when combined with disease networks, the simultaneous use of both generic (multi-tissue) and tissue-specific conserved coexpression allows a more efficient prediction of human disease genes than the use of generic conserved coexpression alone. Using this strategy, we were able to identify high-probability candidates for 238 orphan disease loci. We provide proof of concept that this combined use of generic and tissue-specific conserved coexpression can be very useful to prioritize the mutational candidates obtained from deep-sequencing projects, even in the case of genetic disorders as heterogeneous as XLMR.
33766143
Genome-wide coexpression dynamics: Theory and application
High-throughput expression profiling enables the global study of gene activities. Genes with positively correlated expression profiles are likely to encode functionally related proteins. However, all biological processes are interlocked, and each protein may play multiple cellular roles. Thus the coexpression of any two functionally related genes may depend on the constantly varying, yet often-unknown cellular state. To initiate a systematic study on this issue, a theory of coexpression dynamics is presented. This theory is used to rationalize a strategy of conducting a genome-wide search for the most critical cellular players that may affect the coexpression pattern of any two genes. In one example, using a yeast data set, our method reveals how the enzymes associated with the urea cycle are expressed to ensure proper mass flow of the involved metabolites. The correlation between ARG2 and CAR2 is found to change from positive to negative as the expression level of CPA2 increases. This delicate interplay in correlation signifies a remarkable control on the influx and efflux of ornithine and reflects well the intrinsic cellular demand for arginine. In addition to the urea cycle, our examples include SCH9 and CYR1 (both implicated in a recent longevity study), cytochrome c1 (mitochondrial electron transport), calmodulin (main calcium-binding protein), PFK1 and PFK2 (glycolysis), and two genes, ECM1 and YNL101W, the functions of which are newly revealed. The complexity in computation is eased by a new result from mathematical statistics.
3131371
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Manyof these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentallyconfirmed the predictions implied bysome of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
19412190
The budding yeast rRNA and ribosome biosynthesis (RRB) regulon contains over 200 genes
The ribosome biogenesis pathway constitutes one of the major metabolic obligations for a dividing yeast cell and it depends upon the activity of hundreds of gene products to produce the necessary rRNA and ribosomal protein components. Previously, we reported that a set of 65 S. cerevisiae genes that function in the rRNA biosynthesis pathway are transcriptionally co‐regulated as cells pass through a variety of physiological transitions. By analysing multiple microarray‐based transcriptional datasets, we have extended that study and now suggest that the ribosomal and rRNA biosynthesis regulon contains over 200 genes. This regulon is distinct from the set of ribosomal protein genes, and the promoters of the expanded RRB gene set are highly enriched for the PAC and RRPE motifs. Since a similar pattern of organization and gene regulation can be recognized in C. albicans, the RRB regulon appears to be a conserved, extensive, and metabolically important group of genes. Copyright © 2006 John Wiley & Sons, Ltd.
9622989
An empirical comparison of four initialization methods for the K-Means algorithm
In this paper, we aim to compare empirically four initialization methods for the K-Means algorithm: random, Forgy, MacQueen and Kaufman. Although this algorithm is known for its robustness, it is widely reported in the literature that its performance depends upon two key points: initial clustering and instance order. We conduct a series of experiments to draw up (in terms of mean, maximum, minimum and standard deviation) the probability distribution of the square-error values of the final clusters returned by the K-Means algorithm independently on any initial clustering and on any instance order when each of the four initialization methods is used. The results of our experiments illustrate that the random and the Kaufman initialization methods outperform the rest of the compared methods as they make the K-Means more effective and more independent on initial clustering and on instance order. In addition, we compare the convergence speed of the K-Means algorithm when using each of the four initialization methods. Our results suggest that the Kaufman initialization method induces to the K-Means algorithm a more desirable behaviour with respect to the convergence speed than the random initialization method.
5612356
Cluster analysis and display of genome-wide expression patterns.
A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
371422
Gene clustering using self-organizing maps and particle swarm optimization
Gene clustering, the process of grouping related genes in the same cluster, is at the foundation of different genomic studies that aim at analyzing the function of genes. Microarray technologies have made it possible to measure gene expression levels for thousand of genes simultaneously. For knowledge to be extracted from the datasets generated by these technologies, the datasets have to be presented to a scientist in a meaningful way. Gene clustering methods serve this purpose. In this paper, a hybrid clustering approach that is based on self-organizing maps and particle swarm optimization is proposed. In the proposed algorithm, the rate of convergence is improved by adding a conscience factor to the self-organizing maps algorithm. The robustness of the result is measured by using a resampling technique. The algorithm is implemented on a cluster of workstations.
14813790
Paradigm of Tunable Clustering Using Binarization of Consensus Partition Matrices (Bi-CoPaM) for Gene Discovery
Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.
15327847
Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis
BackgroundThe scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result.ResultsIn this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters’ tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other.ConclusionsThe first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.
20348491
A system for enhancing genome-wide coexpression dynamics study.
Statistical similarity analysis has been instrumental in elucidation of the voluminous microarray data. Genes with correlated expression profiles tend to be functionally associated. However, the majority of functionally associated genes turn out to be uncorrelated. One conceivable reason is that the expression of a gene can be sensitively dependent on the often-varying cellular state. The intrinsic state change has to be plastically accommodated by gene-regulatory mechanisms. To capture such dynamic coexpression between genes, a concept termed "liquid association" (LA) has been introduced recently. LA offers a scoring system to guide a genome-wide search for critical cellular players that may interfere with the coexpression of a pair of genes, thereby weakening their overall correlation. Although the LA method works in many cases, a direct extension to more than two genes is hindered by the "curse of dimensionality." Here we introduce a strategy of finding an informative 2D projection to generalize LA for multiple genes. A web site is constructed that performs on-line LA computation for any user-specified group of genes. We apply this scoring system to study yeast protein complexes by using the Saccharomyces cerevisiae protein complexes database of the Munich Information Center for Protein Sequences. Human genes are also investigated by profiling of 60 cancer cell lines of the National Cancer Institute. In particular, our system links the expression of the Alzheimer's disease hallmark gene APP (amyloid-beta precursor protein) to the beta-site-cleaving enzymes BACE and BACE2, the gamma-site-cleaving enzymes presenilin 1 and 2, apolipoprotein E, and other Alzheimer's disease-related genes.
16095037
Differential coexpression analysis using microarray data and its application to human cancer
MOTIVATION
Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer.


RESULTS
We collected 10 published gene expression datasets from cancers of 13 different tissues and constructed 2 distinct coexpression networks: a tumor network and normal network. Comparison of the two networks showed that cancer affected many coexpression relationships. Functional changes such as alteration in energy metabolism, promotion of cell growth and enhanced immune activity were accompanied with coexpression changes. Coregulation of collagen genes that may control invasion and metastatic spread of tumor cells was also found. Cluster analysis in the tumor network identified groups of highly interconnected genes related to ribosomal protein synthesis, the cell cycle and antigen presentation. Metallothionein expression was also found to be clustered, which may play a role in apoptosis control in tumor cells. Our results show that this model would serve as a novel method for analyzing microarrays beyond the specific implications for cancer.
23815964
Coexpression of VEGF and angiopoietin-1 promotes angiogenesis and cardiomyocyte proliferation reduces apoptosis in porcine myocardial infarction (MI) heart
VEGF and angiopoietin-1 (Ang1) are two major angiogenic factors being investigated for the treatment of myocardial infarction (MI). Targeting VEGF and Ang1 expression in the ischemic myocardium can increase their local therapeutic effects and reduce possible adverse effects. Adeno-associated viral vectors (AAVs) expressing cardiac-specific and hypoxia-inducible VEGF [AAV-myosin light chain-2v (MLC)VEGF] and Ang1 (AAV-MLCAng1) were coinjected (VEGF/Ang1 group) into six different sites of the porcine myocardium at the peri-infarct zone immediately after ligating the left descending coronary artery. An identical dose of AAV-Cytomegalovirus (CMV)LacZ or saline was injected into control animals. AAV genomes were detected in the liver in addition to the heart. RT-PCR, Western blotting, and ELISA analyses showed that VEGF and Ang1 were predominantly expressed in the myocardium in the infarct core and border of the infarct heart. Gated single-photon emission computed tomography analyses showed that the VEGF/Ang1 group had better cardiac function and myocardial perfusion at 8 wk than at 2 wk after vector injection. Compared with the saline and LacZ controls, the VEGF/Ang1 group expressed higher phosphorylated Akt and Bcl-xL, less Caspase-3 and Bad, and had higher vascular density, more proliferating cardiomyocytes, and less apoptotic cells in the infarct and peri-infarct zones. Thus, cardiac-specific and hypoxia-induced coexpression of VEGF and Ang1 improves the perfusion and function of porcine MI heart through the induction of angiogenesis and cardiomyocyte proliferation, activation of prosurvival pathways, and reduction of cell apoptosis.
6321522
Biclustering of Expression Data
An efficient node-deletion algorithm is introduced to find submatrices in expression data that have low mean squared residue scores and it is shown to perform well in finding co-regulation patterns in yeast and human. This introduces "biclustering", or simultaneous clustering of both genes and conditions, to knowledge discovery from expression data. This approach overcomes some problems associated with traditional clustering methods, by allowing automatic discovery of similarity based on a subset of attributes, simultaneous clustering of genes and conditions, and overlapped grouping that provides a better representation for genes with multiple functions or regulated by many factors.
11403283
A systematic comparison and evaluation of biclustering methods for gene expression data
MOTIVATION
In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available.


RESULTS
First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings.
2664253
Yeast gene CMR1/YDL156W is consistently co-expressed with genes participating in DNA-metabolic processes in a variety of stringent clustering experiments
The binarization of consensus partition matrices (Bi-CoPaM) method has, among its unique features, the ability to perform ensemble clustering over the same set of genes from multiple microarray datasets by using various clustering methods in order to generate tunable tight clusters. Therefore, we have used the Bi-CoPaM method to the most synchronized 500 cell-cycle-regulated yeast genes from different microarray datasets to produce four tight, specific and exclusive clusters of co-expressed genes. We found 19 genes formed the tightest of the four clusters and this included the gene CMR1/YDL156W, which was an uncharacterized gene at the time of our investigations. Two very recent proteomic and biochemical studies have independently revealed many facets of CMR1 protein, although the precise functions of the protein remain to be elucidated. Our computational results complement these biological results and add more evidence to their recent findings of CMR1 as potentially participating in many of the DNA-metabolism processes such as replication, repair and transcription. Interestingly, our results demonstrate the close co-expressions of CMR1 and the replication protein A (RPA), the cohesion complex and the DNA polymerases α, δ and ɛ, as well as suggest functional relationships between CMR1 and the respective proteins. In addition, the analysis provides further substantial evidence that the expression of the CMR1 gene could be regulated by the MBF complex. In summary, the application of a novel analytic technique in large biological datasets has provided supporting evidence for a gene of previously unknown function, further hypotheses to test, and a more general demonstration of the value of sophisticated methods to explore new large datasets now so readily generated in biological experiments.
13523816
Application of the Bi-CoPaM Method to Five Escherichia Coli Datasets Generated under Various Biological Conditions
The increasing amounts of high-throughput biological datasets stimulate the information engineering and machine learning research community to direct more studies towards designing and applying novel methods which are sophisticated and specialised to tackle the problems that are specific in such datasets. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method tackles the problem of scrutinising multiple gene expression microarray datasets to identify the subsets of genes which are consistently co-expressed across them. It allows for clustering results which better reflect the biological fact that most of the genes in any cell are expected to be irrelevant to the specific context in hand, as well as the fact that many genes might participate in multiple processes. This has been achieved by clustering the given set of genes while allowing any gene to have any of the three eventualities, to be exclusively assigned to a single cluster, to be simultaneously assigned to multiple clusters, or not to be assigned to any of the clusters. In this study, we expand the scope of application of the Bi-CoPaM method by applying it, for the first time, to bacterial datasets, namely to a set of five Escherichia coli bacterial datasets generated under different biological conditions, in order to identify the subsets of genes which are consistently co-expressed, i.e. well correlated with each other. We identify two clusters with such consistent co-expression, and interestingly, they themselves are consistently negatively correlated with each other. The first cluster is enriched with genes participating in protein synthesis and DNA repair while the second is enriched with transporting genes. Consequently, we draw biological hypotheses that relate some of the genes with currently unknown biological processes to their potential processes. These hypotheses can serve as pilots for focused future gene discovery studies.
436744
Enhanced SMART framework for gene clustering using successive processing
In this paper, we develop an enhanced splitting merging awareness tactics (E-SMART) framework using successive processing. Instead of selecting the best clustering from the results by using clustering selection criterion in original SMART framework, we introduce a successive processing strategy into the framework to subtract clusters one by one in iterations. In doing so, the silhouette index is employed to evaluate the intermediate clusters and order them according to their index values from high to low. Then we subtract the best cluster from the original dataset and iterate the remaining dataset back to the splitting-while-merging (SWM) process to start a new iteration. The clustering and subtracting are repeated successively and terminated automatically, once no splitting happened in the SWM process. Consequently, all clusters can be obtained by iterations. We implement the framework using component-wise expectation maximization (CEM) for finite mixture models (FMM). The E-SMART-FMM implementation is tested in real NCI-60 cancer dataset. We evaluate the clustering results from the proposed algorithm, together with two existing self-splitting algorithms, using two popular validation indices other than the silhouette index. The results of both validation indices consistently demonstrate that E-SMART-FMM is superior to the existing algorithms.
95931968
Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes
The goal of present work is to analyse the effect of having non-informative variables (NIV) in a data set when applying cluster analysis and to propose a method computationally capable of detecting and removing these variables. The method proposed is based on the use of a genetic algorithm to select those variables important to make the presence of groups in data clear. The procedure has been implemented to be used with k-means and using the cluster silhouettes as fitness function for the genetic algorithm. 
 
The main problem that can appear when applying the method to real data is the fact that, in general, we do not know a priori what the real cluster structure is (number and composition of the groups). 
 
The work explores the evolution of the silhouette values computed from the clusters built by using k-means when non-informative variables are added to the original data set in both a literature data set as well as some simulated data in higher dimension. The procedure has also been applied to real data sets.
15712376
Cytoplasmic-nuclear genomic barriers in rice pollen development revealed by comparison of global gene expression profiles among five independent cytoplasmic male sterile lines.
Cytoplasmic male sterility (CMS) is one of the most ideal phenomena known in higher plants to describe the incompatibilities between mitochondrial-nuclear genomic interactions. To elucidate the dependency of pollen development on mitochondrial genotypes and cytoplasmic-nuclear genomic barriers, we employed five CMS isogenic lines of rice, CW-, W11-, LD-, BT- and WA-type CMS lines, that exhibit distinct pollen-defective phenotypes, and we characterized the CMS phenotypes and the nuclear gene expression patterns in conjunction with their mitochondrial genomic structures. These five CMS lines carried independent mitotypes, and W11, LD and BT mitochondrial genomes were relatively close with respect to their phylogeny. In anthers at the uninucleate microspore and bicellular pollen stages, 8,199 genes significantly changed their expression in at least one of the CMS lines. Common expression patterns were observed in BT, LD and W11 after k-means clustering. Among the genes encoding putative mitochondrial proteins, ALTERNATIVE OXIDASE 1A, a gene for the well-known mitochondrial stress marker, was included in the group ectopically up-regulated in anthers at the bicellular pollen stage of BT, LD and W11. Several other clusters were also regulated in a cytoplasm-specific manner during pollen development. These clear similarities in gene regulatory networks of BT-, LD- and W11-CMS lines indicate that the phylogenetic relationships of the mitochondrial genotypes are strongly correlated with nuclear gene expression patterns and pollen abortion phenotypes, providing evidence of the mitochondrial epistacy over the nuclear genome during pollen development.
30205867
Identification of embryonic pancreatic genes using Xenopus DNA microarrays
The pancreas is both an exocrine and endocrine endodermal organ involved in digestion and glucose homeostasis. During embryogenesis, the anlagen of the pancreas arise from dorsal and ventral evaginations of the foregut that later fuse to form a single organ. To better understand the molecular genetics of early pancreas development, we sought to isolate markers that are uniquely expressed in this tissue. Microarray analysis was performed comparing dissected pancreatic buds, liver buds, and the stomach region of tadpole stage Xenopus embryos. A total of 912 genes were found to be differentially expressed between these organs during early stages of organogenesis. K‐means clustering analysis predicted 120 of these genes to be specifically enriched in the pancreas. Of these, we report on the novel expression patterns of 24 genes. Our analyses implicate the involvement of previously unsuspected signaling pathways during early pancreas development. Developmental Dynamics 238:1455–1466, 2009. © 2009 Wiley‐Liss, Inc.
43724327
Regulation of megakaryocytic differentiation of K562 cells by FosB, a member of the Fos family of AP-1 transcription factors
Abstract.The regulation of megakaryocytic differentiation is poorly understood. Using K562 cells, which can partly recapitulate the process in response to phorbol 12-myristate 13-acetate (PMA), we performed microarray-based gene expression profiling to identify genes that play significant roles in megakaryopoiesis. Here, we describe the function of FosB, an AP-1 transcription factor. FosB is induced in PMA treated K562 cells in a sustained manner and forms an active AP-1 protein-DNA complex. Down-regulation of FosB with specific shRNAs inhibited the induction of CD41, a specific cell surface marker of megakaryocytes. We also show that activation of the PKC-MEK-ERK signaling pathway is required for induction of FosB and CD41. Finally, we cross-examined the microarray data in conjunction with gene function annotation data to identify additional target genes of FosB. We define 3 genes, INHBA, CD9, and ITGA2B as regulatory targets of FosB and show that CD9, in particular, is a direct target of FosB.
23264453
Genome-wide analysis of gene expression profiles during the kernel development of maize (Zea mays L.).
Maize kernel is an important source of food, feed, and industrial raw materials. The elucidation of the molecular mechanisms of maize kernel development will be helpful for the manipulation of maize improvements. A microarray with approximately 58,000 probes was used to study dynamic gene expression during kernel development from fertilization to physiological maturity. By comparing six consecutive time points, 3445 differentially expressed genes were identified. These genes were then grouped into 10 clusters showing specific expression patterns using a K-means clustering algorithm. An investigation of function and expression patterns of genes elucidate the regulation mechanism underlying the important developmental processes cell division and kernel filling. The differential expression of genes involved in plant hormone signaling pathways suggested that phytohormone might play a critical role in the kernel developmental process. Moreover, regulation of some transcription factors and protein kinases might be involved in the whole developmental process.
16771648
Finding large average submatrices in high dimensional data
The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data. Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.) In this paper we propose and evaluate a statistically motivated biclustering procedure (LAS) that finds large average submatrices within a given real-valued data matrix. The procedure operates in an iterative-residual fashion, and is driven by a Bonferroni-based significance score that effectively trades off between submatrix size and average value. We examine the performance and potential utility of LAS, and compare it with a number of existing methods, through an extensive three-part validation study using two gene expression datasets. The validation study examines quantitative properties of biclusters, biological and clinical assessments using auxiliary information, and classification of disease subtypes using bicluster membership. In addition, we carry out a simulation study to assess the effectiveness and noise sensitivity of the LAS search procedure. These results suggest that LAS is an effective exploratory tool for the discovery of biologically relevant structures in high dimensional data. Software is available at this https URL
15590424
A random-periods model for expression of cell-cycle genes.
We propose a nonlinear regression model for quantitatively analyzing periodic gene expression in studies of experimentally synchronized cells. Our model accounts for the observed attenuation in cycle amplitude by a simple and biologically plausible mechanism. We represent the expression level for each gene as an average across a large number of cells. For a given cell-cycle gene, we model its expression in each cell in the culture as following the same sinusoidal function except that the period, which in any individual cell must be the same for all cell-cycle genes, varies randomly across cells. We model these random periods by using a lognormal distribution. The variability in period causes the measured amplitude of the cyclic expression trajectory to attenuate over time as cells fall increasingly out of synchrony. Gene-specific parameters include initial amplitude and phase angle. Applying the model to data from Whitfield et al. [Whitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J, I., Ball, C. A., et al. (2002) Mol. Biol. Cell 13, 1977-2000], we fit the trajectories of 18 well characterized phase-marker genes and find that the fit does not suffer when a common lognormal distribution is assumed for all 18 genes compared with a separate distribution for each. We then use the model to identify 337 periodically expressed transcripts, including the 18 phase-marker genes. The model permits estimation of and hypothesis testing about biologically meaningful parameters that characterize cycling genes.
377350
Principal component analysis for clustering gene expression data
MOTIVATION
There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes.


RESULTS
Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.
29173982
Gene expression microarray data analysis demystified.
The increasing use of gene expression microarrays, and depositing of the resulting data into public repositories, means that more investigators are interested in using the technology either directly or through meta analysis of the publicly available data. The tools available for data analysis have generally been developed for use by experts in the field, making them difficult to use by the general research community. For those interested in entering the field, especially those without a background in statistics, it is difficult to understand why experimental results can be so variable. The purpose of this review is to go through the workflow of a typical microarray experiment, to show that decisions made at each step, from choice of platform through statistical analysis methods to biological interpretation, are all sources of this variability.
4643842
A Survey of Clustering Ensemble Algorithms
Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.
5704362
Spectral biclustering of microarray data: coclustering genes and conditions.
Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).
3193955
An Improved Biclustering Method for Analyzing Gene Expression Profiles
Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church1 to capture the coherence of a subset of genes and a subset of conditions. A set of heuristic algorithms were also designed to either find one bicluster or a set of biclusters, which consist of iterations of masking null values and discovered biclusters, coarse and fine node deletion, node addition, and the inclusion of inverted data. These heuristics inevitably suffer from some serious drawback. The masking of null values and discovered biclusters with random numbers may result in the phenomenon of random interference which in turn impacts the discovery of high quality biclusters. To address this issue and to further accelerate the biclustering process, we generalize the model of bicluster to incorporate null values and propose a probabilistic algorithm (FLOC) that can discover a set of k possibly overlapping biclusters simultaneously. Furthermore, this algorithm can easily be extended to support additional features that suit different requirements at virtually little cost. Experimental study on the yeast gene expression data2 shows that the FLOC algorithm can offer substantial improvements over the previously proposed algorithm.
10011119
Extracting Conserved Gene Expression Motifs from Gene Expression Data
We propose a representation for gene expression data called conserved gene expression motifs or XMOTIFs. A gene's expression level is conserved across a set of samples if the gene is expressed with the same abundance in all the samples. A conserved gene expression motif is a subset of genes that is simultaneously conserved across a subset of samples. We present a computational technique to discover large conserved gene motifs that cover all the samples and classes in the data. When applied to published data sets representing different cancers or disease outcomes, our algorithm constructs XMOTIFS that distinguish between the various classes.
11847258
Co-clustering documents and words using bipartite spectral graph partitioning
Both document clustering and word clustering are well studied problems. Most existing algorithms cluster documents and words separately but not simultaneously. In this paper we present the novel idea of modeling the document collection as a bipartite graph between documents and words, using which the simultaneous clustering problem can be posed as a bipartite graph partitioning problem. To solve the partitioning problem, we use a new spectral co-clustering algorithm that uses the second left and right singular vectors of an appropriately scaled word-document matrix to yield good bipartitionings. The spectral algorithm enjoys some optimality properties; it can be shown that the singular vectors solve a real relaxation to the NP-complete graph bipartitioning problem. We present experimental results to verify that the resulting co-clustering algorithm works well in practice.
5839507
Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.
We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: alpha factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu
24107017
A genome-wide transcriptional analysis of the mitotic cell cycle.
Progression through the eukaryotic cell cycle is known to be both regulated and accompanied by periodic fluctuation in the expression levels of numerous genes. We report here the genome-wide characterization of mRNA transcript levels during the cell cycle of the budding yeast S. cerevisiae. Cell cycle-dependent periodicity was found for 416 of the 6220 monitored transcripts. More than 25% of the 416 genes were found directly adjacent to other genes in the genome that displayed induction in the same cell cycle phase, suggesting a mechanism for local chromosomal organization in global mRNA regulation. More than 60% of the characterized genes that displayed mRNA fluctuation have already been implicated in cell cycle period-specific biological roles. Because more than 20% of human proteins display significant homology to yeast proteins, these results also link a range of human genes to cell cycle period-specific biological functions.
2958291
The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle.
Transcription patterns shift dramatically as cells transit from one phase of the cell cycle to another. To better define this transcriptional circuitry, we collected new microarray data across the cell cycle of budding yeast. The combined analysis of these data with three other cell cycle data sets identifies hundreds of new highly periodic transcripts and provides a weighted average peak time for each transcript. Using these data and phylogenetic comparisons of promoter sequences, we have identified a late S-phase-specific promoter element. This element is the binding site for the forkhead protein Hcm1, which is required for its cell cycle-specific activity. Among the cell cycle-regulated genes that contain conserved Hcm1-binding sites, there is a significant enrichment of genes involved in chromosome segregation, spindle dynamics, and budding. This may explain why Hcm1 mutants show 10-fold elevated rates of chromosome loss and require the spindle checkpoint for viability. Hcm1 also induces the M-phase-specific transcription factors FKH1, FKH2, and NDD1, and two cell cycle-specific transcriptional repressors, WHI5 and YHP1. As such, Hcm1 fills a significant gap in our understanding of the transcriptional circuitry that underlies the cell cycle.
205213307
Global control of cell-cycle transcription by coupled CDK and network oscillators
A significant fraction of the Saccharomyces cerevisiae genome is transcribed periodically during the cell division cycle, indicating that properly timed gene expression is important for regulating cell-cycle events. Genomic analyses of the localization and expression dynamics of transcription factors suggest that a network of sequentially expressed transcription factors could control the temporal programme of transcription during the cell cycle. However, directed studies interrogating small numbers of genes indicate that their periodic transcription is governed by the activity of cyclin-dependent kinases (CDKs). To determine the extent to which the global cell-cycle transcription programme is controlled by cyclin–CDK complexes, we examined genome-wide transcription dynamics in budding yeast mutant cells that do not express S-phase and mitotic cyclins. Here we show that a significant fraction of periodic genes are aberrantly expressed in the cyclin mutant. Although cells lacking cyclins are blocked at the G1/S border, nearly 70% of periodic genes continued to be expressed periodically and on schedule. Our findings reveal that although CDKs have a function in the regulation of cell-cycle transcription, they are not solely responsible for establishing the global periodic transcription programme. We propose that periodic transcription is an emergent property of a transcription factor network that can function as a cell-cycle oscillator independently of, and in tandem with, the CDK oscillator.
680949
The transcriptional program of sporulation in budding yeast.
Diploid cells of budding yeast produce haploid cells through the developmental program of sporulation, which consists of meiosis and spore morphogenesis. DNA microarrays containing nearly every yeast gene were used to assay changes in gene expression during sporulation. At least seven distinct temporal patterns of induction were observed. The transcription factor Ndt80 appeared to be important for induction of a large group of genes at the end of meiotic prophase. Consensus sequences known or proposed to be responsible for temporal regulation could be identified solely from analysis of sequences of coordinately expressed genes. The temporal expression pattern provided clues to potential functions of hundreds of previously uncharacterized genes, some of which have vertebrate homologs that may function during gametogenesis.
17686011
Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p.
Eukaryotic cells respond to DNA damage by arresting the cell cycle and modulating gene expression to ensure efficient DNA repair. The human ATR kinase and its homolog in yeast, MEC1, play central roles in transducing the damage signal. To characterize the role of the Mec1 pathway in modulating the cellular response to DNA damage, we used DNA microarrays to observe genomic expression in Saccharomyces cerevisiae responding to two different DNA-damaging agents. We compared the genome-wide expression patterns of wild-type cells and mutants defective in Mec1 signaling, including mec1, dun1, and crt1 mutants, under normal growth conditions and in response to the methylating-agent methylmethane sulfonate (MMS) and ionizing radiation. Here, we present a comparative analysis of wild-type and mutant cells responding to these DNA-damaging agents, and identify specific features of the gene expression responses that are dependent on the Mec1 pathway. Among the hundreds of genes whose expression was affected by Mec1p, one set of genes appears to represent an MEC1-dependent expression signature of DNA damage. Other aspects of the genomic responses were independent of Mec1p, and likely independent of DNA damage, suggesting the pleiotropic effects of MMS and ionizing radiation. The complete data set as well as supplemental materials is available at http://www-genome.stanford.edu/mec1.
2596007
How yeast re-programmes its transcriptional profile in response to different nutrient impulses
BackgroundA microorganism is able to adapt to changes in its physicochemical or nutritional environment and this is crucial for its survival. The yeast, Saccharomyces cerevisiae, has developed mechanisms to respond to such environmental changes in a rapid and effective manner; such responses may demand a widespread re-programming of gene activity. The dynamics of the re-organization of the cellular activities of S. cerevisiae in response to the sudden and transient removal of either carbon or nitrogen limitation has been studied by following both the short- and long-term changes in yeast's transcriptomic profiles.ResultsThe study, which spans timescales from seconds to hours, has revealed the hierarchy of metabolic and genetic regulatory switches that allow yeast to adapt to, and recover from, a pulse of a previously limiting nutrient. At the transcriptome level, a glucose impulse evoked significant changes in the expression of genes concerned with glycolysis, carboxylic acid metabolism, oxidative phosphorylation, and nucleic acid and sulphur metabolism. In ammonium-limited cultures, an ammonium impulse resulted in the significant changes in the expression of genes involved in nitrogen metabolism and ion transport. Although both perturbations evoked significant changes in the expression of genes involved in the machinery and process of protein synthesis, the transcriptomic response was delayed and less complex in the case of an ammonium impulse. Analysis of the regulatory events by two different system-level, network-based approaches provided further information about dynamic organization of yeast cells as a response to a nutritional change.ConclusionsThe study provided important information on the temporal organization of transcriptomic organization and underlying regulatory events as a response to both carbon and nitrogen impulse. It has also revealed the importance of a long-term dynamic analysis of the response to the relaxation of a nutritional limitation to understand the molecular basis of the cells' dynamic behaviour.
41766468
Systematic identification and functional screens of uncharacterized proteins associated with eukaryotic ribosomal complexes.
Translation regulation is a critical means by which cells control growth, division, and apoptosis. To gain further insight into translation and related processes, we performed multifaceted mass spectrometry-based proteomic screens of yeast ribosomal complexes and discovered an association of 77 uncharacterized yeast proteins with ribosomes. Immunoblotting revealed an EDTA-dependent cosedimentation with ribosomes in sucrose gradients for 11 candidate translation-machinery-associated (TMA) proteins. Tandem affinity purification linked one candidate, LSM12, to the RNA processing proteins PBP1 and PBP4. A second candidate, TMA46, interacted with RBG1, a GTPase that interacts with ribosomes. By adapting translation assays to high-throughput screening methods, we showed that null yeast strains harboring deletions for several of the TMA genes had alterations in protein synthesis rates (TMA7 and TMA19), susceptibility to drugs that inhibit translation (TMA7), translation fidelity (TMA20), and polyribosome profiles (TMA7, TMA19, and TMA20). TMA20 has significant sequence homology with the oncogene MCT-1. Expression of human MCT-1 in the Deltatma20 yeast mutant complemented translation-related defects, strongly implying that MCT-1 functions in translation-related processes. Together these findings implicate the TMA proteins and, potentially, their human homologs, in translation related processes.
2047843
Identification of a novel methyltransferase, Bmt2, responsible for the N-1-methyl-adenosine base modification of 25S rRNA in Saccharomyces cerevisiae
The 25S rRNA of yeast contains several base modifications in the functionally important regions. The enzymes responsible for most of these base modifications remained unknown. Recently, we identified Rrp8 as a methyltransferase involved in m1A645 modification of 25S rRNA. Here, we discovered a previously uncharacterized gene YBR141C to be responsible for second m1A2142 modification of helix 65 of 25S rRNA. The gene was identified by reversed phase–HPLC screening of all deletion mutants of putative RNA methyltransferase and was confirmed by gene complementation and phenotypic characterization. Because of the function of its encoded protein, YBR141C was named BMT2 (base methyltransferase of 25S RNA). Helix 65 belongs to domain IV, which accounts for most of the intersubunit surface of the large subunit. The 3D structure prediction of Bmt2 supported it to be an Ado Met methyltransferase belonging to Rossmann fold superfamily. In addition, we demonstrated that the substitution of G180R in the S-adenosyl-l-methionine–binding motif drastically reduces the catalytic function of the protein in vivo. Furthermore, we analysed the significance of m1A2142 modification in ribosome synthesis and translation. Intriguingly, the loss of m1A2142 modification confers anisomycin and peroxide sensitivity to the cells. Our results underline the importance of RNA modifications in cellular physiology.
11725765
90S pre-ribosomes include the 35S pre-rRNA, the U3 snoRNP, and 40S subunit processing factors but predominantly lack 60S synthesis factors.
We report the characterization of early pre-ribosomal particles. Twelve TAP-tagged components each showed nucleolar localization, sedimented at approximately 90S on sucrose gradients, and coprecipitated both the 35S pre-rRNA and the U3 snoRNA. Thirty-five non-ribosomal proteins were coprecipitated, including proteins associated with U3 (Nop56p, Nop58p, Sof1p, Rrp9, Dhr1p, Imp3p, Imp4p, and Mpp10p) and other factors required for 18S rRNA synthesis (Nop14p, Bms1p, and Krr1p). Mutations in components of the 90S pre-ribosomes impaired 40S subunit assembly and export. Strikingly, few components of recently characterized pre-60S ribosomes were identified in the 90S pre-ribosomes. We conclude that the 40S synthesis machinery predominately associates with the 35S pre-rRNA factors, whereas factors required for 60S subunit synthesis largely bind later, showing an unexpected dichotomy in binding.
4421200
Transcriptional regulatory code of a eukaryotic genome
DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.
14228237
Sch9 regulates ribosome biogenesis via Stb3, Dot6 and Tod6 and the histone deacetylase complex RPD3L
TORC1 is a conserved multisubunit kinase complex that regulates many aspects of eukaryotic growth including the biosynthesis of ribosomes. The TOR protein kinase resident in TORC1 is responsive to environmental cues and is potently inhibited by the natural product rapamycin. Recent characterization of the rapamycin‐sensitive phosphoproteome in yeast has yielded insights into how TORC1 regulates growth. Here, we show that Sch9, an AGC family kinase and direct substrate of TORC1, promotes ribosome biogenesis (Ribi) and ribosomal protein (RP) gene expression via direct inhibitory phosphorylation of the transcriptional repressors Stb3, Dot6 and Tod6. Deletion of STB3, DOT6 and TOD6 partially bypasses the growth and cell size defects of an sch9 strain and reveals interdependent regulation of both Ribi and RP gene expression, and other aspects of Ribi. Dephosphorylation of Stb3, Dot6 and Tod6 enables recruitment of the RPD3L histone deacetylase complex to repress Ribi/RP gene promoters. Taken together with previous studies, these results suggest that Sch9 is a master regulator of ribosome biogenesis through the control of Ribi, RP, ribosomal RNA and tRNA gene transcription.
13787960
Cell-cycle control of gene expression in budding and fission yeast.
Cell-cycle control of transcription seems to be a universal feature of proliferating cells, although relatively little is known about its biological significance and conservation between organisms. The two distantly related yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe have provided valuable complementary insight into the regulation of periodic transcription as a function of the cell cycle. More recently, genome-wide studies of proliferating cells have identified hundreds of periodically expressed genes and underlying mechanisms of transcriptional control. This review discusses the regulation of three major transcriptional waves, which roughly coincide with three main cell-cycle transitions (initiation of DNA replication, entry into mitosis, and exit from mitosis). I also compare and contrast the transcriptional regulatory networks between the two yeasts and discuss the evolutionary conservation and possible roles for cell cycle-regulated transcription.