Clustering methods for microarray gene expression data

Utility of Banff Human Organ Transplant Gene Panel in Human Kidney Transplant Biopsies

BACKGROUND

Microarray transcript analysis of human renal transplantation biopsies has successfully identified the many patterns of graft rejection. To evaluate an alternative, this report tests whether gene expression from the Banff Human Organ Transplant (B-HOT) probe set panel, derived from validated microarrays, can identify the relevant allograft diagnoses directly from archival human renal transplant formalin-fixed paraffin-embedded biopsies. To test this hypothesis, principal components (PCs) of gene expressions were used to identify allograft diagnoses, to classify diagnoses, and to determine whether the PC data were rich enough to identify diagnostic subtypes by clustering, which are all needed if the B-HOT panel can substitute for microarrays.

METHODS

RNA was isolated from routine, archival formalin-fixed paraffin-embedded tissue renal biopsy cores with both rejection and nonrejection diagnoses. The B-HOT panel expression of 770 genes was analyzed by PCs, which were then tested to determine their ability to identify diagnoses.

RESULTS

PCs of microarray gene sets identified the Banff categories of renal allograft diagnoses, modeled well the aggregate diagnoses, showing a similar correspondence with the pathologic diagnoses as microarrays. Clustering of the PCs identified diagnostic subtypes including non-chronic antibody-mediated rejection with high endothelial expression. PCs of cell types and pathways identified new mechanistic patterns including differential expression of B and plasma cells.

CONCLUSIONS

Using PCs of gene expression from the B-Hot panel confirms the utility of the B-HOT panel to identify allograft diagnoses and is similar to microarrays. The B-HOT panel will accelerate and expand transcript analysis and will be useful for longitudinal and outcome studies.

Cluster Analysis of Microarray Data

The cluster analysis has been widely applied by researchers from several scientific fields over the last decades. Advances in knowledge of biological phenomena have revived a great interest in cluster analysis due in part to the large amount of microarray data. Traditional clustering algorithms show, apart from the need of user-defined parameters, clear limitations to handle microarray data owing to its inherent characteristics: high-dimensional-low-sample-sized, highly redundant, and noisy. That has motivated the study of clustering algorithms tailored to the task of analyzing microarray data, which currently continue being developed and adapted. The present chapter is devoted to review clustering methods with different cluster analysis approaches in the challenging context of microarray data. Furthermore, the validation of the clustering results is briefly discussed by means of validity indexes used to assess the goodness of the number of clusters and the induced cluster assignments.

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

BACKGROUND

Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.

RESULTS

We developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward's method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes.

CONCLUSION

We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive 'Fuzzy Inference of Gene-sets (FIGS)' package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.

BEYOND TEXT: USING ARRAYS TO REPRESENT AND ANALYZE ETHNOGRAPHIC DATA

Recent methodological debates in sociology have focused on how data and analyses might be made more open and accessible, how the process of theorizing and knowledge production might be made more explicit, and how developing means of visualization can help address these issues. In ethnography, where scholars from various traditions do not necessarily share basic epistemological assumptions about the research enterprise with either their quantitative colleagues or one another, these issues are particularly complex. Nevertheless, ethnographers working within the field of sociology face a set of common pragmatic challenges related to managing, analyzing, and presenting the rich context-dependent data generated during fieldwork. Inspired by both ongoing discussions about how sociological research might be made more transparent, as well as innovations in other data-centered fields, the authors developed an interactive visual approach that provides tools for addressing these shared pragmatic challenges. They label the approach "ethnoarray" analysis. This article introduces this approach and explains how it can help scholars address widely shared logistical and technical complexities, while remaining sensitive to both ethnography's epistemic diversity and its practitioners shared commitment to depth, context, and interpretation. The authors use data from an ethnographic study of serious illness to construct a model of an ethnoarray and explain how such an array might be linked to data repositories to facilitate new forms of analysis, interpretation, and sharing within scholarly and lay communities. They conclude by discussing some potential implications of the ethnoarray and related approaches for the scope, practice, and forms of ethnography.

biDCG: a new method for discovering global features of DNA microarray data via an iterative re-clustering procedure

Biclustering techniques have become very popular in cancer genetics studies, as they are tools that are expected to connect phenotypes to genotypes, i.e. to identify subgroups of cancer patients based on the fact that they share similar gene expression patterns as well as to identify subgroups of genes that are specific to these subtypes of cancer and therefore could serve as biomarkers. In this paper we propose a new approach for identifying such relationships or biclusters between patients and gene expression profiles. This method, named biDCG, rests on two key concepts. First, it uses a new clustering technique, DCG-tree [Fushing et al, PLos One, 8, e56259 (2013)] that generates ultrametric topological spaces that capture the geometries of both the patient data set and the gene data set. Second, it optimizes the definitions of bicluster membership through an iterative two-way reclustering procedure in which patients and genes are reclustered in turn, based respectively on subsets of genes and patients defined in the previous round. We have validated biDCG on simulated and real data. Based on the simulated data we have shown that biDCG compares favorably to other biclustering techniques applied to cancer genomics data. The results on the real data sets have shown that biDCG is able to retrieve relevant biological information.

A Nonparametric Bayesian Model for Local Clustering with Application to Proteomics

We propose a nonparametric Bayesian local clustering (NoB-LoC) approach for heterogeneous data. NoB-LoC implements inference for nested clusters as posterior inference under a Bayesian model. Using protein expression data as an example, the NoB-LoC model defines a protein (column) cluster as a set of proteins that give rise to the same partition of the samples (rows). In other words, the sample partitions are nested within protein clusters. The common clustering of the samples gives meaning to the protein clusters. Any pair of samples might belong to the same cluster for one protein set but to different clusters for another protein set. These local features are different from features obtained by global clustering approaches such as hierarchical clustering, which create only one partition of samples that applies for all the proteins in the data set. In addition, the NoB-LoC model is different from most other local or nested clustering methods, which define clusters based on common parameters in the sampling model. As an added and important feature, the NoB-LoC method probabilistically excludes sets of irrelevant proteins and samples that do not meaningfully co-cluster with other proteins and samples, thus improving the inference on the clustering of the remaining proteins and samples. Inference is guided by a joint probability model for all the random elements. We provide a simulation study and a motivating example to demonstrate the unique features of the NoB-LoC model.

Clustering gene expression data using a diffraction-inspired framework

Background

The recent developments in microarray technology has allowed for the simultaneous measurement of gene expression levels. The large amount of captured data challenges conventional statistical tools for analysing and finding inherent correlations between genes and samples. The unsupervised clustering approach is often used, resulting in the development of a wide variety of algorithms. Typical clustering algorithms require selecting certain parameters to operate, for instance the number of expected clusters, as well as defining a similarity measure to quantify the distance between data points. The diffraction‐based clustering algorithm however is designed to overcome this necessity for user‐defined parameters, as it is able to automatically search the data for any underlying structure.

Methods

The diffraction‐based clustering algorithm presented in this paper is tested using five well‐known expression datasets pertaining to cancerous tissue samples. The clustering results are then compared to those results obtained from conventional algorithms such as the k‐means, fuzzy c‐means, self‐organising map, hierarchical clustering algorithm, Gaussian mixture model and density‐based spatial clustering of applications with noise (DBSCAN). The performance of each algorithm is measured using an average external criterion and an average validity index.

Results

The diffraction‐based clustering algorithm is shown to be independent of the number of clusters as the algorithm searches the feature space and requires no form of parameter selection. The results show that the diffraction‐based clustering algorithm performs significantly better on the real biological datasets compared to the other existing algorithms.

Conclusion

The results of the diffraction‐based clustering algorithm presented in this paper suggest that the method can provide researchers with a new tool for successfully analysing microarray data.

Nucleoid-enriched proteomes in developing plastids and chloroplasts from maize leaves: a new conceptual framework for nucleoid functions

Plastids contain multiple copies of the plastid chromosome, folded together with proteins and RNA into nucleoids. The degree to which components of the plastid gene expression and protein biogenesis machineries are nucleoid associated, and the factors involved in plastid DNA organization, repair, and replication, are poorly understood. To provide a conceptual framework for nucleoid function, we characterized the proteomes of highly enriched nucleoid fractions of proplastids and mature chloroplasts isolated from the maize (Zea mays) leaf base and tip, respectively, using mass spectrometry. Quantitative comparisons with proteomes of unfractionated proplastids and chloroplasts facilitated the determination of nucleoid-enriched proteins. This nucleoid-enriched proteome included proteins involved in DNA replication, organization, and repair as well as transcription, mRNA processing, splicing, and editing. Many proteins of unknown function, including pentatricopeptide repeat (PPR), tetratricopeptide repeat (TPR), DnaJ, and mitochondrial transcription factor (mTERF) domain proteins, were identified. Strikingly, 70S ribosome and ribosome assembly factors were strongly overrepresented in nucleoid fractions, but protein chaperones were not. Our analysis strongly suggests that mRNA processing, splicing, and editing, as well as ribosome assembly, take place in association with the nucleoid, suggesting that these processes occur cotranscriptionally. The plastid developmental state did not dramatically change the nucleoid-enriched proteome but did quantitatively shift the predominating function from RNA metabolism in undeveloped plastids to translation and homeostasis in chloroplasts. This study extends the known maize plastid proteome by hundreds of proteins, including more than 40 PPR and mTERF domain proteins, and provides a resource for targeted studies on plastid gene expression. Details of protein identification and annotation are provided in the Plant Proteome Database.

CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms

MOTIVATION

Data clustering techniques are an essential component of a good data analysis toolbox. Many current bioinformatics applications are inherently compute-intense and work with very large datasets. Sequential algorithms are inadequate for providing the necessary performance. For this reason, we have created Clustering Algorithms for Massively Parallel Architectures, Including GPU Nodes (CAMPAIGN), a central resource for data clustering algorithms and tools that are implemented specifically for execution on massively parallel processing architectures.

RESULTS

CAMPAIGN is a library of data clustering algorithms and tools, written in 'C for CUDA' for Nvidia GPUs. The library provides up to two orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source resource. New modules from the community will be accepted into the library and the layout of it is such that it can easily be extended to promising future platforms such as OpenCL.

AVAILABILITY

Releases of the CAMPAIGN library are freely available for download under the LGPL from https://simtk.org/home/campaign. Source code can also be obtained through anonymous subversion access as described on https://simtk.org/scm/?group_id=453.

CONTACT

kjk33@cantab.net.

Large-scale label-free quantitative proteomics of the pea aphid-Buchnera symbiosis

Many insects are nutritionally dependent on symbiotic microorganisms that have tiny genomes and are housed in specialized host cells called bacteriocytes. The obligate symbiosis between the pea aphid Acyrthosiphon pisum and the γ-proteobacterium Buchnera aphidicola (only 584 predicted proteins) is particularly amenable for molecular analysis because the genomes of both partners have been sequenced. To better define the symbiotic relationship between this aphid and Buchnera, we used large-scale, high accuracy tandem mass spectrometry (nanoLC-LTQ-Orbtrap) to identify aphid and Buchnera proteins in the whole aphid body, purified bacteriocytes, isolated Buchnera cells and the residual bacteriocyte fraction. More than 1900 aphid and 400 Buchnera proteins were identified. All enzymes in amino acid metabolism annotated in the Buchnera genome were detected, reflecting the high (68%) coverage of the proteome and supporting the core function of Buchnera in the aphid symbiosis. Transporters mediating the transport of predicted metabolites were present in the bacteriocyte. Label-free spectral counting combined with hierarchical clustering, allowed to define the quantitative distribution of a subset of these proteins across both symbiotic partners, yielding no evidence for the selective transfer of protein among the partners in either direction. This is the first quantitative proteome analysis of bacteriocyte symbiosis, providing a wealth of information about molecular function of both the host cell and bacterial symbiont.

Identification of a gene regulatory network necessary for the initiation of oligodendrocyte differentiation

Differentiation of oligodendrocyte progenitor cells (OPCs) into mature oligodendrocytes requires extensive changes in gene expression, which are partly mediated by post-translational modifications of nucleosomal histones. An essential modification for oligodendrocyte differentiation is the removal of acetyl groups from lysine residues which is catalyzed by histone deacetylases (HDACs). The transcriptional targets of HDAC activity within OPCs however, have remained elusive and have been identified in this study by interrogating the oligodendrocyte transcriptome. Using a novel algorithm that allows clustering of gene transcripts according to expression kinetics and expression levels, we defined major waves of co-regulated genes. The initial overall decrease in gene expression was followed by the up-regulation of genes involved in lipid metabolism and myelination. Functional annotation of the down-regulated gene clusters identified transcripts involved in cell cycle regulation, transcription, and RNA processing. To define whether these genes were the targets of HDAC activity, we cultured rat OPCs in the presence of trichostatin A (TSA), an HDAC inhibitor previously shown to inhibit oligodendrocyte differentiation. By overlaying the defined oligodendrocyte transcriptome with the list of 'TSA sensitive' genes, we determined that a high percentage of 'TSA sensitive' genes are part of a normal program of oligodendrocyte differentiation. TSA treatment increased the expression of genes whose down-regulation occurs very early after induction of OPC differentiation, but did not affect the expression of genes with a slower kinetic. Among the increased 'TSA sensitive' genes we detected several transcription factors including Id2, Egr1, and Sox11, whose down-regulation is critical for OPC differentiation. Thus, HDAC target genes include clusters of co-regulated genes involved in transcriptional repression. These results support a de-repression model of oligodendrocyte lineage progression that relies on the concurrent down-regulation of several inhibitors of differentiation.

Structural and metabolic transitions of C4 leaf development and differentiation defined by microscopy and quantitative proteomics in maize

C(4) grasses, such as maize (Zea mays), have high photosynthetic efficiency through combined biochemical and structural adaptations. C(4) photosynthesis is established along the developmental axis of the leaf blade, leading from an undifferentiated leaf base just above the ligule into highly specialized mesophyll cells (MCs) and bundle sheath cells (BSCs) at the tip. To resolve the kinetics of maize leaf development and C(4) differentiation and to obtain a systems-level understanding of maize leaf formation, the accumulation profiles of proteomes of the leaf and the isolated BSCs with their vascular bundle along the developmental gradient were determined using large-scale mass spectrometry. This was complemented by extensive qualitative and quantitative microscopy analysis of structural features (e.g., Kranz anatomy, plasmodesmata, cell wall, and organelles). More than 4300 proteins were identified and functionally annotated. Developmental protein accumulation profiles and hierarchical cluster analysis then determined the kinetics of organelle biogenesis, formation of cellular structures, metabolism, and coexpression patterns. Two main expression clusters were observed, each divided in subclusters, suggesting that a limited number of developmental regulatory networks organize concerted protein accumulation along the leaf gradient. The coexpression with BSC and MC markers provided strong candidates for further analysis of C(4) specialization, in particular transporters and biogenesis factors. Based on the integrated information, we describe five developmental transitions that provide a conceptual and practical template for further analysis. An online protein expression viewer is provided through the Plant Proteome Database.

Methods for transcriptomic analyses of the porcine host immune response: application to Salmonella infection using microarrays

Technological developments in both the collection and analysis of molecular genetic data over the past few years have provided new opportunities for an improved understanding of the global response to pathogen exposure. Such developments are particularly dramatic for scientists studying the pig, where tools to measure the expression of tens of thousands of transcripts, as well as unprecedented data on the porcine genome sequence, have combined to expand our abilities to elucidate the porcine immune system. In this review, we describe these recent developments in the context of our work using primarily microarrays to explore gene expression changes during infection of pigs by Salmonella. Thus while the focus is not a comprehensive review of all possible approaches, we provide links and information on both the tools we use as well as alternatives commonly available for transcriptomic data collection and analysis of porcine immune responses. Through this review, we expect readers will gain an appreciation for the necessary steps to plan, conduct, analyze and interpret the data from transcriptomic analyses directly applicable to their research interests.

Comparative analysis of acute and chronic corticosteroid pharmacogenomic effects in rat liver: transcriptional dynamics and regulatory structures

Background

Comprehensively understanding corticosteroid pharmacogenomic effects is an essential step towards an insight into the underlying molecular mechanisms for both beneficial and detrimental clinical effects. Nevertheless, even in a single tissue different methods of corticosteroid administration can induce different patterns of expression and regulatory control structures. Therefore, rich in vivo datasets of pharmacological time-series with two dosing regimens sampled from rat liver are examined for temporal patterns of changes in gene expression and their regulatory commonalities.

Results

The study addresses two issues, including (1) identifying significant transcriptional modules coupled with dynamic expression patterns and (2) predicting relevant common transcriptional controls to better understand the underlying mechanisms of corticosteroid adverse effects. Following the orientation of meta-analysis, an extended computational approach that explores the concept of agreement matrix from consensus clustering has been proposed with the aims of identifying gene clusters that share common expression patterns across multiple dosing regimens as well as handling challenges in the analysis of microarray data from heterogeneous sources, e.g. different platforms and time-grids in this study. Six significant transcriptional modules coupled with typical patterns of expression have been identified. Functional analysis reveals that virtually all enriched functions (gene ontologies, pathways) in these modules are shown to be related to metabolic processes, implying the importance of these modules in adverse effects under the administration of corticosteroids. Relevant putative transcriptional regulators (e.g. RXRF, FKHD, SP1F) are also predicted to provide another source of information towards better understanding the complexities of expression patterns and the underlying regulatory mechanisms of those modules.

Conclusions

We have proposed a framework to identify significant coexpressed clusters of genes across multiple conditions experimented from different microarray platforms, time-grids, and also tissues if applicable. Analysis on rich in vivo datasets of corticosteroid time-series yielded significant insights into the pharmacogenomic effects of corticosteroids, especially the relevance to metabolic side-effects. This has been illustrated through enriched metabolic functions in those transcriptional modules and the presence of GRE binding motifs in those enriched pathways, providing significant modules for further analysis on pharmacogenomic corticosteroid effects.

Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma

We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z-score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b-high/CD10-low/CD221-high) and a second group clustering close to fibroblasts (CD49b-low/CD10-high/CD221-low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes.

A semi-parametric Bayesian model for unsupervised differential co-expression analysis

Background

Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples.

Results

We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples) with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ERα regulatory network.

Conclusions

We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.

Using a state-space model and location analysis to infer time-delayed regulatory networks

Computational gene regulation models provide a means for scientists to draw biological inferences from time-course gene expression data. Based on the state-space approach, we developed a new modeling tool for inferring gene regulatory networks, called time-delayed Gene Regulatory Networks (tdGRNs). tdGRN takes time-delayed regulatory relationships into consideration when developing the model. In addition, a priori biological knowledge from genome-wide location analysis is incorporated into the structure of the gene regulatory network. tdGRN is evaluated on both an artificial dataset and a published gene expression data set. It not only determines regulatory relationships that are known to exist but also uncovers potential new ones. The results indicate that the proposed tool is effective in inferring gene regulatory relationships with time delay. tdGRN is complementary to existing methods for inferring gene regulatory networks. The novel part of the proposed tool is that it is able to infer time-delayed regulatory relationships.

Unsupervised selection of highly coexpressed and noncoexpressed genes using a consensus clustering approach

In this paper we explore the concept of consensus clustering to identify, within a set of differentially expressed genes, a subset of genes that are either highly coexpressed or highly noncoexpressed based on the hypothesis that this subset would serve as a better starting point for further analyses. A number of core clustering methods form the basis for the assertion of an agreement matrix (AM) characterizing the level of coexpression between any two probesets. In order to overcome the limitations of using a single distance metric, we explore different metrics and examine the sensitivity of the AM as a function of the input number of clusters to find a suggestive number of clusters that best describes a particular dataset. The result of this level of analysis is a systematic framework for eliminating probesets that cannot be clearly characterized as either coexpressed or noncoexpressed with others, thus eliminating a number of probesets from further analysis. Subsequently, an agglomerative hierarchical clustering approach is applied to cluster the selected subset using the agreement metric information as the similarity measure. Thus, the goal of the proposed methodology is twofold: (1) we opt to identify a more "clusterable" subset of the original set; and (2) we aim at further refining the subset in order to identify a core of genes that contains genes that are either coexpressed or noncoexpressed within a certain confidence level. The approach is tested with a number of data sets, both synthetic and real, and it is demonstrated that it is successful in identifying more clusterable, also hypothesized to be more biologically relevant, subsets of genes and expression profiles.

Somatic, germline and sex hierarchy regulated gene expression during Drosophila metamorphosis

Background

Drosophila melanogaster undergoes a complete metamorphosis, during which time the larval male and female forms transition into sexually dimorphic, reproductive adult forms. To understand this complex morphogenetic process at a molecular-genetic level, whole genome microarray analyses were performed.

Results

The temporal gene expression patterns during metamorphosis were determined for all predicted genes, in both somatic and germline tissues of males and females separately. Temporal changes in transcript abundance for genes of known functions were found to correlate with known developmental processes that occur during metamorphosis. We find that large numbers of genes are sex-differentially expressed in both male and female germline tissues, and relatively few are sex-differentially expressed in somatic tissues. The majority of genes with somatic, sex-differential expression were found to be expressed in a stage-specific manner, suggesting that they mediate discrete developmental events. The Sex-lethal paralog, CG3056, displays somatic, male-biased expression at several time points in metamorphosis. Gene expression downstream of the somatic, sex determination genes transformer and doublesex (dsx) was examined in two-day old pupae, which allowed for the identification of genes regulated as a consequence of the sex determination hierarchy. These include the homeotic gene abdominal A, which is more highly expressed in females as compared to males, as a consequence of dsx. For most genes regulated downstream of dsx during pupal development, the mode of regulation is distinct from that observed for the well-studied direct targets of DSX, Yolk protein 1 and 2.

Conclusion

The data and analyses presented here provide a comprehensive assessment of gene expression during metamorphosis in each sex, in both somatic and germline tissues. Many of the genes that underlie critical developmental processes during metamorphosis, including sex-specific processes, have been identified. These results provide a framework for further functional studies on the regulation of sex-specific development.

Microarray experiments to uncover Toll-like receptor function

This chapter is intended as a handbook for anyone interested in using microarrays to study Toll-like receptor (TLR) function or any other biological question. Although microarray technology has developed into a standard tool at many laboratories disposal, most of the actual microarray processing is done by core facilities using highly specialized equipment. This chapter only briefly describes these methods in principle and instead focus on the parts that investigators themselves can influence, such as the experimental design, RNA isolation, statistical analysis, cluster analysis, data visualization, and biological interpretation.

The DIURNAL project: DIURNAL and circadian expression profiling, model-based pattern matching, and promoter analysis

The DIURNAL project ( http://diurnal.cgrb.oregonstate.edu/ ) provides a graphical interface for mining and viewing diurnal and circadian microarray data for Arabidopsis thaliana, poplar, and rice. The database is searchable and provides access to several user-friendly Web-based data-mining tools with easy-to-understand output. The associated tools include HAYSTACK ( http://haystack.cgrb.oregonstate.edu/ ) and ELEMENT ( http://element.cgrb.oregonstate.edu/ ). HAYSTACK is a model-based pattern-matching algorithm for identifying genes that are coexpressed and potentially coregulated. HAYSTACK can be used to analyze virtually any large-scale microarray data set and provides an alternative method for clustering microarray data from any experimental system by grouping together genes whose expression patterns match the same or similar user-defined patterns. ELEMENT is a Web-based program for identifying potential cis-regulatory elements in the promoters of coregulated genes in Arabidopsis, poplar, and rice. Together, DIURNAL, HAYSTACK, and ELEMENT can be used to facilitate cross-species comparisons among the plant species supported and to accelerate functional genomics efforts in the laboratory.

Genome-wide studies in thyroid neoplasia

There is much interest in the application of genome biology to the field of thyroid neoplasia, despite the relatively low mortality rate associated with thyroid cancer in general. The principal reason for this interest is that the field of thyroid neoplasia stands to benefit from the application of genomic information to address a variety of pathologic and clinical issues. In addition to practical patient care issues, there is an excellent opportunity of expand the basic understanding of thyroid carcinogenesis. In this article, the most relevant genomic work on thyroid tumors performed to date is reviewed along with some general comments about the potential impact of genomic biology on thyroid pathology and the management of patients with thyroid nodules and cancer.

Probing genetic algorithms for feature selection in comprehensive metabolic profiling approach

Six different clones of 1-year-old loblolly pine (Pinus taeda L.) seedlings grown under standardized conditions in a green house were used for sample preparation and further analysis. Three independent and complementary analytical techniques for metabolic profiling were applied in the present study: hydrophilic interaction chromatography (HILIC-LC/ESI-MS), reversed-phase liquid chromatography (RP-LC/ESI-MS), and gas chromatography all coupled to mass spectrometry (GC/TOF-MS). Unsupervised methods, such as principle component analysis (PCA) and clustering, and supervised methods, such as classification, were used for data mining. Genetic algorithms (GA), a multivariate approach, was probed for selection of the smallest subsets of potentially discriminative classifiers. From more than 2000 peaks found in total, small subsets were selected by GA as highly potential classifiers allowing discrimination among six investigated genotypes. Annotated GC/TOF-MS data allowed the generation of a small subset of identified metabolites. LC/ESI-MS data and small subsets require further annotation. The present study demonstrated that combination of comprehensive metabolic profiling and advanced data mining techniques provides a powerful metabolomic approach for biomarker discovery among small molecules. Utilizing GA for feature selection allowed the generation of small subsets of potent classifiers.