Gene-level Data Research Articles

Extending the R Library PROPER to Enable Power Calculations for Isoform-Level Analysis with EBSeq.

RNA-Sequencing (RNA-Seq) has become a routine technology for investigating gene expression differences in comparative transcriptomic studies. Differential expression (DE) analysis of the isoforms of genes is just emerging now that expression (read counts) can be estimated with higher accuracy at the isoform level. Estimating the statistical power that can be achieved with a specific number of repeats is a key step in RNA-Seq analysis. The R library proper was developed to provide realistic empirical power analysis. However, proper uses differential expression methods more suited for power calculation of gene-level expression data. We propose extensions to this tool that would allow for power analysis which takes into account the specificities of isoforms expression. This was achieved by enabling the use of EBSeq, a DE approach well-tailored for isoform-level expression, as an additional analysis method within PROPER. The new extensions and exemplar code for their usage are freely available online at: https://github.com/agaye/proper_extension

Convergent evidence for the molecular basis of musical traits.

To obtain aggregate evidence for the molecular basis of musical abilities and the effects of music, we integrated gene-level data from 105 published studies across multiple species including humans, songbirds and several other animals and used a convergent evidence method to prioritize the top candidate genes. Several of the identified top candidate genes like EGR1, FOS, ARC, BDNF and DUSP1 are known to be activity-dependent immediate early genes that respond to sensory and motor stimuli in the brain. Several other top candidate genes like MAPK10, SNCA, ARHGAP24, TET2, UBE2D3, FAM13A and NUDT9 are located on chromosome 4q21-q24, on the candidate genomic region for music abilities in humans. Functional annotation analyses showed the enrichment of genes involved in functions like cognition, learning, memory, neuronal excitation and apoptosis, long-term potentiation and CDK5 signaling pathway. Interestingly, all these biological functions are known to be essential processes underlying learning and memory that are also fundamental for musical abilities including recognition and production of sound. In summary, our study prioritized top candidate genes related to musical traits.

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available data sets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

Release of (and lessons learned from mining) a pioneering large toxicogenomics database.

We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds. The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays. The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches. Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.

Tobacco habituated and non-habituated subjects exhibit different mutational spectrums in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common non-skin cancer in the world. Tobacco chewing is implicated with most of the cases of HNSCC but this type of cancer is increasing in non-tobacco chewers as well. This study was instigated to provide comprehensive variant and gene-level data in HNSCC subjects of the Indian population and fill the gap in the literature on comparative assessment of gene mutations in cancer subjects with a habit of tobacco and those without any habit using targeted amplicon sequencing. We performed targeted Amplicon sequencing of 409 tumor suppressor genes and oncogenes, frequently mutated across many cancer types, including head and neck. DNA from primary tumor tissues and matched blood was analyzed for HNSCC patients with a habit of tobacco and those without any habit. PDE4DIP, SYNE1, and NOTCH1 emerged as the highly mutated genes in HNSCC. A total of 39 candidate causal variants in 22 unique cancer driver genes were identified in non-habitual (WoH) and habitual (WH) subjects. Comparison of genes from both the subjects, showed seven unique cancer driver genes (KIT, ATM, RNF213, GATA2, DST, RET, CYP2C19) in WoH, while WH showed five (IL7R, PKHD1, MLL3, PTPRD, MAPK8) and 10 genes (SETD2, ATR, CDKN2A, NCOA4, TP53, SYNE1, KAT6B, THBS1, PTPRT, and FGFR3) were common to both subjects. In addition to this NOTCH1, NOTCH2, and NOTCH4 gene were found to be mutated only in habitual subjects. These findings strongly support a causal role for tobacco, acting via PI3K and MAPK pathway inhibition and stimulation of various genes leading to oncogenic transformations in case of tobacco chewers. In case of non-tobacco chewers it appears that mutations in the pathway affecting the squamous epithelial lineage and DNA repair genes lead to HNSCC. Somatic mutation in CYP2C19 gene in the non-habitual subjects suggests that this gene may have a tobacco independent role in development and progression of HNSCC. In addition to sharing high mutation rate, NOTCH gene family was found to be mutated only in habitual sample. Further, presence of mutated genes not earlier reported to be involved in HNSCC, suggest that the Indian sub-continent may have different sets of genes, as compared to other parts of the world, involved in the development and progression of HNSCC.Electronic supplementary materialThe online version of this article (doi:10.1007/s13205-014-0267-0) contains supplementary material, which is available to authorized users.

Abstract 4361: Target and drug discovery for recalcitrant, rare and neglected cancers.

Abstract Efforts are underway to improve the treatment of recalcitrant, rare and neglected cancers through the discovery of potential therapeutic targets, the identification of possible therapeutic combinations, and the identification of genomic vulnerabilities, using state-of-the-art drug discovery, molecular characterization, and mechanism-of-action techniques. Our current focus is on sarcoma and small cell lung cancer. Sarcoma comprises approximately 1% of cancers inclusive of 50 subtypes and occurs in patients of all ages with a frequency spread evenly over the human age range. Small cell lung cancer (SCLC) constitutes approximately 15% of lung cancers, is extremely aggressive, has a high mortality rate and frequently recurs after conventional cytotoxic chemotherapy. We have established comprehensive panels of human sarcoma and small cell lung cancer cell lines (approximately 80 per tumor type). Cell lines are being screened for response to all of the FDA approved anticancer agents and to a library of investigational agents with a goal of identifying existing agents that may be suitable for novel clinical trials, plus identifying potential points of vulnerability for drug discovery. Analysis of selected somatic mutations showed that ATM is frequently mutated in both soft tissue and bone sarcoma. In addition, gene expression profiles were measured in sarcoma cell lines using Affymetrix Exon 1.0 ST arrays in an effort to facilitate identification of splice variants and fusion genes. The gene-level data from the Exon ST1 array compares well with published U133plus 2 expression profiles. Principal components analysis of genes in 48 sarcoma lines indicates that expression varies primarily by disease subtype. For instance, Ewing's Sarcoma overexpressed genes including PRKCB, NPY5R and NPY1R, ITM2A plus adrenergic receptors B1, B3 and A1D, while ACTC1, IGF2 and CHRNA1 were dysregulated in rhabdomyosarcoma. Predicted associations between gene and miRNA expression included CSF1 with miR-128, thioredoxin reductase with miR-324-5p, MDM4 with miR-152, and PODXL with miR-199a-5p, suggesting potential regulatory relationships that might be exploited as cancer targets. In these lines, exome expression was found to be altered more than 10% in 232 genes, including known splice variant genes such as KLK11, UBE2C and sarcoma related fusion genes EWSR1 and ETV. Calculated fusion scores based on EWSR1-FLI1 expression profiles successfully segregated Ewing's sarcoma from all other sarcoma cell lines. Association analysis between gene and microRNA expression and sensitivity to specific anticancer agents is ongoing. Our mission is to improve the treatment of these neglected cancers by providing a comprehensive public database of molecular signatures and sensitivities that can be leveraged by the scientific community at large. Funded by NCI Contract no. HHSN261200800001E. Citation Format: Anne Monks, Annamaria Rapisarda, Kazimierz O. Wrzeszczynski, E. Michael August, Eric C. Polley, Sudhir B. Kondapaka, Gurmeet Kaur, Dianne Newton, Beverley A. Teicher. Target and drug discovery for recalcitrant, rare and neglected cancers. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4361. doi:10.1158/1538-7445.AM2013-4361

Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

PRD: A protein-RNA interaction database.

Although protein–RNA interactions (PRIs) are involved in various important cellular processes, compiled data on PRIs are still limited. This contrasts with protein–protein interactions, which have been intensively recorded in public databases and subjected to network level analysis. Here, we introduce PRD, an online database of PRIs, dispersed across several sources, including scientific literature. Currently, over 10,000 interactions have been stored in PRD using PSI-MI 2.5, which is a standard model for describing detailed molecular interactions, with an emphasis on gene level data. Users can browse all recorded interactions and execute flexible keyword searches against the database via a web interface. Our database is not only a reference of PRIs, but will also be a valuable resource for studying characteristics of PRI networks.AvailabilityPRD can be freely accessed at http://pri.hgc.jp/

ArrayMap: A Reference Resource for Genomic Copy Number Imbalances in Human Malignancies

BackgroundThe delineation of genomic copy number abnormalities (CNAs) from cancer samples has been instrumental for identification of tumor suppressor genes and oncogenes and proven useful for clinical marker detection. An increasing number of projects have mapped CNAs using high-resolution microarray based techniques. So far, no single resource does provide a global collection of readily accessible oncogenomic array data.Methodology/Principal FindingsWe here present arrayMap, a curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides a platform for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. To date, the resource incorporates more than 40,000 arrays in 224 cancer types extracted from several resources, including the NCBI’s Gene Expression Omnibus (GEO), EBI’s ArrayExpress (AE), The Cancer Genome Atlas (TCGA), publication supplements and direct submissions. For the majority of the included datasets, probe level and integrated visualization facilitate gene level and genome wide data review. Results from multi-case selections can be connected to downstream data analysis and visualization tools.Conclusions/SignificanceTo our knowledge, currently no data source provides an extensive collection of high resolution oncogenomic CNA data which readily could be used for genomic feature mining, across a representative range of cancer entities. arrayMap represents our effort for providing a long term platform for oncogenomic CNA data independent of specific platform considerations or specific project dependence. The online database can be accessed at http//www.arraymap.org.

Abstract 1607: Airway gene expression alterations associated with lung cancer chemoprevention using green tea extract

Abstract Lung cancer is the leading cause of cancer death in the United States and in the world. Identifying an effective chemopreventive agent for lung cancer would reduce lung cancer mortality by reversing, preventing, or delaying carcinogenic progression. Several randomized, controlled lung cancer chemoprevention trials have produced neutral or harmful results; with the exception of oral iloprost that demonstrated a small, but significant improvement in histology after 6 months of treatment in former smokers. One of the challenges in lung cancer chemoprevention trials have been the lack of surrogate endpoints to establish drug efficacy. In this study we use molecular profiling to study alterations assoicated with changes in airway histology and treatment with a promising chemopreventative, green tea extract. Airway epithelial cells were collected from patients with bronchial dysplasia during bronchoscopy at baseline, on-treatment, and post-treatment with green tea extract or placebo ranging from 2 to 6 months (n=27 patients, n=63 samples). RNA from the samples was processed and hybridized to Affymetrix Human Gene 1.0 ST arrays. Gene-level expression data was obtained using the Robust Multiarray Average (RMA) algorithm and ANOVA and linear modeling strategies were used to identify gene expression alterations associated with dysplasia regression and green tea extract treatment. Cancer and smoking-related pathway gene expression signatures were used to predict the pathway activation of each sample using a binary regression model. Gene set enrichment analysis (GSEA) was used to identify important biological pathways. Airway gene expression alterations associated with dysplasia regression were identified and pathways such as p53 and mTOR signaling were enriched among genes up-regulated in airways with dysplasia. The E2F3 oncogenic pathway was also found to be significantly altered in airways with dysplasia (p<0.05). The effect of green tea extract on airway gene expression was more pronounced among former versus current smokers. As a result, the degree to which pathways were modulated by green tea extract varied with smoking status. Pathways related to metabolism of xenobiotics, and glutathione, and retinol were decreased among treated current smokers while genes related to oxidative phosphorylation and DNA repair were increased in treated former smokers (FDR q-value<0.05). Our studies suggest that airway gene expression is altered in high-risk smokers with premalignant airway lesions and that this airway “field of injury” can be modulated by treatment with green tea extract especially among former smokers. We are currently investigating whether airway gene-expression can serve as an intermediate biomarker of response to green tea extract and identify those smokers who are most likely to benefit from this type of chemopreventive strategy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1607. doi:1538-7445.AM2012-1607

Abstract 3697: Phase II study of therapy selected by molecular profiling in patients with previously treated metastatic pancreatic cancer - SU2C-001

Abstract We have completed a second line Phase II study of therapy to be selected by molecular profiling in 35 patients with previously treated metastatic pancreatic cancer. On this study all patients have a biopsy of a metastatic lesion (usually liver). Therapeutic targets are identified by profiling the metastatic biopsies with the combined use of immunohistochemistry (IHC), comparative genomic hybridization (CGH) on sorted tumor populations, and correlating gene expression microarray data to that of a panel of xenografts for which drug sensitivity has been previously determined. The IHC assays are performed in a CLIA certified laboratory and include the following therapeutic targets: MRP1, TOPO1, MGMT, PGP, Her2/Neu, PTEN, c-kit, RRM1, EGFR, BCRP, PDGFR, TOP2A, TS, ERCC1, SPARC, ER, PR, and AR. In addition each sample is screened by PCR for the presence of the most common K-RAS mutations. Gene expression microarray analysis is performed at John Hopkins University using Affymetrix U133 Plus 2.0 gene arrays. For CGH analysis flow sorted nuclei of diploid and aneuploid tumor cell populations are processed and hybridized to 400,000 feature CGH arrays. We have identified distinct high level focal amplicons targeting AKT2, LYN, RET, CDK6, K-RAS, MYCBP, BCL11A, RASA1 and WNT6, and gene specific homozygous deletions including CDKN2A, PTEN, MAP2K4, RASA1 and PARK2 in the sorted aneuploid tumor nuclei. In an effort to elucidate new targets and potential contexts of vulnerability based on the results of this trial, we have extracted combined gene level data for each patient and present these mapped to 32 pancreatic specific pathways and processes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3697. doi:1538-7445.AM2012-3697

A Model-Based Analysis to Infer the Functional Content of a Gene List

An important challenge in statistical genomics concerns integrating experimental data with exogenous information about gene function. A number of statistical methods are available to address this challenge, but most do not accommodate complexities in the functional record. To infer activity of a functional category (e.g., a gene ontology term), most methods use gene-level data on that category, but do not use other functional properties of the same genes. Not doing so creates undue errors in inference. Recent developments in model-based category analysis aim to overcome this difficulty, but in attempting to do so they are faced with serious computational problems. This paper investigates statistical properties and the structure of posterior computation in one such model for the analysis of functional category data. We examine the graphical structures underlying posterior computation in the original parameterization and in a new parameterization aimed at leveraging elements of the model. We characterize identifiability of the underlying activation states, describe a new prior distribution, and introduce approximations that aim to support numerical methods for posterior inference.

On differential gene expression using RNA-Seq data.

Motivation:RNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes. Most published methods collapse the position-level read data into a single gene-specific expression measurement. Statistical inference proceeds by modeling these gene-level expression measurements.Results:We present a Bayesian method of calling differential expression (BM-DE) that directly models the position-level read counts. We demonstrate the potential advantage of the BM-DE method compared to existing approaches that rely on gene-level aggregate data. An important additional feature of the proposed approach is that BM-DE can be used to analyze RNA-Seq data from experiments without biological replicates. This becomes possible since the approach works with multiple position-level read counts for each gene. We demonstrate the importance of modeling for position-level read counts with a yeast data set and a simulation study.Availability:A public domain R package is available from http://odin.mdacc.tmc.edu/~ylji/BMDE/.

Large-Scale Analysis of Gene Expression and Connectivity in the Rodent Brain: Insights through Data Integration

Recent research in C. elegans and the rodent has identified correlations between gene expression and connectivity. Here we extend this type of approach to examine complex patterns of gene expression in the rodent brain in the context of regional brain connectivity and differences in cellular populations. Using multiple large-scale data sets obtained from public sources, we identified two novel patterns of mouse brain gene expression showing a strong degree of anti-correlation, and relate this to multiple data modalities including macroscale connectivity. We found that these signatures are associated with differences in expression of neuronal and oligodendrocyte markers, suggesting they reflect regional differences in cellular populations. We also find that the expression level of these genes is correlated with connectivity degree, with regions expressing the neuron-enriched pattern having more incoming and outgoing connections with other regions. Our results exemplify what is possible when increasingly detailed large-scale cell- and gene-level data sets are integrated with connectivity data.

A Method to Detect Differential Gene Expression in Cross-Species Hybridization Experiments at Gene and Probe Level

MotivationWhole genome microarrays are increasingly becoming the method of choice to study responses in model organisms to disease, stressors or other stimuli. However, whole genome sequences are available for only some model organisms, and there are still many species whose genome sequences are not yet available. Cross-species studies, where arrays developed for one species are used to study gene expression in a closely related species, have been used to address this gap, with some promising results. Current analytical methods have included filtration of some probes or genes that showed low hybridization activities. But consensus filtration schemes are still not available.ResultsA novel masking procedure is proposed based on currently available target species sequences to filter out probes and study a cross-species data set using this masking procedure and gene-set analysis. Gene-set analysis evaluates the association of some priori defined gene groups with a phenotype of interest. Two methods, Gene Set Enrichment Analysis (GSEA) and Test of Test Statistics (ToTS) were investigated. The results showed that masking procedure together with ToTS method worked well in our data set. The results from an alternative way to study cross-species hybridization experiments without masking are also presented. We hypothesize that the multi-probes structure of Affymetrix microarrays makes it possible to aggregate the effects of both well-hybridized and poorly-hybridized probes to study a group of genes. The principles of gene-set analysis were applied to the probe-level data instead of gene-level data. The results showed that ToTS can give valuable information and thus can be used as a powerful technique for analyzing cross-species hybridization experiments.AvailabilitySoftware in the form of R code is available at http://anson.ucdavis.edu/~ychen/cross-species.htmlSupplementary DataSupplementary data are available at http://anson.ucdavis.edu/~ychen/cross-species.html

Cross-platform expression microarray performance in a mouse model of mitochondrial disease therapy

Microarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3′-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway-level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses. Methods: Liver RNA from B6.Alb/cre,Pdss2loxP/loxP mice having primary coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA. Results: Expression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with ‘steroid biosynthesis’ consistently identified as the most down-regulated metabolic pathway by probucol treatment. Conclusions: This work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges.

COLdb, a database linking genetic data to molecular function in fibrillar collagens

Fibrillar collagens are ubiquitous proteins essential for the structural integrity of bones, skin, blood vessels, and other tissues. Mutations in collagen genes result in disorders including osteogenesis imperfecta, chondrodysplasias, and Ehlers-Danlos syndromes, but the molecular basis for the heterogeneity of clinical phenotypes is not well understood. A more complete understanding of the relationship between sequence and phenotype requires synthesis of multiple facets of collagen structure and function. To facilitate such an analysis, we developed COLdb, a freely available database integrating collagen biological and physicochemical properties with known variants. A Web-based, interactive, graphical user interface displays the data as annotations on the collagen protein sequences. Collagen gene-level data are provided as custom tracks for display in the UCSC genome browser. COLdb currently includes 35,582 data points spanning collagen types I, II, and III, and, importantly, users can add their own data to the display. The database is the first comprehensive integration of disparate functional information on the three major fibrillar collagens, and the first electronic collection of mutations in the COL2A1 gene.

Detection of TMPRSS2-ERG Translocations in Human Prostate Cancer by Expression Profiling Using GeneChip Human Exon 1.0 ST Arrays

Translocation of TMPRSS2 to the ERG gene, found in a high proportion of human prostate cancer, results in overexpression of the 3'-ERG sequences joined to the 5'-TMPRSS2 promoter. The studies presented here were designed to test the ability of expression analysis on GeneChip Human Exon 1.0 ST arrays to detect 5'-TMPRSS2-ERG-3' hybrid transcripts encoded by this translocation. Monitoring the relative expression of each ERG exon revealed altered transcription of the ERG gene in 15 of a series of 27 prostate cancer samples. In all cases, exons 4 to 11 exhibited enhanced expression compared with exons 2 and 3. This pattern of expression indicated that the most abundant hybrid transcripts involve fusions to ERG exon 4, and RT-PCR analyses confirmed the joining of TMPRSS2 exon 1 to ERG exon 4 in all 15 cases. The exon expression patterns also indicated that TMPRSS2-ERG fusion transcripts commonly contain deletion of ERG exon 8. Analysis of gene-level data from the arrays allowed the identification of genes whose expression levels significantly correlated with the presence of the translocation. These studies demonstrate that expression analyses using exon arrays represent a valuable approach for detecting ETS gene translocation in prostate cancer, in parallel with analyses of gene expression profiles.