Strategy for encoding and comparison of gene expression signatures

MOASL: Predicting drug mechanism of actions through similarity learning with transcriptomic signature

Understanding the mechanisms of actions (MOAs) of compounds is crucial in drug discovery. A common step in drug MOAs annotation is to query the dysregulated gene signatures induced by drugs in a reference library of pre-defined signatures. However, traditional similarity-based computational strategies face challenges when dealing with high-dimensional and noisy transcriptional signature data. To address this issue, we introduce MOASL (MOAs prediction via Similarity Learning), a novel approach that contrastive to learn similarity embeddings among signatures with shared MOAs automatically. We evaluated the accuracy of signature matching on various transcriptional activity score (TAS) datasets and individual cell lines by using MOASL. The results show MOASL achieved higher performance over several statistical and machine learning methods. Furthermore, we provided the rationale of our model by visualizing the signature annotation procedure. Using MOASL, the MOAs label of query signature could be conveniently defined by calculating the similarity between the query embedding and the reference embeddings. Finally, we applied MOASL to repurpose thousands of compounds as glucocorticoid receptor (GR) agonists, accurately identifying 8 out of the top 10 compounds. MOASL is conveniently accessible on GitHub at https://github.com/jianglikun/MOASL, empowering researchers and practitioners in the field of drug discovery to predict the MOAs of drug.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

A comprehensive evaluation of connectivity methods for L1000 data

The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, using the drug–drug similarities from the Drug Repurposing Hub database as a benchmark standard, we evaluated six popular published methods for the prediction performance of drug–drug relationships based on the partial area under the receiver operating characteristic (ROC) curve at false positive rates of 0.001, 0.005 and 0.01 (AUC0.001, AUC0.005 and AUC0.01). The similarity evaluating algorithm called ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200. Further, we tested these methods with an experimentally derived gene signature related to estrogen in breast cancer cells, and the results confirmed that ZhangScore was more accurate than other methods. Moreover, based on scoring results of ZhangScore for the gene signature of TOP2A knockdown, in addition to well-known TOP2A inhibitors, we identified a number of potential inhibitors and at least two of them were the subject of previous investigation. Our studies provide potential guidelines for researchers to choose the suitable connectivity method. The six connectivity methods used in this report have been implemented in R package (https://github.com/Jasonlinchina/RCSM).

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Identification of a gene-expression predictor for diagnosis and personalized stratification of lupus patients

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a wide spectrum of clinical manifestations and degrees of severity. Few genomic biomarkers for SLE have been validated and employed to inform clinical classifications and decisions. To discover and assess the gene-expression based SLE predictors in published studies, we performed a meta-analysis using our established signature database and a data similarity-driven strategy. From 13 training data sets on SLE gene-expression studies, we identified a SLE meta-signature (SLEmetaSig100) containing 100 concordant genes that are involved in DNA sensors and the IFN signaling pathway. We rigorously examined SLEmetaSig100 with both retrospective and prospective validation in two independent data sets. Using unsupervised clustering, we retrospectively elucidated that SLEmetaSig100 could classify clinical samples into two groups that correlated with SLE disease status and disease activities. More importantly, SLEmetaSig100 enabled personalized stratification demonstrating its ability to prospectively predict SLE disease at the individual patient level. To evaluate the performance of SLEmetaSig100 in predicting SLE, we predicted 1,171 testing samples to be either non-SLE or SLE with positive predictive value (97–99%), specificity (85%-84%), and sensitivity (60–84%). Our study suggests that SLEmetaSig100 has enhanced predictive value to facilitate current SLE clinical classification and provides personalized disease activity monitoring.

SigMat: a classification scheme for gene signature matching

Motivation

Several large-scale efforts have been made to collect gene expression signatures from a variety of biological conditions, such as response of cell lines to treatment with drugs, or tumor samples with different characteristics. These gene signature collections are utilized through bioinformatics tools for 'signature matching', whereby a researcher studying an expression profile can identify previously cataloged biological conditions most related to their profile. Signature matching tools typically retrieve from the collection the signature that has highest similarity to the user-provided profile. Alternatively, classification models may be applied where each biological condition in the signature collection is a class label; however, such models are trained on the collection of available signatures and may not generalize to the novel cellular context or cell line of the researcher's expression profile.

Results

We present an advanced multi-way classification algorithm for signature matching, called SigMat, that is trained on a large signature collection from a well-studied cellular context, but can also classify signatures from other cell types by relying on an additional, small collection of signatures representing the target cell type. It uses these 'tuning data' to learn two additional parameters that help adapt its predictions for other cellular contexts. SigMat outperforms other similarity scores and classification methods in identifying the correct label of a query expression profile from as many as 244 or 500 candidate classes (drug treatments) cataloged by the LINCS L1000 project. SigMat retains its high accuracy in cross-cell line applications even when the amount of tuning data is severely limited.

Availability and implementation

SigMat is available on GitHub at https://github.com/JinfengXiao/SigMat.

Supplementary information

Supplementary data are available at Bioinformatics online.

Neonatal Metabolomic Profiles Related to Prenatal Arsenic Exposure

Prenatal inorganic arsenic (iAs) exposure is associated with health effects evident at birth and later in life. An understanding of the relationship between prenatal iAs exposure and alterations in the neonatal metabolome could reveal critical molecular modifications, potentially underpinning disease etiologies. In this study, nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was used to identify metabolites in neonate cord serum associated with prenatal iAs exposure in participants from the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort, in Gómez Palacio, Mexico. Through multivariable linear regression, ten cord serum metabolites were identified as significantly associated with total urinary iAs and/or iAs metabolites, measured as %iAs, %monomethylated arsenicals (MMAs), and %dimethylated arsenicals (DMAs). A total of 17 metabolites were identified as significantly associated with total iAs and/or iAs metabolites in cord serum. These metabolites are indicative of changes in important biochemical pathways such as vitamin metabolism, the citric acid (TCA) cycle, and amino acid metabolism. These data highlight that maternal biotransformation of iAs and neonatal levels of iAs and its metabolites are associated with differences in neonate cord metabolomic profiles. The results demonstrate the potential utility of metabolites as biomarkers/indicators of in utero environmental exposure.

Investigating the Role of Fetal Gene Expression in Preterm Birth

Second-trimester amniotic fluid supernatant (AFS) contains cell-free fetal RNA (cffRNA) transcripts that can provide information about fetal gene expression. In a retrospective case-control study, we measured second-trimester fetal gene expression using cffRNA extracted from AFS in women who had spontaneous preterm birth (sPTB) <34 weeks and in women who delivered >37 weeks. We extracted cffRNA from AFS of women with singletons who had second-trimester genetic amniocenteses. Twenty-one gravidas who had sPTB and 21 term controls were matched 1:1 for maternal age, fetal sex, race, gestational age (GA) at the time of amniocentesis, and medication exposure. Cell-free fetal RNA was extracted and hybridized to a customized 65-gene NanoString panel containing genes related to oxidative stress, inflammation, and hypothalamic-pituitary-adrenal (HPA) axis and included 15 housekeeping genes. Two models were run, 1 examining sPTB in relation to case/control status and 1 examining sPTB in relation to GA as a continuous variable. Among cases, the gene expression of nitric oxide synthase 1 ( NOS1), d-aspartate oxidase ( DDO), and Beta-2-microglobulin ( B2M) was higher than controls ( P value < .05; false discovery rate-corrected Q value of ≤0.10). Nitric oxide synthase 1 and DDO are genes associated with oxidative stress; B2M is a marker of the fetal inflammatory response. Fetal HPA gene expression is not associated with GA at delivery or sPTB in second-trimester AFS. Alterations of fetal gene expression related to inflammation and oxidative stress antedate clinical symptoms and may be useful for early identification of patients at risk of having an sPTB.

Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping

BACKGROUND

Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data.

RESULTS

We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most.

CONCLUSIONS

The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms.

A Murine Model of K-RAS and β-Catenin Induced Renal Tumors Expresses High Levels of E2F1 and Resembles Human Wilms Tumor

PURPOSE

Wilms tumor is the most common renal neoplasm of childhood. We previously found that restricted activation of the WNT/β-catenin pathway in renal epithelium late in kidney development is sufficient to induce small primitive neoplasms with features of epithelial Wilms tumor. Metastatic disease progression required simultaneous addition of an activating mutation of the oncogene K-RAS. We sought to define the molecular pathways activated in this process and their relationship to human renal malignancies.

MATERIALS AND METHODS

Affymetrix® expression microarray data from murine kidneys with activation of K-ras and/or Ctnnb1 (β-catenin) restricted to renal epithelium were analyzed and compared to publicly available expression data on normal and neoplastic human renal tissue. Target genes were verified by immunoblot and immunohistochemistry.

RESULTS

Mouse kidney tumors with activation of K-ras and Ctnnb1, and human renal malignancies had similar mRNA expression signatures and were associated with activation of networks centered on β-catenin and TP53. Up-regulation of WNT/β-catenin targets (MYC, Survivin, FOXA2, Axin2 and Cyclin D1) was confirmed by immunoblot. K-RAS/β-catenin murine kidney tumors were more similar to human Wilms tumor than to other renal malignancies and demonstrated activation of a TP53 dependent network of genes, including the transcription factor E2F1. Up-regulation of E2F1 was confirmed in murine and human Wilms tumor samples.

CONCLUSIONS

Simultaneous activation of K-RAS and β-catenin in embryonic renal epithelium leads to neoplasms similar to human Wilms tumor and associated with activation of TP53 and up-regulation of E2F1. Further studies are warranted to evaluate the role of TP53 and E2F1 in human Wilms tumor.

ExpTreeDB: web-based query and visualization of manually annotated gene expression profiling experiments of human and mouse from GEO

MOTIVATION

Numerous public microarray datasets are valuable resources for the scientific communities. Several online tools have made great steps to use these data by querying related datasets with users' own gene signatures or expression profiles. However, dataset annotation and result exhibition still need to be improved.

RESULTS

ExpTreeDB is a database that allows for queries on human and mouse microarray experiments from Gene Expression Omnibus with gene signatures or profiles. Compared with similar applications, ExpTreeDB pays more attention to dataset annotations and result visualization. We introduced a multiple-level annotation system to depict and organize original experiments. For example, a tamoxifen-treated cell line experiment is hierarchically annotated as 'agent→drug→estrogen receptor antagonist→tamoxifen'. Consequently, retrieved results are exhibited by an interactive tree-structured graphics, which provide an overview for related experiments and might enlighten users on key items of interest.

AVAILABILITY AND IMPLEMENTATION

The database is freely available at http://biotech.bmi.ac.cn/ExpTreeDB. Web site is implemented in Perl, PHP, R, MySQL and Apache.

SPARCL1 suppresses metastasis in prostate cancer

PURPOSE

Metastasis, the main cause of death from cancer, remains poorly understood at the molecular level.

EXPERIMENTAL DESIGN

Based on a pattern of reduced expression in human prostate cancer tissues and tumor cell lines, a candidate suppressor gene (SPARCL1) was identified. We used in vitro approaches to determine whether overexpression of SPARCL1 affects cell growth, migration, and invasiveness. We then employed xenograft mouse models to analyze the impact of SPARCL1 on prostate cancer cell growth and metastasis in vivo.

RESULTS

SPARCL1 expression did not inhibit tumor cell proliferation in vitro. By contrast, SPARCL1 did suppress tumor cell migration and invasiveness in vitro and tumor metastatic growth in vivo, conferring improved survival in xenograft mouse models.

CONCLUSIONS

We present the first in vivo data suggesting that SPARCL1 suppresses metastasis of prostate cancer.

A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer

Background

Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.

Methods and Findings

We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers.

Conclusions

We have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations.

AnyExpress: integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm

Background

Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a single platform and are not flexible enough to support custom changes, which arise from the new statistical methods, updated versions of reference data, and better platforms released every month or year. Current tools are so tightly coupled with reference information, such as reference genome, transcriptome database, and SNP, which are often erroneous or outdated, that the output results are incorrect and misleading.

Results

We developed AnyExpress, a software package that combines cross-platform gene expression data using a fast interval-matching algorithm. Supported platforms include next-generation-sequencing technology, microarray, SAGE, MPSS, and more. Users can define custom target transcriptome database references for probe/read mapping in any species, as well as criteria to remove undesirable probes/reads.

AnyExpress offers scalable processing features such as binding, normalization, and summarization that are not present in existing software tools.

As a case study, we applied AnyExpress to published Affymetrix microarray and Illumina NGS RNA-Seq data from human kidney and liver. The mean of within-platform correlation coefficient was 0.98 for within-platform samples in kidney and liver, respectively. The mean of cross-platform correlation coefficients was 0.73. These results confirmed those of the original and secondary studies. Applying filtering produced higher agreement between microarray and NGS, according to an agreement index calculated from differentially expressed genes.

Conclusion

AnyExpress can combine cross-platform gene expression data, process data from both open- and closed-platforms, select a custom target reference, filter out undesirable probes or reads based on custom-defined biological features, and perform quantile-normalization with a large number of microarray samples. AnyExpress is fast, comprehensive, flexible, and freely available at http://anyexpress.sourceforge.net.

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns

Background

DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc.

Description

We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (Drosophila melanogaster, Caenorhabditis elegans) and vertebrates (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies.

Conclusions

Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.

Generalized random set framework for functional enrichment analysis using primary genomics datasets

MOTIVATION

Functional enrichment analysis using primary genomics datasets is an emerging approach to complement established methods for functional enrichment based on predefined lists of functionally related genes. Currently used methods depend on creating lists of 'significant' and 'non-significant' genes based on ad hoc significance cutoffs. This can lead to loss of statistical power and can introduce biases affecting the interpretation of experimental results.

RESULTS

We developed and validated a new statistical framework, generalized random set (GRS) analysis, for comparing the genomic signatures in two datasets without the need for gene categorization. In our tests, GRS produced correct measures of statistical significance, and it showed dramatic improvement in the statistical power over other methods currently used in this setting. We also developed a procedure for identifying genes driving the concordance of the genomics profiles and demonstrated a dramatic improvement in functional coherence of genes identified in such analysis.

AVAILABILITY

GRS can be downloaded as part of the R package CLEAN from http://ClusterAnalysis.org/. An online implementation is available at http://GenomicsPortals.org/.

MARQ: an online tool to mine GEO for experiments with similar or opposite gene expression signatures

The enormous amount of data available in public gene expression repositories such as Gene Expression Omnibus (GEO) offers an inestimable resource to explore gene expression programs across several organisms and conditions. This information can be used to discover experiments that induce similar or opposite gene expression patterns to a given query, which in turn may lead to the discovery of new relationships among diseases, drugs or pathways, as well as the generation of new hypotheses. In this work, we present MARQ, a web-based application that allows researchers to compare a query set of genes, e.g. a set of over- and under-expressed genes, against a signature database built from GEO datasets for different organisms and platforms. MARQ offers an easy-to-use and integrated environment to mine GEO, in order to identify conditions that induce similar or opposite gene expression patterns to a given experimental condition. MARQ also includes additional functionalities for the exploration of the results, including a meta-analysis pipeline to find genes that are differentially expressed across different experiments. The application is freely available at http://marq.dacya.ucm.es.

Probing differentially expressed genes against a microarray database for in silico suppressor/enhancer and inhibitor/activator screens

High-density oligonucleotide arrays are widely used for analysis of gene expression on a genomic scale, but the generated data remain largely inaccessible for comparative analysis purposes. Similarity searches in databases with differentially expressed gene (DEG) lists may be used to assign potential functions to new genes and to identify potential chemical inhibitors/activators and genetic suppressors/enhancers. Although this is a very promising concept, it requires the compatibility and validity of the DEG lists to be significantly improved. Using Arabidopsis and human datasets, we have developed guidelines for the performance of similarity searches against databases that collect microarray data. We found that, in comparison with many other methods, a rank-product analysis achieves a higher degree of inter- and intra-laboratory consistency of DEG lists, and is advantageous for assessing similarities and differences between them. To support this concept, we developed a tool called MASTA (microarray overlap search tool and analysis), and re-analyzed over 600 Arabidopsis microarray expression datasets. This revealed that large-scale searches produce reliable intersections between DEG lists that prove to be useful for genetic analysis, thus aiding in the characterization of cellular and molecular mechanisms. We show that this approach can be used to discover unexpected connections and to illuminate unanticipated interactions between individual genes.

Web-based interrogation of gene expression signatures using EXALT

Background

Widespread use of high-throughput techniques such as microarrays to monitor gene expression levels has resulted in an explosive growth of data sets in public domains. Integration and exploration of these complex and heterogeneous data have become a major challenge.

Results

The EXALT (EXpression signature AnaLysis Tool) online program enables meta-analysis of gene expression profiles derived from publically accessible sources. Searches can be executed online against two large databases currently containing more than 28,000 gene expression signatures derived from GEO (Gene Expression Omnibus) and published expression profiles of human cancer. Comparisons among gene expression signatures can be performed with homology analysis and co-expression analysis. Results can be visualized instantly in a plot or a heat map. Three typical use cases are illustrated.

Conclusions

The EXALT online program is uniquely suited for discovering relationships among transcriptional profiles and searching gene expression patterns derived from diverse physiological and pathological settings. The EXALT online program is freely available for non-commercial users from http://seq.mc.vanderbilt.edu/exalt/.

Candidate metastasis suppressor genes uncovered by array comparative genomic hybridization in a mouse allograft model of prostate cancer

Background

The purpose of this study was to identify candidate metastasis suppressor genes from a mouse allograft model of prostate cancer (NE-10). This allograft model originally developed metastases by twelve weeks after implantation in male athymic nude mice, but lost the ability to metastasize after a number of in vivo passages. We performed high resolution array comparative genomic hybridization on the metastasizing and non-metastasizing allografts to identify chromosome imbalances that differed between the two groups of tumors.

Results

This analysis uncovered a deletion on chromosome 2 that differed between the metastasizing and non-metastasizing tumors. Bioinformatics filters were employed to mine this region of the genome for candidate metastasis suppressor genes. Of the 146 known genes that reside within the region of interest on mouse chromosome 2, four candidate metastasis suppressor genes (Slc27a2, Mall, Snrpb, and Rassf2) were identified. Quantitative expression analysis confirmed decreased expression of these genes in the metastasizing compared to non-metastasizing tumors.

Conclusion

This study presents combined genomics and bioinformatics approaches for identifying potential metastasis suppressor genes. The genes identified here are candidates for further studies to determine their functional role in inhibiting metastases in the NE-10 allograft model and human prostate cancer.

GEOGLE: context mining tool for the correlation between gene expression and the phenotypic distinction

Background

In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches.

Results

In our work, we integrated gene expression information from Gene Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings (MeSH) and signaling pathway knowledge from sigPathway entries to develop a context mining tool for gene expression analysis – GEOGLE. GEOGLE offers a rapid and convenient way for searching relevant experimental datasets, pathways and biological terms according to multiple types of queries: including biomedical vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, GEOGLE summarizes the signature genes from a subset of GDSes and estimates the correlation between gene expression and the phenotypic distinction with an integrated p value.

Conclusion

This approach performing global searching of expression data may expand the traditional way of collecting heterogeneous gene expression experiment data. GEOGLE is a novel tool that provides researchers a quantitative way to understand the correlation between gene expression and phenotypic distinction through meta-analysis of gene expression datasets from different experiments, as well as the biological meaning behind. The web site and user guide of GEOGLE are available at: