ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling

The variation game: Cracking complex genetic disorders with NGS and omics data

Tremendous advances in Next Generation Sequencing (NGS) and high-throughput omics methods have brought us one step closer towards mechanistic understanding of the complex disease at the molecular level. In this review, we discuss four basic regulatory mechanisms implicated in complex genetic diseases, such as cancer, neurological disorders, heart disease, diabetes, and many others. The mechanisms, including genetic variations, copy-number variations, posttranscriptional variations, and epigenetic variations, can be detected using a variety of NGS methods. We propose that malfunctions detected in these mechanisms are not necessarily independent, since these malfunctions are often found associated with the same disease and targeting the same gene, group of genes, or functional pathway. As an example, we discuss possible rewiring effects of the cancer-associated genetic, structural, and posttranscriptional variations on the protein-protein interaction (PPI) network centered around P53 protein. The review highlights multi-layered complexity of common genetic disorders and suggests that integration of NGS and omics data is a critical step in developing new computational methods capable of deciphering this complexity.

Genomic analysis of clonal eosinophils by CGH arrays reveals new genetic regions involved in chronic eosinophilia

To assess the presence of genetic imbalances in patients with myeloproliferative neoplasms (MPNs), 38 patients with chronic eosinophilia were studied by array comparative genomic hybridization (aCGH): seven had chronic myelogenous leukaemia (CML), BCR-ABL1 positive, nine patients had myeloproliferative neoplasia Ph- (MPN-Ph-), three had a myeloid neoplasm associated with a PDGFRA rearrangement, and the remaining two cases were Lymphoproliferative T neoplasms associated with eosinophilia. In addition, 17 patients had a secondary eosinophilia and were used as controls. Eosinophilic enrichment was carried out in all cases. Genomic imbalances were found in 76% of all MPN patients. Losses on 20q were the most frequent genetic abnormality in MPNs (32%), affected the three types of MPN studied. This study also found losses at 11q13.3 in 26% of patients with MPN-Ph- and in 19p13.11 in two of the three patients with an MPN associated with a PDGFRA rearrangement. In addition, 29% of patients with CML had losses on 8q24. In summary, aCGH revealed clonality in eosinophils in most MPNs, suggesting that it could be a useful technique for defining clonality in these diseases. The presence of genetic losses in new regions could provide new insights into the knowledge of these MPN associated with eosinophilia.

SegAnnDB: interactive Web-based genomic segmentation

MOTIVATION

DNA copy number profiles characterize regions of chromosome gains, losses and breakpoints in tumor genomes. Although many models have been proposed to detect these alterations, it is not clear which model is appropriate before visual inspection the signal, noise and models for a particular profile.

RESULTS

We propose SegAnnDB, a Web-based computer vision system for genomic segmentation: first, visually inspect the profiles and manually annotate altered regions, then SegAnnDB determines the precise alteration locations using a mathematical model of the data and annotations. SegAnnDB facilitates collaboration between biologists and bioinformaticians, and uses the University of California, Santa Cruz genome browser to visualize copy number alterations alongside known genes.

AVAILABILITY AND IMPLEMENTATION

The breakpoints project on INRIA GForge hosts the source code, an Amazon Machine Image can be launched and a demonstration Web site is http://bioviz.rocq.inria.fr.

Clinical applications of BAC array-CGH to the study of diffuse large B-cell lymphomas

BAC array-CGH is a powerful method to identify DNA copy number changes (gains, amplifications and deletions) on a genome-wide scale, and to map these changes to genomic sequence. It is based on the analysis of genomic DNA isolated from test and reference cell populations, the differential labelling with fluorescent dyes and the co-hybridization with a genomic array. BAC array-CGH has proven to be a specific, sensitive, and reliable technique, with considerable advantages compared to other methods used for the analysis of DNA copy number changes. The application of genome scanning technologies and the recent advances in bioinformatics tools that enable us to perform a robust and highly sensitive analysis of array-CGH data, useful not only for genome scanning of tumor cells but also in the identification of novel cancer related genes, oncogenes and suppressor genes. Cytogenetic analysis provides essential information for diagnosis and prognosis in patients with hematologic malignancies such as lymphomas. However, the chromosomal interpretation in non-Hodgkin lymphoma (NHL) is sometimes inconclusive. Copy number aberrations identified by BAC array-CGH analyses could be a complementary methodology to chromosomal analysis. In NHL the genomic imbalances might have a prognostic rather than a diagnostic value. In fact, the diagnosis of NHL is based on pathological and molecular cytogenetics data. Furthermore genetic variations and their association with specific types of lymphoma development, and elucidation of the variable genetic pathways leading to lymphoma development, are important directions for future cancer research. Array-CGH, along with FISH and PCR, will be used for routine diagnostic purposes in near future.

Genovar: a detection and visualization tool for genomic variants

BACKGROUND

Along with single nucleotide polymorphisms (SNPs), copy number variation (CNV) is considered an important source of genetic variation associated with disease susceptibility. Despite the importance of CNV, the tools currently available for its analysis often produce false positive results due to limitations such as low resolution of array platforms, platform specificity, and the type of CNV. To resolve this problem, spurious signals must be separated from true signals by visual inspection. None of the previously reported CNV analysis tools support this function and the simultaneous visualization of comparative genomic hybridization arrays (aCGH) and sequence alignment. The purpose of the present study was to develop a useful program for the efficient detection and visualization of CNV regions that enables the manual exclusion of erroneous signals.

RESULTS

A JAVA-based stand-alone program called Genovar was developed. To ascertain whether a detected CNV region is a novel variant, Genovar compares the detected CNV regions with previously reported CNV regions using the Database of Genomic Variants (DGV, http://projects.tcag.ca/variation) and the Single Nucleotide Polymorphism Database (dbSNP). The current version of Genovar is capable of visualizing genomic data from sources such as the aCGH data file and sequence alignment format files.

CONCLUSIONS

Genovar is freely accessible and provides a user-friendly graphic user interface (GUI) to facilitate the detection of CNV regions. The program also provides comprehensive information to help in the elimination of spurious signals by visual inspection, making Genovar a valuable tool for reducing false positive CNV results.

AVAILABILITY

http://genovar.sourceforge.net/.

Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM).

Array-based comparative genomics is a standard approach for the identification of DNA copy number polymorphisms between closely related genomes. The huge amounts of data produced by these experiments require efficient and accurate bioinformatics tools for the identification of copy number polymorphisms. Hidden Markov Models (HMMs) are frequently used for analyzing such data sets, but current models are based on first-order HMMs only having limited capabilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. We develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling these dependencies to overcome this limitation. In an in-depth case study with Arabidopsis thaliana, we find that parsimonious higher-order HMMs clearly improve the identification of copy number polymorphisms in comparison to standard first-order HMMs and other frequently used methods. Functional analysis of identified polymorphisms revealed details of genomic differences between the accessions C24 and Col-0 of Arabidopsis thaliana. An additional study on human cell lines further indicates that parsimonious HMMs are well-suited for the analysis of Array-CGH data.

Molecular characterization of the region 7q22.1 in splenic marginal zone lymphomas

Splenic marginal zone lymphomas (SMZL) are an uncommon type of B-cell non-Hodgkin's lymphoma (NHL-B) in which no specific chromosomal translocations have been described. In contrast, the most frequent cytogenetic abnormality is the loss of the long arm of chromosome 7 (7q). Previous reports have located this loss in the 7q32 region. In order to better characterize the genomic imbalances in SMZL, molecular studies were carried out in 73 patients with SMZL. To gain insight into the mapping at 7q a tiling array was also used. The results confirmed the loss of 7q as the most frequent change. In addition, several abnormalities, including 4q22.1, 1q21.3–q22, 6q25.3, 20q13.33, 3q28, 2q23.3–q24.1 and 17p13, were also present. A loss of 7q22.1 at 99925039–101348479 bp was observed in half of the cases. The region of 7q22.1 has not previously been characterised in SMZL. Our results confirmed the presence of a new region of loss on chromosome 7 in these NHL.

FISH Oracle: a web server for flexible visualization of DNA copy number data in a genomic context

Background

The rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Optimally, such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate.

Results

We have developed a web-based software FISH Oracle to visualize data from multiple array CGH experiments in a genomic context. Its fast visualization engine and advanced web and database technology supports highly interactive use. FISH Oracle comes with a convenient data import mechanism, powerful search options for genomic elements (e.g. gene names or karyobands), quick navigation and zooming into interesting regions, and mechanisms to export the visualization into different high quality formats. These features make the software especially suitable for the needs of life scientists.

Conclusions

FISH Oracle offers a fast and easy to use visualization tool for array CGH and SNP array data. It allows for the identification of genomic regions representing minimal common changes based on data from one or more experiments. FISH Oracle will be instrumental to identify candidate onco and tumor suppressor genes based on the frequency and genomic position of DNA copy number changes. The FISH Oracle application and an installed demo web server are available at http://www.zbh.uni-hamburg.de/fishoracle.

Deciphering squamous cell carcinoma using multidimensional genomic approaches

Squamous cell carcinomas (SqCCs) arise in a wide range of tissues including skin, lung, and oral mucosa. Although all SqCCs are epithelial in origin and share common nomenclature, these cancers differ greatly with respect to incidence, prognosis, and treatment. Current knowledge of genetic similarities and differences between SqCCs is insufficient to describe the biology of these cancers, which arise from diverse tissue origins. In this paper we provide a general overview of whole genome approaches for gene and pathway discovery and highlight the advancement of integrative genomics as a state-of-the-art technology in the study of SqCC genetics.

CAM: a web tool for combining array CGH and microarray gene expression data from multiple samples

We develop a web-based tool for Combining Array CGH copy number aberration data and Microarray gene expression data (CAM). This tool analyzes these two data sets from multiple samples to detect genes having both DNA copy number aberrations (CNAs) and gene expression changes. CAM provides several statistical methods for identifying CNAs, which are consistent across multiple samples. Identified CNAs and their correlated gene expression changes are then visualized along the chromosomes. As a result, CAM is a useful tool for identifying disease related genes when these two types of data sets are available. To illustrate the various analysis outputs of CAM, we subsequently provide ten sets of example data from seven cancer types.

A survey of analysis software for array-comparative genomic hybridisation studies to detect copy number variation

Copy number variants (CNVs) create a major source of variation among individuals and populations. Array-based comparative genomic hybridisation (aCGH) is a powerful method used to detect and compare the copy numbers of DNA sequences at high resolution along the genome. In recent years, several informatics tools for accurate and efficient CNV detection and assessment have been developed. In this paper, most of the well known algorithms, analysis software and the limitations of that software will be briefly reviewed.

waviCGH: a web application for the analysis and visualization of genomic copy number alterations

waviCGH is a versatile web server for the analysis and comparison of genomic copy number alterations in multiple samples from any species. waviCGH processes data generated by high density SNP-arrays, array-CGH or copy-number calls generated by any technique. waviCGH includes methods for pre-processing of the data, segmentation, calling of gains and losses, and minimal common regions determination over a set of experiments. The server is a user-friendly interface to the analytical methods, with emphasis on results visualization in a genomic context. Analysis tools are introduced to the user as the different steps to follow in an experimental protocol. All the analysis steps generate high quality images and tables ready to be imported into spreadsheet programs. Additionally, for human, mouse and rat, altered regions are represented in a biological context by mapping them into chromosomes in an integrated cytogenetic browser. waviCGH is available at http://wavi.bioinfo.cnio.es.

Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer

Advances in high-throughput, genome-wide profiling technologies have allowed for an unprecedented view of the cancer genome landscape. Specifically, high-density microarrays and sequencing-based strategies have been widely utilized to identify genetic (such as gene dosage, allelic status, and mutations in gene sequence) and epigenetic (such as DNA methylation, histone modification, and microRNA) aberrations in cancer. Although the application of these profiling technologies in unidimensional analyses has been instrumental in cancer gene discovery, genes affected by low-frequency events are often overlooked. The integrative approach of analyzing parallel dimensions has enabled the identification of (a) genes that are often disrupted by multiple mechanisms but at low frequencies by any one mechanism and (b) pathways that are often disrupted at multiple components but at low frequencies at individual components. These benefits of using an integrative approach illustrate the concept that the whole is greater than the sum of its parts. As efforts have now turned toward parallel and integrative multidimensional approaches for studying the cancer genome landscape in hopes of obtaining a more insightful understanding of the key genes and pathways driving cancer cells, this review describes key findings disseminating from such high-throughput, integrative analyses, including contributions to our understanding of causative genetic events in cancer cell biology.

Intragenic GNAS deletion involving exon A/B in pseudohypoparathyroidism type 1A resulting in an apparent loss of exon A/B methylation: potential for misdiagnosis of pseudohypoparathyroidism type 1B

CONTEXT

Several endocrine diseases that share resistance to PTH are grouped under the term pseudohypoparathyroidism (PHP). Patients with PHP type Ia show additional hormone resistance, defective erythrocyte G(s)alpha activity, and dysmorphic features termed Albright's hereditary osteodystrophy (AHO). Patients with PHP-Ib show less diverse hormone resistance and normal G(s)alpha activity; AHO features are typically absent in PHP-Ib. Mutations affecting G(s)alpha coding exons of GNAS and epigenetic alterations in the same gene are associated with PHP-Ia and -Ib, respectively. The epigenetic GNAS changes in familial PHP-Ib are caused by microdeletions near or within GNAS but without involving G(s)alpha coding exons.

OBJECTIVE

We sought to identify the molecular defect in a patient who was diagnosed with PHP-Ia based on clinical presentation (hormone resistance and AHO) but displayed the molecular features typically associated with PHP-Ib (loss of methylation at exon A/B) without previously described genetic mutations.

METHODS

Microsatellite typing, comparative genome hybridization, and allelic dosage were performed for proband and her parents.

RESULTS

Comparative genome hybridization revealed a deletion of 30,431 bp extending from the intronic region between exons XL and A/B to intron 5. The same mutation was also demonstrated, by PCR, in the patient's mother, but polymorphism and allele dosage analyses indicated that she had this mutation in a mosaic manner.

CONCLUSION

We discovered a novel multiexonic GNAS deletion transmitted to our patient from her mother who is mosaic for this mutation. The deletion led to different phenotypic manifestations in the two generation and appeared, in the patient, as loss of GNAS imprinting.

Functional profiling methods in cancer

The introduction of new high-throughput methodologies such as DNA microarrays constitutes a major breakthrough in cancer research. The unprecedented amount of data produced by such technologies has opened new avenues for interrogating living systems although, at the same time, it has demanded of the development of new data analytical methods as well as new strategies for testing hypotheses. A history of early successful applications in cancer boosted the use of microarrays and fostered further applications in other fields. Keeping the pace with these technologies, bioinformatics offers new solutions for data analysis and, what is more important, permits the formulation of a new class of hypotheses inspired in systems biology, more oriented to pathways or, in general, to modules of functionally related genes. Although these analytical methodologies are new, some options are already available and are discussed in this chapter.

CHESS (CgHExpreSS): a comprehensive analysis tool for the analysis of genomic alterations and their effects on the expression profile of the genome

Background

Genomic alterations frequently occur in many cancer patients and play important mechanistic roles in the pathogenesis of cancer. Furthermore, they can modify the expression level of genes due to altered copy number in the corresponding region of the chromosome. An accumulating body of evidence supports the possibility that strong genome-wide correlation exists between DNA content and gene expression. Therefore, more comprehensive analysis is needed to quantify the relationship between genomic alteration and gene expression. A well-designed bioinformatics tool is essential to perform this kind of integrative analysis. A few programs have already been introduced for integrative analysis. However, there are many limitations in their performance of comprehensive integrated analysis using published software because of limitations in implemented algorithms and visualization modules.

Results

To address this issue, we have implemented the Java-based program CHESS to allow integrative analysis of two experimental data sets: genomic alteration and genome-wide expression profile. CHESS is composed of a genomic alteration analysis module and an integrative analysis module. The genomic alteration analysis module detects genomic alteration by applying a threshold based method or SW-ARRAY algorithm and investigates whether the detected alteration is phenotype specific or not. On the other hand, the integrative analysis module measures the genomic alteration's influence on gene expression. It is divided into two separate parts. The first part calculates overall correlation between comparative genomic hybridization ratio and gene expression level by applying following three statistical methods: simple linear regression, Spearman rank correlation and Pearson's correlation. In the second part, CHESS detects the genes that are differentially expressed according to the genomic alteration pattern with three alternative statistical approaches: Student's t-test, Fisher's exact test and Chi square test. By successive operations of two modules, users can clarify how gene expression levels are affected by the phenotype specific genomic alterations. As CHESS was developed in both Java application and web environments, it can be run on a web browser or a local machine. It also supports all experimental platforms if a properly formatted text file is provided to include the chromosomal position of probes and their gene identifiers.

Conclusions

CHESS is a user-friendly tool for investigating disease specific genomic alterations and quantitative relationships between those genomic alterations and genome-wide gene expression profiling.

A computational procedure to identify significant overlap of differentially expressed and genomic imbalanced regions in cancer datasets

The integration of high-throughput genomic data represents an opportunity for deciphering the interplay between structural and functional organization of genomes and for discovering novel biomarkers. However, the development of integrative approaches to complement gene expression (GE) data with other types of gene information, such as copy number (CN) and chromosomal localization, still represents a computational challenge in the genomic arena. This work presents a computational procedure that directly integrates CN and GE profiles at genome-wide level. When applied to DNA/RNA paired data, this approach leads to the identification of Significant Overlaps of Differentially Expressed and Genomic Imbalanced Regions (SODEGIR). This goal is accomplished in three steps. The first step extends to CN a method for detecting regional imbalances in GE. The second part provides the integration of CN and GE data and identifies chromosomal regions with concordantly altered genomic and transcriptional status in a tumor sample. The last step elevates the single-sample analysis to an entire dataset of tumor specimens. When applied to study chromosomal aberrations in a collection of astrocytoma and renal carcinoma samples, the procedure proved to be effective in identifying discrete chromosomal regions of coordinated CN alterations and changes in transcriptional levels.

Advanced analysis and visualization of gene copy number and expression data

BACKGROUND

Gene copy number and gene expression values play important roles in cancer initiation and progression. Both can be measured with high-throughput microarrays and some methodologies to integrate and analyze these data exist. However, varying gene sets within different gene expression and copy number microarrays present significant challenges.

RESULTS

We report an advanced version of earlier published CGH-Plotter that rapidly can identify amplified and deleted areas using gene copy number data. With CGH-Plotter v2, the copy number values can be filtered based on the genomic location in basepair units. After filtering, the values for the missing genes can be interpolated. Moreover, the effect of non-informative areas in the genome can be systematically removed by smoothing and interpolating. Further, we developed a tool (ECN) to illustrate the CGH-data values annotated based on the gene expression. The ECN-tool is a MATLAB toolbox enabling straightforward illustration of copy numbers annotated based on the gene expression levels.

CONCLUSION

CGH-Plotter v2 provides two methods for analyzing copy number data; dynamic programming and genomic location based smoothing. With ECN-tool the data analyzed with CGH-Plotter v2 can easily be illustrated along the chromosomes individually or along the whole genome. ECN-tool plots the copy number data annotated based on the gene expression data, and it is easy to find the genes that are both over-expressed and amplified or under-expressed and deleted in the samples. From the resulting figures it is straightforward to select interesting genes.

SIGMA2: a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes

Background

High throughput microarray technologies have afforded the investigation of genomes, epigenomes, and transcriptomes at unprecedented resolution. However, software packages to handle, analyze, and visualize data from these multiple 'omics disciplines have not been adequately developed.

Results

Here, we present SIGMA², a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes. Multi-dimensional datasets can be simultaneously visualized and analyzed with respect to each dimension, allowing combinatorial integration of the different assays belonging to the different 'omics.

Conclusion

The identification of genes altered at multiple levels such as copy number, loss of heterozygosity (LOH), DNA methylation and the detection of consequential changes in gene expression can be concertedly performed, establishing SIGMA² as a novel tool to facilitate the high throughput systems biology analysis of cancer.

Formulating and testing hypotheses in functional genomics

OBJECTIVE

The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the results, relating the available genomic information to the hypotheses that originated the experiment.

METHODS AND RESULTS

Initially, this interpretation has been made on a pre-selection of relevant genes, based on the experimental values, followed by the study of the enrichment in some functional properties. Nevertheless, functional enrichment methods, demonstrated to have a flaw: the first step of gene selection was too stringent given that the cooperation among genes was ignored. The assumption that modules of genes related by relevant biological properties (functionality, co-regulation, chromosomal location, etc.) are the real actors of the cell biology lead to the development of new procedures, inspired in systems biology criteria, generically known as gene-set methods. These methods have been successfully used to analyze transcriptomic and large-scale genotyping experiments as well as to test other different genome-scale hypothesis in other fields such as phylogenomics.

GEPAS, a web-based tool for microarray data analysis and interpretation

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.

MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data

Background

Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms.

Results

To address this issue, we have developed an integrative genomic analysis platform MD-SeeGH, a software tool that allows users to rapidly and directly analyze genomic datasets spanning multiple genomic experiments. With MD-SeeGH, users have the flexibility to easily update datasets in accordance with new genomic builds, make a quality assessment of data using the filtering features, and identify genetic alterations within single or across multiple experiments. Multiple sample analysis in MD-SeeGH allows users to compare profiles from many experiments alongside tracks containing detailed localized gene information, microRNA, CpG islands, and copy number variations.

Conclusion

MD-SeeGH is a new platform for the integrative analysis of diverse microarray data, facilitating multiple profile analyses and group comparisons.

CGHweb: a tool for comparing DNA copy number segmentations from multiple algorithms

Accurate estimation of DNA copy numbers from array comparative genomic hybridization (CGH) data is important for characterizing the cancer genome. An important part of this process is the segmentation of the log-ratios between the sample and control DNA along the chromosome into regions of different copy numbers. However, multiple algorithms are available in the literature for this procedure and the results can vary substantially among these. Thus, a visualization tool that can display the segmented profiles from a number of methods can be helpful to the biologist or the clinician to ascertain that a feature of interest did not arise as an artifact of the algorithm. Such a tool also allows the methodologist to easily contrast his method against others. We developed a web-based tool that applies a number of popular algorithms to a single array CGH profile entered by the user. It generates a heatmap panel of the segmented profiles for each method as well as a consensus profile. The clickable heatmap can be moved along the chromosome and zoomed in or out. It also displays the time that each algorithm took and provides numerical values of the segmented profiles for download. The web interface calls algorithms written in the statistical language R. We encourage developers of new algorithms to submit their routines to be incorporated into the website.

AVAILABILITY

http://compbio.med.harvard.edu/CGHweb.

Integrative analysis reveals the direct and indirect interactions between DNA copy number aberrations and gene expression changes

MOTIVATION

DNA copy number aberrations (CNAs) and gene expression (GE) changes provide valuable information for studying chromosomal instability and its consequences in cancer. While it is clear that the structural aberrations and the transcript levels are intertwined, their relationship is more complex and subtle than initially suspected. Most studies so far have focused on how a CNA affects the expression levels of those genes contained within that CNA.

RESULTS

To better understand the impact of CNAs on expression, we investigated the correlation of each CNA to all other genes in the genome. The correlations are computed over multiple patients that have both expression and copy number measurements in brain, bladder and breast cancer data sets. We find that a CNA has a direct impact on the gene amplified or deleted, but it also has a broad, indirect impact elsewhere. To identify a set of CNAs that is coordinately associated with the expression changes of a set of genes, we used a biclustering algorithm on the correlation matrix. For each of the three cancer types examined, the aberrations in several loci are associated with cancer-type specific biological pathways that have been described in the literature: CNAs of chromosome (chr) 7p13 were significantly correlated with epidermal growth factor receptor signaling pathway in glioblastoma multiforme, chr 13q with NF-kappaB cascades in bladder cancer, and chr 11p with Reck pathway in breast cancer. In all three data sets, gene sets related to cell cycle/division such as M phase, DNA replication and cell division were also associated with CNAs. Our results suggest that CNAs are both directly and indirectly correlated with changes in expression and that it is beneficial to examine the indirect effects of CNAs.

AVAILABILITY

The code is available upon request.

ADaCGH: A parallelized web-based application and R package for the analysis of aCGH data

BACKGROUND

Copy number alterations (CNAs) in genomic DNA have been associated with complex human diseases, including cancer. One of the most common techniques to detect CNAs is array-based comparative genomic hybridization (aCGH). The availability of aCGH platforms and the need for identification of CNAs has resulted in a wealth of methodological studies.

METHODOLOGY/PRINCIPAL FINDINGS

ADaCGH is an R package and a web-based application for the analysis of aCGH data. It implements eight methods for detection of CNAs, gains and losses of genomic DNA, including all of the best performing ones from two recent reviews (CBS, GLAD, CGHseg, HMM). For improved speed, we use parallel computing (via MPI). Additional information (GO terms, PubMed citations, KEGG and Reactome pathways) is available for individual genes, and for sets of genes with altered copy numbers.

CONCLUSIONS/SIGNIFICANCE

ADACGH represents a qualitative increase in the standards of these types of applications: a) all of the best performing algorithms are included, not just one or two; b) we do not limit ourselves to providing a thin layer of CGI on top of existing BioConductor packages, but instead carefully use parallelization, examining different schemes, and are able to achieve significant decreases in user waiting time (factors up to 45x); c) we have added functionality not currently available in some methods, to adapt to recent recommendations (e.g., merging of segmentation results in wavelet-based and CGHseg algorithms); d) we incorporate redundancy, fault-tolerance and checkpointing, which are unique among web-based, parallelized applications; e) all of the code is available under open source licenses, allowing to build upon, copy, and adapt our code for other software projects.