Large-scale Statistical Analysis Research Articles

An efficient heuristic approach to detecting graph isomorphism based on combinations of highly discriminating invariants

The search for an easily computable, finite, complete set of graph invariants remains a challenging research topic. All measures characterizing the topology of a graph that have been developed thus far exhibit some degree of degeneracy, i.e., an inability to distinguish between non-isomorphic graphs. In this paper, we show that certain graph invariants can be useful in substantially reducing the computational complexity of isomorphism testing. Our findings are underpinned by numerical results based on a large scale statistical analysis.

Capital Raising in the U.S.: The Significance of Unregistered Offerings Using the Regulation D Exemption

In this report we present an analysis of the information extracted from Form D filings received by the Commission since the beginning of 2009. The results are intended to inform the Commission on the amount and nature of capital raised through unregistered offerings claiming a Regulation D exemption, and to provide some preliminary perspective on the state of competition and regulatory burden in capital markets. In particular, we compare the amount of capital raised using the Regulation D exemption to capital raised from other unregistered and registered offering methods. This information may be particularly useful in assessing the merit of current or potential future rulemaking activity. This analysis is not intended to inform the Commission on compliance with or enforcement of federal securities laws.One of the original purposes of Regulation D, first adopted in 1982 and amended on three subsequent occasions, was to collect and analyze data on issuers seeking a 1933 Act registration exemption to address the capital formation needs of smaller companies. However, until 2008, issuers filed Form D on paper forms, making the extraction of information for large-scale statistical analysis problematic. In February 2008, the SEC adopted amendments to Form D that require issuers to submit their Form D filings electronically and in a structured data format. As a result of the new requirements, which were phased in from September 2008 through March of 2009, Form D filings are now machine-readable. Using basic text parsing tools, the Division of Risk, Strategy and Financial Innovation staff was able to extract the reported elements and place them in a database enabling the large-scale statistical analysis reported here.

Fast approximation of matrix coherence and statistical leverage

The statistical leverage scores of a matrix A are the squared row-norms of the matrix containing its (top) left singular vectors and the coherence is the largest leverage score. These quantities are of interest in recently-popular problems such as matrix completion and Nystrom-based low-rank matrix approximation as well as in large-scale statistical data analysis applications more generally; moreover, they are of interest since they define the key structural nonuniformity that must be dealt with in developing fast randomized matrix algorithms. Our main result is a randomized algorithm that takes as input an arbitrary n × d matrix A, with n ≫ d, and that returns as output relative-error approximations to all n of the statistical leverage scores. The proposed algorithm runs (under assumptions on the precise values of n and d) in O(nd logn) time, as opposed to the O(nd2) time required by the naive algorithm that involves computing an orthogonal basis for the range of A. Our analysis may be viewed in terms of computing a relative-error approximation to an underconstrained least-squares approximation problem, or, relatedly, it may be viewed as an application of Johnson-Lindenstrauss type ideas. Several practically-important extensions of our basic result are also described, including the approximation of so-called cross-leverage scores, the extension of these ideas to matrices with n ≈ d, and the extension to streaming environments.

Introduction: Human migration to protected area edges in Africa and Latin America: Questioning large-scale statistical analysis

The introduction to this set of papers highlights four challenges to the large-scale analysis of population growth at protected area edges in Africa and Latin America undertaken by George Wittemyer and colleagues in their 2008 paper published in Science. First, it raises questions about their sampling procedures, given national-level variation in systems of protected area designation and protected area estates. Second, it challenges the largely economic model of migration decisions that underlies their analysis. Third, it highlights the neglected variable of land tenure systems as a factor facilitating or impeding migration. Fourth, it points to the problematic politics of reducing human communities and polities to 'populations' subject to management from afar.

Subsampling methods for genomic inference

Large-scale statistical analysis of data sets associated with genome sequences plays an important role in modern biology. A key component of such statistical analyses is the computation of p-values and confidence bounds for statistics defined on the genome. Currently such computation is commonly achieved through ad hoc simulation measures. The method of randomization, which is at the heart of these simulation procedures, can significantly affect the resulting statistical conclusions. Most simulation schemes introduce a variety of hidden assumptions regarding the nature of the randomness in the data, resulting in a failure to capture biologically meaningful relationships. To address the need for a method of assessing the significance of observations within large scale genomic studies, where there often exists a complex dependency structure between observations, we propose a unified solution built upon a data subsampling approach. We propose a piecewise stationary model for genome sequences and show that the subsampling approach gives correct answers under this model. We illustrate the method on three simulation studies and two real data examples.

An integrated analysis of molecular aberrations in NCI-60 cell lines

BackgroundCancer is a complex disease where various types of molecular aberrations drive the development and progression of malignancies. Large-scale screenings of multiple types of molecular aberrations (e.g., mutations, copy number variations, DNA methylations, gene expressions) become increasingly important in the prognosis and study of cancer. Consequently, a computational model integrating multiple types of information is essential for the analysis of the comprehensive data.ResultsWe propose an integrated modeling framework to identify the statistical and putative causal relations of various molecular aberrations and gene expressions in cancer. To reduce spurious associations among the massive number of probed features, we sequentially applied three layers of logistic regression models with increasing complexity and uncertainty regarding the possible mechanisms connecting molecular aberrations and gene expressions. Layer 1 models associate gene expressions with the molecular aberrations on the same loci. Layer 2 models associate expressions with the aberrations on different loci but have known mechanistic links. Layer 3 models associate expressions with nonlocal aberrations which have unknown mechanistic links. We applied the layered models to the integrated datasets of NCI-60 cancer cell lines and validated the results with large-scale statistical analysis. Furthermore, we discovered/reaffirmed the following prominent links: (1)Protein expressions are generally consistent with mRNA expressions. (2)Several gene expressions are modulated by composite local aberrations. For instance, CDKN2A expressions are repressed by either frame-shift mutations or DNA methylations. (3)Amplification of chromosome 6q in leukemia elevates the expression of MYB, and the downstream targets of MYB on other chromosomes are up-regulated accordingly. (4)Amplification of chromosome 3p and hypo-methylation of PAX3 together elevate MITF expression in melanoma, which up-regulates the downstream targets of MITF. (5)Mutations of TP53 are negatively associated with its direct target genes.ConclusionsThe analysis results on NCI-60 data justify the utility of the layered models for the incoming flow of cancer genomic data. Experimental validations on selected prominent links and application of the layered modeling framework to other integrated datasets will be carried out subsequently.

Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical development.

For generations the study of vocal development and its role in language has been conducted laboriously, with human transcribers and analysts coding and taking measurements from small recorded samples. Our research illustrates a method to obtain measures of early speech development through automated analysis of massive quantities of day-long audio recordings collected naturalistically in children's homes. A primary goal is to provide insights into the development of infant control over infrastructural characteristics of speech through large-scale statistical analysis of strategically selected acoustic parameters. In pursuit of this goal we have discovered that the first automated approach we implemented is not only able to track children's development on acoustic parameters known to play key roles in speech, but also is able to differentiate vocalizations from typically developing children and children with autism or language delay. The method is totally automated, with no human intervention, allowing efficient sampling and analysis at unprecedented scales. The work shows the potential to fundamentally enhance research in vocal development and to add a fully objective measure to the battery used to detect speech-related disorders in early childhood. Thus, automated analysis should soon be able to contribute to screening and diagnosis procedures for early disorders, and more generally, the findings suggest fundamental methods for the study of language in natural environments.

Large-scale statistical analysis of early failures in Cu electromigration, Part II: Scaling behavior and short-length effects

The first part of this study, presented in a separate paper, focused on the early failure mechanisms in down-flow electromigration. Since bimodality can occur at very small percentage levels, specific test structures were designed based on the Wheatstone Bridge technique. The use of these structures enabled a tested sample size past 800,000 for the 90 nm technology node, allowing a direct analysis of electromigration failure mechanisms at the single-digit ppm regime. The activation energy for the down-flow early failure mechanism was determined to be 0.83±0.01 eV, significantly lower than the usually reported activation energy of about 0.90 eV for electromigration-induced diffusion along Cu/SiCN interfaces. Very short experimental lifetimes due to small, slit-shaped voids under vias were found to control the chip lifetime at operating conditions. In this second part of our large-scale, statistical study, we will discuss the electromigration scaling behavior across 90, 65, and 45 nm technologies. Results indicate that the early failure mechanism follows the expected dependency, i.e., the lifetimes scale with the interconnect line height and the critical void size. The slitlike character of the early failure void morphology also raises concerns about the validity of the short-length effect for this mechanism. A very small amount of Cu depletion may cause failure even before a stress gradient is established. We therefore conducted large-scale statistical experiments close to the critical current density-length product (jL)∗. The results indicate that at very small failure percentages, the critical product extrapolates to about 2100±300 A/cm for SiCOH-based dielectrics in 90 nm technology. This value represents a decrease from the previously determined (jL)∗ product of about 3000±500 A/cm for the same dielectric material and technology node, acquired with single link interconnects. Utilizing the advantages of the Wheatstone Bridge technique, the total sample size encompassing 90, 65, and 45 nm technologies was increased past 1.2×106.

Large-scale statistical analysis of early failures in Cu electromigration, Part I: Dominating mechanisms

With continuing scaling of Cu-based metallization, the electromigration (EM) failure risk has remained one of the most important reliability concerns for advanced process technologies. The main factors requiring attention are the activation energy related to the dominating diffusion mechanism, the current exponent as well as the median lifetimes and lognormal standard deviation values of experimentally acquired failure time distributions. In general, the origin and scaling behavior of these parameters are relatively well understood. However, the observation of strong bimodality for the electron up-flow direction in dual-inlaid Cu interconnects has added complexity. The failure voids can occur both within the via (“early” mode) or within the trench (“late” mode). Over the last few years, bimodality has been reported also in down-flow EM, leading to very short lifetimes due to small, slit-shaped voids under vias. These voids, requiring only a very limited amount of mass movement, are generally causing concerns with respect to long-term, reliable chip operation at elevated temperatures. For a more thorough investigation of the aforementioned early failure phenomena, specific test structures were designed based on the Wheatstone Bridge (WSB) technique. The use of these structures enabled an increase in the tested sample size past 800 000 for the 90 nm technology node, allowing a direct analysis of EM failure mechanisms at the single-digit ppm regime. Results indicate that down-flow EM can exhibit bimodality at very small percentage levels, not readily identifiable with standard testing methods. The activation energy for the down-flow early failure mechanism was determined to be 0.83±0.01 eV. Within the small error bounds of this large-scale statistical experiment, this value is deemed to be significantly lower than the usually reported activation energy of 0.90 eV for EM-induced diffusion along Cu/SiCN interfaces. Due to the advantages of the WSB technique, we were also able to expand the experimental temperature range down to 150 °C, coming quite close to typical operating conditions up to 125 °C. As a result of the lowered activation energy, we conclude that the down-flow early failure mode may control the chip lifetime at operating conditions. This publication contains the first part of our large-scale statistical analysis of early failures in Cu EM. In the second part of this study, we will discuss the EM scaling behavior across 90, 65, and 45 nm technologies. In addition, short-length effects will be evaluated using our large-scale, statistical approach. Utilizing the advantages of the WSB technique, the total sample size will be increased past 1.2 million.

The distribution of wellbeing in Ireland.

There is a substantial knowledge gap about the distribution of mental health in community populations. The European Social Survey is particularly useful as it contains information on over 40,000 individuals, including 2,286 Irish adults. The objective of this study is to conduct a large scale statistical analysis to examine the distribution and determinants of mental wellbeing in a large representative sample of the Irish population. Analysis of the European Social Survey using robust multiple linear and non-linear regression techniques. The data-set contains WHO-Five scores and subjective wellbeing for a sample of 2,286 Irish people interviewed in their homes in 2005. Ireland has the second highest average WHO-Five score among the 22 countries in the European Social Survey. Multiple linear regression analysis across the distribution of WHO-Five reveals a wellbeing gradient largely related to education and social capital variables. A probit model examining the determinants of vulnerability to psychiatric morbidity reveals that a similar set of factors predict scores below the threshold point on the WHO-Five scale. The results are consistent with marked differences in mental wellbeing across education levels and variables relating to social capital factors. Such indicators provide a useful index for policy-makers and researchers. However, much further work is needed to identify causal mechanisms generating observed differences in mental health across different socioeconomic groups.

Abundant conserved microRNA target sites in the 5′-untranslated region and coding sequence

Recent studies have shown that miRNAs can target the promoter and CDS region. Thus, we predicted miRNA target sites in the 5'-UTR, CDS and 3'-UTR of Homo sapiens, Mus musculus and Drosophila melanogaster using miRanda and TargetScan. Target-site densities normalized with the average region length were higher in the 5'-UTR than 3'-UTR in all three organisms but were lower in the negative data set. Interestingly, the putative target sites were more conserved than non-target regions in both the 5'-UTR and 3'-UTR, implying that target sites in the 5'-UTR are subject to high selective pressure and might be functional. In Drosophila, 48 of 78 (61.5%) miRNAs showed high similarities with predicted siRNAs. Based on the results of previous experimental studies and a large-scale statistical analysis, we conclude that miRNA-mediated regulation is not limited to the 3'-UTR. However, the functionality of target sites in the 5'-UTR and CDS requires thorough investigation.

Structure discovery in PPI networks using pattern-based network decomposition

The large, complex networks of interactions between proteins provide a lens through which one can examine the structure and function of biological systems. Previous analyses of these continually growing networks have primarily followed either of two approaches: large-scale statistical analysis of holistic network properties, or small-scale analysis of local topological features. Meanwhile, investigation of meso-scale network structure (above that of individual functional modules, while maintaining the significance of individual proteins) has been hindered by the computational complexity of structural search in networks. Examining protein-protein interaction (PPI) networks at the meso-scale may provide insights into the presence and form of relationships between individual protein complexes and functional modules. In this article, we present an efficient algorithm for performing sub-graph isomorphism queries on a network and show its computational advantage over previous methods. We also present a novel application of this form of topological search which permits analysis of a network's structure at a scale between that of individual functional modules and that of network-wide properties. This analysis provides support for the presence of hierarchical modularity in the PPI network of Saccharomyces cerevisiae.

Ab initio thermochemistry using optimal-balance models with isodesmic corrections: The ATOMIC protocol

A theoretical composite approach, termed ATOMIC for Ab initio Thermochemistry using Optimal-balance Models with Isodesmic Corrections, is introduced for the calculation of molecular atomization energies and enthalpies of formation. Care is taken to achieve optimal balance in accuracy and cost between the various components contributing to high-level estimates of the fully correlated energy at the infinite-basis-set limit. To this end, the energy at the coupled-cluster level of theory including single, double, and quasiperturbational triple excitations is decomposed into Hartree-Fock, low-order correlation (MP2, CCSD), and connected-triples contributions and into valence-shell and core contributions. Statistical analyses for 73 representative neutral closed-shell molecules containing hydrogen and at least three first-row atoms (CNOF) are used to devise basis-set and extrapolation requirements for each of the eight components to maintain a given level of accuracy. Pople's concept of bond-separation reactions is implemented in an ab initio framework, providing for a complete set of high-level precomputed isodesmic corrections which can be used for any molecule for which a valence structure can be drawn. Use of these corrections is shown to lower basis-set requirements dramatically for each of the eight components of the composite model. A hierarchy of three levels is suggested for isodesmically corrected composite models which reproduce atomization energies at the reference level of theory to within 0.1 kcal/mol (A), 0.3 kcal/mol (B), and 1 kcal/mol (C). Large-scale statistical analysis shows that corrections beyond the CCSD(T) reference level of theory, including coupled-cluster theory with fully relaxed connected triple and quadruple excitations, first-order relativistic and diagonal Born-Oppenheimer corrections can normally be dealt with using a greatly simplified model that assumes thermoneutral bond-separation reactions and that reduces the estimate of these corrections to the simple task of adding up bond increments. Preliminary validation with experimental enthalpies of formation using the subset of neutral closed-shell (HCNOF) species contained in the G3/99 test set indicates that the ATOMIC protocol performs slightly better than the popular G3 approach. The newly introduced protocol does not require empirical calibration, however, and it is still efficient enough to be applied routinely to molecules with 10 or 20 nonhydrogen atoms.

Random amino acid mutations and protein misfolding lead to Shannon limit in sequence-structure communication.

The transmission of genomic information from coding sequence to protein structure during protein synthesis is subject to stochastic errors. To analyze transmission limits in the presence of spurious errors, Shannon's noisy channel theorem is applied to a communication channel between amino acid sequences and their structures established from a large-scale statistical analysis of protein atomic coordinates. While Shannon's theorem confirms that in close to native conformations information is transmitted with limited error probability, additional random errors in sequence (amino acid substitutions) and in structure (structural defects) trigger a decrease in communication capacity toward a Shannon limit at 0.010 bits per amino acid symbol at which communication breaks down. In several controls, simulated error rates above a critical threshold and models of unfolded structures always produce capacities below this limiting value. Thus an essential biological system can be realistically modeled as a digital communication channel that is (a) sensitive to random errors and (b) restricted by a Shannon error limit. This forms a novel basis for predictions consistent with observed rates of defective ribosomal products during protein synthesis, and with the estimated excess of mutual information in protein contact potentials.

Political Marketing Matters

ABSTRACT The effects of political marketing are difficult to measure. This article contends that “multi-dimensional case analysis”-i.e., the comparison of similar elections across demographic levels and across time-can reliably assess the impact of political marketing on campaign outcomes. A multi-dimensional examination of Congressman Harold Ford Jr.'s sophomore surge demonstrates the potential impact of candidate positioning. Specifically, the congressman's shift to the center of his Memphis, Tennessee, constituency significantly enhanced his electoral performance. This substantive finding carries a methodological lesson: the evaluation of marketing effects is greatly improved through the use of a tightly structured research design that employs, not large-scale statistical analysis or controlled field experimentation, but in-depth, case-by-case investigation.

Insight into human sex ratio imbalance: the more boys born, the more infertile men

This study investigated, through large-scale statistical analysis of the global population, whether the human sex ratio is skewing worldwide, and if so, why and how it shifts, and the impact of any shift on human reproduction. A significant imbalance of the sex ratio was observed in the whole human population, resulting in a vicious circle where the more boys born, the more infertile men occur as a consequence of the adjustment of the human sex ratio. It is proposed that both accelerated degeneration of the Y chromosome and dynamically differential fertilization of the egg by the X- and Y-bearing spermatozoa may contribute to the changing structure of the human population, suggesting a potential selection pressure favouring an alternative sex-determining/differentiation system for the human species: the DMRT1 gene on the autosome may tend to replace the SRY gene on the Y chromosome as a master sex-determining gene during evolution. These viewpoints may open a debate on the future of the human race, and assist in guiding diagnostic, prognostic and preventive treatments of human infertility and in defining population policy.

Identification of Genes with Potential Roles in Apple Fruit Development and Biochemistry through Large-Scale Statistical Analysis of Expressed Sequence Tags

Advanced studies of apple (Malus domestica Borkh) development, physiology, and biochemistry have been hampered by the lack of appropriate genomics tools. One exception is the recent acquisition of extensive expressed sequence tag (EST) data. The entire available EST dataset for apple resulted from the efforts of at least 20 contributors and was derived from more than 70 cDNA libraries representing diverse transcriptional profiles from a variety of organs, fruit parts, developmental stages, biotic and abiotic stresses, and from at least nine cultivars. We analyzed apple EST sequences available in public databanks using statistical algorithms to identify those apple genes that are likely to be highly expressed in fruit, expressed uniquely or preferentially in fruit, and/or temporally or spatially regulated during fruit growth and development. We applied these results to the analysis of biochemical pathways involved in biosynthesis of precursors for volatile esters and identified a subset of apple genes that may participate in generating flavor and aroma components found in mature fruit.

Unravelling the regulatory structure of biochemical networks using stimulus response experiments and large-scale model selection

To unravel the complex in vivo regulatory interdependences of biochemical networks, experiments with the living organism are absolutely necessary. Stimulus response experiments (SREs) have become increasingly popular in recent years. The response of metabolite concentrations from all major parts of the central metabolism is monitored over time by modem analytical methods, producing several thousand data points. SREs are applied to determine enzyme kinetic parameters and to find unknown enzyme regulatory mechanisms. Owing to the complex regulatory structure of metabolic networks and the amount of measured data, the evaluation of an SRE has to be extensively supported by modelling. If the enzyme regulatory mechanisms are part of the investigation, a large number of models with different enzyme kinetics have to be tested for their ability to reproduce the observed behaviour. In this contribution, a systematic model-building process for data-driven exploratory modelling is introduced with the aim of discovering essential features of the biological system. The process is based on data pre-processing, correlation-based hypothesis generation, automatic model family generation, large-scale model selection and statistical analysis of the best-fitting models followed by an extraction of common features. It is illustrated by the example of the aromatic amino acid synthesis pathway in Escherichia coli.

Hypothesis Decay?: Blood Lead–Fluoridation Link Not Confirmed

Vol. 114, No. 1 EnvironewsOpen AccessHypothesis Decay?: Blood Lead–Fluoridation Link Not Confirmedis accompanied byBlood Lead Concentrations in Children and Method of Water Fluoridation in the United States, 1988–1994 John Tibbetts John Tibbetts Search for more papers by this author Published:1 January 2006https://doi.org/10.1289/ehp.114-a49AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit Numerous studies of various populations have shown that adding fluoride to drinking water prevents dental decay. However, a 1999 study in Massachusetts and a 2000 study in New York reported associations between the use of silicofluoride compounds in community water systems and elevated blood lead (PbB) concentrations in children. Now a team of researchers has tested the hypothesis generated by the Massachusetts and New York studies against findings from two other studies and found no cause for concern [EHP 114:130–134].As of 2000, the Centers for Disease Control and Prevention estimated that more than 162 million Americans were receiving fluoridated water. In the United States, several agents are used for fluoridation, including silicofluoride compounds (sodium silicofluoride and hydrofluosilicic acid) and sodium fluoride. Researchers with the Massachusetts and New York studies hypothesized that the silicofluoride compounds in tap water might enhance lead leaching from pipes and increase lead absorption from the water itself. Elevated PbB concentrations in children are associated with a host of cognitive, developmental, and behavioral impairments so serious that lead-based paint was banned in the United States in 1978 and lead water pipe solder was banned in the 1980s.The current research group evaluated the relationship between water fluoridation method and PbB concentrations in children by conducting a large-scale statistical analysis of two other preexisting studies: the 1992 Fluoridation Census and the Third National Health and Nutrition Examination Survey (NHANES III). In analyzing data from NHANES III and the 1992 Fluoridation Census, the team improved on prior analyses by log-transforming raw PbB concentration and by including information on possible confounding factors missing from the Massachusetts and New York studies. These included poverty status, urbanicity, duration of residence, and year in which the dwelling was built.The NHANES III sample was comprehensive, representing more than 52 million U.S. children. This survey also oversampled young children, older adults, non-Hispanic blacks, and Mexican Americans to ensure that population estimates for these groups would be statistically reliable.The team found that, overall, the PbB concentrations of children who lived in counties receiving silicofluorides did not differ significantly from the PbB concentrations of children living in counties without fluoridated water. This was true even when researchers controlled for the year in which children’s homes were built. Given these findings, the team states there is no support for concerns that silicofluorides in community water systems cause higher PbB concentrations in children.However, the investigators acknowledge that their analysis has limitations. For example, NHANES III did not measure the lead content of drinking water consumed by study participants. Also, the team did not control for factors such as density of older housing, and they were unable to control for the solubility of lead in pipes affected by different temperatures and water hardness.Because of these limitations, the investigators cannot completely rule out a link between water fluoridation method and lead uptake in children, particularly in children living in older dwellings. They speculate that other studies, possibly those including chemical investigation and animal toxicology, could yield additional valuable data. They conclude that efforts to prevent dental decay via the use of fluoridated drinking water should continue unless a causal effect of specific fluoridation methods on PbB concentration is demonstrated by additional research.Refreshing news.Although some questions remain, a new data analysis fails to confirm fears that fluoridation of drinking water results in higher blood lead along with stronger teeth.FiguresReferencesRelatedDetailsRelated articlesBlood Lead Concentrations in Children and Method of Water Fluoridation in the United States, 1988–199417 August 2005Environmental Health Perspectives Vol. 114, No. 1 January 2006Metrics About Article Metrics Publication History Originally published1 January 2006Published in print1 January 2006 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. 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CGH-Profiler: Data mining based on genomic aberration profiles

BackgroundCGH-Profiler is a program that supports the analysis of genomic aberrations measured by Comparative Genomic Hybridisation (CGH). Comparative genomic hybridisation (CGH) is a well-established, molecular cytogenetic method that allows the detection of chromosomal imbalances in entire genomes. This technique is widely used in routine molecular diagnostics. Typically, chromosomal imbalances are described in a complex syntax based on the International Standard for Cytogenetic Nomenclature (ISCN). This semantic description of chromosomal imbalances hinders a large-scale statistical analysis across different experiments, e.g. for finding aberration patterns associated with a particular disease type or state.ResultsCGH-Profiler circumvents the semantic ISCN description by importing data from different CGH system vendors and by directly transferring the data into a table format that is readily accessible for subsequent statistical analysis. CGH-profiler comes with different consistency checks, calculates various statistics and automatically assigns a median copy number ratio to each chromosomal band.Import of CGH profiles from different CGH system vendors is already supported; its extension to other systems can be readily achieved through Perl scripts.CGH profiler can also be used to analyse comparative expressed sequence hybridisation (CESH) data. CESH reveals gene expression patterns according to chromosomal locations in a similar manner as CGH detects chromosomal imbalances.ConclusionCGH-Profiler is a useful tool for processing of CGH and CESH data.