Low-cost and clinically applicable copy number profiling using repeat DNA

BACKGROUND

Somatic copy number alterations (SCNAs) are an important class of genomic alteration in cancer. They are frequently observed in cancer samples, with studies showing that, on average, SCNAs affect 34% of a cancer cell's genome. Furthermore, SCNAs have been shown to be major drivers of tumour development and have been associated with response to therapy and prognosis. Large-scale cancer genome studies suggest that tumours are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Despite the frequency of SCNAs and their clinical relevance, the use of genomics assays in the clinic is biased towards targeted gene panels, which identify SNVs but provide limited scope to detect SCNAs throughout the genome. There is a need for a comparably low-cost and simple method for high-resolution SCNA profiling.

RESULTS

We present conliga, a fully probabilistic method that infers SCNA profiles from a low-cost, simple, and clinically-relevant assay (FAST-SeqS). When applied to 11 high-purity oesophageal adenocarcinoma samples, we obtain good agreement (Spearman's rank correlation coefficient, r_s=0.94) between conliga's inferred SCNA profiles using FAST-SeqS data (approximately £14 per sample) and those inferred by ASCAT using high-coverage WGS (gold-standard). We find that conliga outperforms CNVkit (r_s=0.89), also applied to FAST-SeqS data, and is comparable to QDNAseq (r_s=0.96) applied to low-coverage WGS, which is approximately four-fold more expensive, more laborious and less clinically-relevant. By performing an in silico dilution series experiment, we find that conliga is particularly suited to detecting SCNAs in low tumour purity samples. At two million reads per sample, conliga is able to detect SCNAs in all nine samples at 3% tumour purity and as low as 0.5% purity in one sample. Crucially, we show that conliga's hidden state information can be used to decide when a sample is abnormal or normal, whereas CNVkit and QDNAseq cannot provide this critical information.

CONCLUSIONS

We show that conliga provides high-resolution SCNA profiles using a convenient, low-cost assay. We believe conliga makes FAST-SeqS a more clinically valuable assay as well as a useful research tool, enabling inexpensive and fast copy number profiling of pre-malignant and cancer samples.

Metabolic switches and adaptations deduced from the proteomes of Streptomyces coelicolor wild type and phoP mutant grown in batch culture

Bacteria in the genus Streptomyces are soil-dwelling oligotrophs and important producers of secondary metabolites. Previously, we showed that global messenger RNA expression was subject to a series of metabolic and regulatory switches during the lifetime of a fermentor batch culture of Streptomyces coelicolor M145. Here we analyze the proteome from eight time points from the same fermentor culture and, because phosphate availability is an important regulator of secondary metabolite production, compare this to the proteome of a similar time course from an S. coelicolor mutant, INB201 (ΔphoP), defective in the control of phosphate utilization. The proteomes provide a detailed view of enzymes involved in central carbon and nitrogen metabolism. Trends in protein expression over the time courses were deduced from a protein abundance index, which also revealed the importance of stress pathway proteins in both cultures. As expected, the ΔphoP mutant was deficient in expression of PhoP-dependent genes, and several putatively compensatory metabolic and regulatory pathways for phosphate scavenging were detected. Notably there is a succession of switches that coordinately induce the production of enzymes for five different secondary metabolite biosynthesis pathways over the course of the batch cultures.

On reverse engineering of gene interaction networks using time course data with repeated measurements

MOTIVATION

Gene expression measurements are the most common data source for reverse engineering gene interaction networks. When dealing with destructive sampling in time course experiments, it is common to average any available measurements for each time point and to treat this as the actual time series data for fitting the network, neglecting the variability contained in the repeated measurements. Proceeding in such a way can affect the retrieved network topology.

RESULTS

We propose a fully Bayesian method for reverse engineering a gene interaction network, based on time course data with repeated measurements. The observations are treated as surrogate measurements of the underlying gene expression. As these measurements often contain outliers, we use a non-Gaussian specification for dealing with measurement error. The network interactions are assumed linear and an autoregressive model is specified, augmented with indicator variables that allow inference on the topology of the network. We analyse two in silico and one in vivo experiments, the latter dealing with the circadian clock in Arabidopsis thaliana. A systematic attenuation of the estimated regulation strengths and a concomitant overestimation of their precision is demonstrated when measurement error is disregarded. Thus, a clear improvement in the inferred topology for the synthetic datasets is demonstrated when this is included. Also, the influence of outliers in the retrieved network is demonstrated when using the in vivo data.

AVAILABILITY

Matlab code and data used in the article are available from http://go.warwick.ac.uk/majuarez/home/materials.

Pomelo II: finding differentially expressed genes

Pomelo II (http://pomelo2.bioinfo.cnio.es) is an open-source, web-based, freely available tool for the analysis of gene (and protein) expression and tissue array data. Pomelo II implements: permutation-based tests for class comparisons (t-test, ANOVA) and regression; survival analysis using Cox model; contingency table analysis with Fisher's exact test; linear models (of which t-test and ANOVA are especial cases) that allow additional covariates for complex experimental designs and use empirical Bayes moderated statistics. Permutation-based and Cox model analysis use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. The source code is available, allowing for extending and reusing the software. A comprehensive test suite is also available, and covers both the user interface and the numerical results. The possibility of including additional covariates, parallelization of computation, open-source availability of the code and comprehensive testing suite make Pomelo II a unique tool.

Asterias: integrated analysis of expression and aCGH data using an open-source, web-based, parallelized software suite

Asterias (http://www.asterias.info) is an open-source, web-based, suite for the analysis of gene expression and aCGH data. Asterias implements validated statistical methods, and most of the applications use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. These applications cover from array normalization to imputation and preprocessing, differential gene expression analysis, class and survival prediction and aCGH analysis. The source code is available, allowing for extention and reuse of the software. The links and analysis of additional functional information, parallelization of computation and open-source availability of the code make Asterias a unique suite that can exploit features specific to web-based environments.

Asterias: a parallelized web-based suite for the analysis of expression and aCGH data

The analysis of expression and CGH arrays plays a central role in the study of complex diseases, especially cancer, including finding markers for early diagnosis and prognosis, choosing an optimal therapy, or increasing our understanding of cancer development and metastasis. Asterias (http://www.asterias.info) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI) and run on a server with 60 CPUs for computation; compared to a desktop or server-based but not parallelized application, parallelization provides speed ups of factors up to 50. Most of our applications allow the user to obtain additional information for user-selected genes (chromosomal location, PubMed ids, Gene Ontology terms, etc.) by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data (DNMAD); converting between different types of gene/clone and protein identifiers (IDconverter/IDClight); filtering and imputation (preP); finding differentially expressed genes related to patient class and survival data (Pomelo II); searching for models of class prediction (Tnasas); using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity (GeneSrF); searching for molecular signatures and predictive genes with survival data (SignS); detecting regions of genomic DNA gain or loss (ADaCGH). The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.

Asterias: A Parallelized Web-based Suite for the Analysis of Expression and aCGH Data

The analysis of expression and CGH arrays plays a central role in the study of complex diseases, especially cancer, including finding markers for early diagnosis and prognosis, choosing an optimal therapy, or increasing our understanding of cancer development and metastasis. Asterias ( http://www.asterias.info ) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI) and run on a server with 60 CPUs for computation; compared to a desktop or server-based but not parallelized application, parallelization provides speed ups of factors up to 50. Most of our applications allow the user to obtain additional information for user-selected genes (chromosomal location, PubMed ids, Gene Ontology terms, etc.) by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data (DNMAD); converting between different types of gene/clone and protein identifiers (IDconverter/IDClight); filtering and imputation (preP); finding differentially expressed genes related to patient class and survival data (Pomelo II); searching for models of class prediction (Tnasas); using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity (GeneSrF); searching for molecular signatures and predictive genes with survival data (SignS); detecting regions of genomic DNA gain or loss (ADaCGH). The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.

The measurement of multiple drug use and its relationship to the patterning of alcohol intake

The increasing prevalence of multiple drug use among adolescents presents researchers with complex conceptual and measurement issues. This work examines definitions of and indicators of multiple drug use involvement, as well as the interrelationships between indices of drug involvement incorporating different dimensions. Data regarding the relationship between patterns of alcohol use and illicit drug use in a population of 1473 teenagers interviewed in a northwestern metropolitan community indicate that : (1) regardless of the dimensions incorporated into four different indices of drug involvement examined, intercorrelations between indices were strong; (2) although the correlations between indices of drug involvement and overall levels of alcohol intake were uniformly weak, it was shown that levels of drug involvement vary directly with the maximum volume of alcohol used. Although the results of the current investigation must be viewed cautiously because of the concentration of heavy users of both alcohol and drugs in the population upon which the research is based, among adolescents, the spacing of drinking episodes and the patterning of alcohol intake may be more important determinants of polydrug involvement thant the quantity of alcohol consumed.

Psychiatric problems in women admitted to an alcoholic detoxification center

The authors studied 293 consecutive female admissions to a public detoxification facility. One-half had a primary diagnosis of alcoholism; the rest had primary diagnosis of affective disorder (14%), antisocial personality (14%), drug abuse (6%) or did not meet the criteria for alcoholism (13%). Each subgroup had unique social backgrounds and characteristic histories of involvement with alcohol, drugs, and antisocial problems. The primary alcoholic woman in the detoxification center resembles the primary alcoholic man in the same setting. The authors underscore the importance of socioeconomic strata and primary versus secondary diagnosis in understanding the prognosis for alcoholics.

Drug abuse among alcoholic women

Of 293 women in an alcoholic treatment center, two-thirds have received prescriptions for drugs of potential abuse, usually hypnotic and antianxiety drugs. One-third of the women admitted abusing substances; 80% of these subjects got prescriptions for potential drugs of abuse while actively abusing. These figures are alarming in light of the lack of evidence that antianxiety drugs, hypnotics, and stimulants are effective in treating alcoholics and drug abusers. Drug-misusing women in this sample reported more suicide attempts and early antisocial problems and had received more psychiatric care than nonabusers. The authors urge that hypnotics, antianxiety drugs, and stimulants almost never be prescribed to outpatient alcoholics and that analgesics be prescribed only with great care.

Alcohol-related problems in adolescents and women

In the roles of student and homemaker, youthful and female problem drinkers can easily hide alcoholism. Compared with adult male alcoholics, their intake of alcohol is less but they are more likely to use other drugs concomitantly. An understanding of their unique characteristics will help the physician recognize patients in these groups with alcohol-related problems.

Stressful life events and alcohol problems among women seen at a detoxication center

No strong temporal associations were found between the occurrence of stressful life events and the onset of alcohol problems.

Propoxyphene and Phencyclidine (PCP) use in adolescents

Trained interviewers utilized a structured research instrument to analyze drug use patterns, personal and familial psychiatric problems, and social backgrounds of a consecutive series of 355 adolescent referred by courts to alcohol counselling and education centers in King County. Within this sample, 4% of the subjects used propoxyphene alone, 16% reported use of phencyclidine (PCP) alone, and 6% reported abuse of both substances. The data consistently reveals that the use of one of these more unusual substances is likely to occur in people with more antisocial, drug, and alcohol problems. Use of both drugs was associated with the most pervasive antisocial history, with over one-third of these subjects fulfilling strict criteria for the antisocial personality. The treatment and prognostic implications of these findings are discussed.

Correlation between two measures of self-esteem and drug use in a college sample

The relationship between drug and alcohol use and self-esteem was investigated using students living in a co-ed dormitory. A questionnaire was distributed containing four sections including demographic information, the Coopersmith Self-esteem Inventory (1967), an A Priori Identity Crisis scale, and a frequency of drug/alcohol use scale. 66% of the questionnaires were returned, yielding a sample of 171 males and 219 females. Heavy use of alcohol was related to low self-esteem. No relationship, however, was found between drug use and self-esteem. The relationship between drug and alcohol use and psychopathology is discussed as being a continuum.