Detection of Recurrent Copy Number Alterations in the Genome: a Probabilistic Approach

Copy number variation (CNV) in genomic DNA is linked to a variety of human diseases (including cancer, HIV acquisition, autoimmune and neurodegenerative diseases), and array-based CGH (aCGH) is currently the main technology to locate CNVs. Several methods can analyze aCGH data at the single sample level, but disease-critical genes are more likely to be found in regions that are common or recurrent among samples. Unfortunately, defining recurrent CNV regions remains a challenge. Moreover, the heterogeneous nature of many diseases requires that we search for CNVs that affect only some subsets of the samples (without prior knowledge of which regions and subsets of samples are affected), but this is neglected by current methods. We have developed two methods to define recurrent CNV regions. Our methods are unique and qualitatively different from existing approaches: they detect both regions over the complete set of arrays and alterations that are common only to some subsets of the samples and, thus, CNV alterations that might characterize previously unknown groups; they use probabilities of alteration as input (not discretized gain/loss calls, which discard uncertainty and variability) and return probabilities of being a shared common region, thus allowing researchers to modify thresholds as needed; the two parameters of the methods have an immediate, straightforward, biological interpretation. Using data from previous studies, we show that we can detect patterns that other methods miss and, by using probabilities, that researchers can modify, as needed, thresholds of immediate interpretability to answer specific research questions. These methods are a qualitative advance in the location of recurrent CNV regions and will be instrumental in efforts to standardize definitions of recurrent CNVs and cluster samples with respect to patterns of CNV, and ultimately in the search for genomic regions harboring disease-critical genes

Finding Recurrent Regions of Copy Number Variation: A Review

Copy number variation (CNV) in genomic DNA is linked to a variety of human diseases, and array-based CGH (aCGH) is currently the main technology to locate CNVs. Although many methods have been developed to analyze aCGH from a single array/subject, disease-critical genes are more likely to be found in regions that are common or recurrent among subjects. Unfortunately, finding recurrent CNV regions remains a challenge. We review existing methods for the identification of recurrent CNV regions. The working definition of ``common\u27\u27 or ``recurrent\u27\u27 region differs between methods, leading to approaches that use different types of input (discretized output from a previous CGH segmentation analysis or intensity ratios), or that incorporate to varied degrees biological considerations (which play a role in the identification of ``interesting\u27\u27 regions and in the details of null models used to assess statistical significance). Very few approaches use and/or return probabilities, and code is not easily available for several methods. We suggest that finding recurrent CNVs could benefit from reframing the problem in a biclustering context. We also emphasize that, when analyzing data from complex diseases with significant among-subject heterogeneity, methods should be able to identify CNVs that affect only a subset of subjects. We make some recommendations about choice among existing methods, and we suggest further methodological research

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

Asterias (\url{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). Most of our applications allow the user to obtain additional information for user-selected genes by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data; converting between different types of gene/clone and protein identifiers; filtering and imputation; finding differentially expressed genes related to patient class and survival data; searching for models of class prediction; using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity; searching for molecular signatures and predictive genes with survival data; detecting regions of genomic DNA gain or loss. 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.Comment: web based application; 3 figure

Smart dual thermal network

Conventional district heating (DH) systems enable demand aggregation at district level and can provide high centralized heat generation performance values. However, thermal Renewable Energy Sources (RES) deployment at building level still remains low, and exploitation suboptimal, as it is limited by the instantaneous thermal load and storage capacity availability of each building. Buildings play the role of consumers that request a variable amount of heat over time and the thermal network the role of unidirectional heat supplier, without any smart interaction. The FP7 project A2PBEER has developed an innovative Smart Dual Thermal Network concept based on RES and Combined Heat and Power (CHP) as generation technologies, that enables transforming existing suboptimal DH systems, into integrated thermal networks with optimized performance and building level RES system production exploitation. It is based on an innovative Smart Dual Building Thermal Substation concept, which allows a bidirectional heat exchange of the buildings with the thermal network, and to aggregate district level distributed production and storage capacity (Virtual District Plant). With this approach buildings become prosumers maximizing decentralized RES production exploitation, as any possible local heat production surplus on any building of the district, will be delivered to the network to be used by other buildings. Additionally, this thermal network allows the delivery of the energy necessary to meet the heating and cooling demand of the buildings through a single hot water distribution network. In this way, it is possible to upgrade conventional DH systems to district heating and cooling systems, without the construction of a district cooling plant and a dedicated cooling distribution network. Cooling is produced at building level through sorption technologies using locally deployed solar collectors and the thermal network as energy sources. Finally, the district typologies and climatic conditions that maximize the potential of this thermal network concept have been identified.The research activities leading to the described developments and results, were funded by the FP7 project A2PBEER, under grant agreement No 906090. Special thanks to Olof Hallström and ClimateWell AB for making the TRNSYS model of the innovative sorption system and developing the component level simulation work

Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH

Genomic DNA copy-number alterations (CNAs) are associated with complex diseases, including cancer: CNAs are indeed related to tumoral grade, metastasis, and patient survival. CNAs discovered from array-based comparative genomic hybridization (aCGH) data have been instrumental in identifying disease-related genes and potential therapeutic targets. To be immediately useful in both clinical and basic research scenarios, aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question, “What is the probability that this gene/region has CNAs?” Current approaches fail, however, to meet these requirements. Here, we introduce reversible jump aCGH (RJaCGH), a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging. RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis. RJaCGH outperforms alternative methods, and the performance difference is even larger with noisy data and highly variable interprobe distance, both commonly found features in aCGH data. Furthermore, our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data. In summary, we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases

Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH

Genomic DNA copy-number alterations (CNAs) are associated with complex diseases, including cancer: CNAs are indeed related to tumoral grade, metastasis, and patient survival. CNAs discovered from array-based comparative genomic hybridization (aCGH) data have been instrumental in identifying disease-related genes and potential therapeutic targets. To be immediately useful in both clinical and basic research scenarios, aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question, “What is the probability that this gene/region has CNAs?” Current approaches fail, however, to meet these requirements. Here, we introduce reversible jump aCGH (RJaCGH), a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging. RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis. RJaCGH outperforms alternative methods, and the performance difference is even larger with noisy data and highly variable interprobe distance, both commonly found features in aCGH data. Furthermore, our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data. In summary, we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases