Person: Wall, Dennis Paul
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Wall
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Dennis Paul
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Wall, Dennis Paul
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Publication Personalized cloud-based bioinformatics services for research and education: Use cases and the elasticHPC package(BioMed Central, 2012) El-Kalioby, Mohamed; Abouelhoda, Mohamed; Krüger, Jan; Giegerich, Robert; Sczyrba, Alexander; Wall, Dennis Paul; Tonellato, PeterBackground: Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. Results: In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Conclusions: Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org.Publication Cloud Computing for Comparative Genomics with Windows Azure Platform(Libertas Academica, 2012) Kim, Insik; Jung, Jae-Yoon; DeLuca, Todd; Nelson, Tristan; Wall, Dennis PaulCloud computing services have emerged as a cost-effective alternative for cluster systems as the number of genomes and required computation power to analyze them increased in recent years. Here we introduce the Microsoft Azure platform with detailed execution steps and a cost comparison with Amazon Web Services.Publication Cross-Pollination of Research Findings, Although Uncommon, May Accelerate Discovery of Human Disease Genes(BioMed Central, 2012) Duda, Marlena; Nelson, Tristan; Wall, Dennis PaulBackground: Technological leaps in genome sequencing have resulted in a surge in discovery of human disease genes. These discoveries have led to increased clarity on the molecular pathology of disease and have also demonstrated considerable overlap in the genetic roots of human diseases. In light of this large genetic overlap, we tested whether cross-disease research approaches lead to faster, more impactful discoveries. Methods: We leveraged several gene-disease association databases to calculate a Mutual Citation Score (MCS) for 10,853 pairs of genetically related diseases to measure the frequency of cross-citation between research fields. To assess the importance of cooperative research, we computed an Individual Disease Cooperation Score (ICS) and the average publication rate for each disease. Results: For all disease pairs with one gene in common, we found that the degree of genetic overlap was a poor predictor of cooperation (r\(^2\)=0.3198) and that the vast majority of disease pairs (89.56%) never cited previous discoveries of the same gene in a different disease, irrespective of the level of genetic similarity between the diseases. A fraction (0.25%) of the pairs demonstrated cross-citation in greater than 5% of their published genetic discoveries and 0.037% cross-referenced discoveries more than 10% of the time. We found strong positive correlations between ICS and publication rate (r\(^2\)=0.7931), and an even stronger correlation between the publication rate and the number of cross-referenced diseases (r\(^2\)=0.8585). These results suggested that cross-disease research may have the potential to yield novel discoveries at a faster pace than singular disease research. Conclusions: Our findings suggest that the frequency of cross-disease study is low despite the high level of genetic similarity among many human diseases, and that collaborative methods may accelerate and increase the impact of new genetic discoveries. Until we have a better understanding of the taxonomy of human diseases, cross-disease research approaches should become the rule rather than the exception.Publication Autworks: a Cross-Disease Network Biology Application for Autism and Related Disorders(BioMed Central, 2012) Nelson, Tristan; Jung, Jae-Yoon; DeLuca, Todd; Hinebaugh, Byron Kent; St Gabriel, Kristian Che; Wall, Dennis PaulBackground: The genetic etiology of autism is heterogeneous. Multiple disorders share genotypic and phenotypic traits with autism. Network based cross-disorder analysis can aid in the understanding and characterization of the molecular pathology of autism, but there are few tools that enable us to conduct cross-disorder analysis and to visualize the results. Description: We have designed Autworks as a web portal to bring together gene interaction and gene-disease association data on autism to enable network construction, visualization, network comparisons with numerous other related neurological conditions and disorders. Users may examine the structure of gene interactions within a set of disorder-associated genes, compare networks of disorder/disease genes with those of other disorders/diseases, and upload their own sets for comparative analysis. Conclusions: Autworks is a web application that provides an easy-to-use resource for researchers of varied backgrounds to analyze the autism gene network structure within and between disorders.Publication Streaming Support for Data Intensive Cloud-Based Sequence Analysis(Hindawi Publishing Corporation, 2013) Issa, Shadi A.; Kienzler, Romeo; El-Kalioby, Mohamed; Tonellato, Peter; Wall, Dennis Paul; Bruggmann, Rémy; Abouelhoda, MohamedCloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of “resources-on-demand” and “pay-as-you-go”, scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation.Publication Phylogenetically informed logic relationships improve detection of biological network organization(BioMed Central, 2011) Cui, Jike; DeLuca, Todd; Jung, Jae-Yoon; Wall, Dennis PaulBackground: A "phylogenetic profile" refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses. Results: Taking advantage of a phylogenetic matrix constructed from the large orthologs database Roundup, we invented a method to create balanced profiles for individual triplets of genes that guarantee equal weight on the different phylogenetic scenarios of coevolution between genes. When we applied this idea to LAPP, the method to search for logic triplets of genes, the balanced profiles resulted in significant performance improvement and the discovery of hundreds of thousands more putative triplets than unadjusted profiles. We found that logic triplets detected biological network organization and identified key proteins and their functions, ranging from neighbouring proteins in local pathways, to well separated proteins in the whole pathway, and to the interactions among different pathways at the system level. Finally, our case study suggested that the directionality in a logic relationship and the profile of a triplet could disclose the connectivity between the triplet and surrounding networks. Conclusion: Balanced profiles are superior to the raw profiles employed by traditional methods of phylogenetic profiling in searching for high order gene sets. Gene triplets can provide valuable information in detection of biological network organization and identification of key genes at different levels of cellular interaction.Publication Use of Machine Learning to Shorten Observation-based Screening and Diagnosis of Autism(Nature Publishing Group, 2012) Wall, Dennis Paul; Kosmicki, Jack; DeLuca, Todd; Harstad, Elizabeth; Fusaro, Vincent AlfredThe Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk.Publication Identification of Autoimmune Gene Signatures in Autism(Nature Publishing Group, 2011) Jung, J-Y; Kohane, Isaac; Wall, Dennis PaulThe role of the immune system in neuropsychiatric diseases, including autism spectrum disorder (ASD), has long been hypothesized. This hypothesis has mainly been supported by family cohort studies and the immunological abnormalities found in ASD patients, but had limited findings in genetic association testing. Two cross-disorder genetic association tests were performed on the genome-wide data sets of ASD and six autoimmune disorders. In the polygenic score test, we examined whether ASD risk alleles with low effect sizes work collectively in specific autoimmune disorders and show significant association statistics. In the genetic variation score test, we tested whether allele-specific associations between ASD and autoimmune disorders can be found using nominally significant single-nucleotide polymorphisms. In both tests, we found that ASD is probabilistically linked to ankylosing spondylitis (AS) and multiple sclerosis (MS). Association coefficients showed that ASD and AS were positively associated, meaning that autism susceptibility alleles may have a similar collective effect in AS. The association coefficients were negative between ASD and MS. Significant associations between ASD and two autoimmune disorders were identified. This genetic association supports the idea that specific immunological abnormalities may underlie the etiology of autism, at least in a number of cases.Publication Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism(Public Library of Science, 2012) Wall, Dennis Paul; Dally, Rebecca; Luyster, Rhiannon; Jung, Jae-Yoon; DeLuca, ToddThe Autism Diagnostic Interview-Revised (ADI-R) is one of the most commonly used instruments for assisting in the behavioral diagnosis of autism. The exam consists of 93 questions that must be answered by a care provider within a focused session that often spans 2.5 hours. We used machine learning techniques to study the complete sets of answers to the ADI-R available at the Autism Genetic Research Exchange (AGRE) for 891 individuals diagnosed with autism and 75 individuals who did not meet the criteria for an autism diagnosis. Our analysis showed that 7 of the 93 items contained in the ADI-R were sufficient to classify autism with 99.9% statistical accuracy. We further tested the accuracy of this 7-question classifier against complete sets of answers from two independent sources, a collection of 1654 individuals with autism from the Simons Foundation and a collection of 322 individuals with autism from the Boston Autism Consortium. In both cases, our classifier performed with nearly 100% statistical accuracy, properly categorizing all but one of the individuals from these two resources who previously had been diagnosed with autism through the standard ADI-R. Our ability to measure specificity was limited by the small numbers of non-spectrum cases in the research data used, however, both real and simulated data demonstrated a range in specificity from 99% to 93.8%. With incidence rates rising, the capacity to diagnose autism quickly and effectively requires careful design of behavioral assessment methods. Ours is an initial attempt to retrospectively analyze large data repositories to derive an accurate, but significantly abbreviated approach that may be used for rapid detection and clinical prioritization of individuals likely to have an autism spectrum disorder. Such a tool could assist in streamlining the clinical diagnostic process overall, leading to faster screening and earlier treatment of individuals with autism.Publication The future of genomics in pathology(Faculty of 1000 Ltd, 2012) Wall, Dennis Paul; Tonellato, PeterThe recent advances in technology and the promise of cheap and fast whole genomic data offer the possibility to revolutionise the discipline of pathology. This should allow pathologists in the near future to diagnose disease rapidly and early to change its course, and to tailor treatment programs to the individual. This review outlines some of these technical advances and the changes needed to make this revolution a reality.