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Search Results: 1 - 10 of 297833 matches for " Atul J. Butte "
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Translational bioinformatics applications in genome medicine
Atul J Butte
Genome Medicine , 2009, DOI: 10.1186/gm64
Abstract: Over the past decade, a large amount of individual-level molecular data has come from the use of gene expression microarrays [1,2], proteomics [3], and DNA sequencing [4,5]. Although high-throughput measurement modalities such as these have been used in biomedical research for over a decade, the role of the bioinformatician has often been relegated to that of data analyst, librarian, database manager, distribution specialist, or software engineer. Occasionally, with introductions made early enough, bioinformaticians have been included in the early design phases of experiments, and their role noted as such on manuscripts and publications. These engineering and infrastructure roles, although important, evolved under the assumption that the scientists making these measurements already know good questions to ask but lack the specific skills to analyze, store, retrieve, and disseminate their data. Engineering roles in bioinformatics are important and are reasonably well funded today (such as in the Cancer Bioinformatics Grid (caBIG), Bioinformatics Research Network (BIRN), and the National Centers for Biomedical Computing (NCBC), all in the United States).But considering and funding solely the engineering roles in bioinformatics understates the potential function of bioinformaticians as scientists - here defined as those who come up with questions - and, even more importantly, it limits the vision for bioinformaticians to ask questions that no other scientists can ask or answer today. It has become increasingly rare for the bioinformatician to take the role of questioner, especially with regard to research that has an impact on medical care or research that yields tools for clinicians or patients. Here, I argue that the next steps needed for the field of bioinformatics are a shift in role towards asking questions and a shift in focus to medicine. The field of translational bioinformatics, defined as '...the development of storage, analytic and interpretive methods to opt
Predicting environmental chemical factors associated with disease-related gene expression data
Chirag J Patel, Atul J Butte
BMC Medical Genomics , 2010, DOI: 10.1186/1755-8794-3-17
Abstract: We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.We were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A.We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.The etiology of many diseases results from interactions between environmental factors and biological factors [1]. Our knowledge regarding interaction between environmental factors, such chemical exposure, and biological factors, such as genes and their products, is increasing with the advent of high-throughput measurement modalities. Building associations between environmental and genetic factors and disease is essential in understanding pathogenesis and creating hypotheses regarding disease etiology. However, it is currently difficult to ascertain multiple associations of chemicals to genes and disease without significant experimental investment or large-scale epidemiological study. Use of publicly-available environmental chemical factor and genomic data may facilitate the discovery of these associations.We desired to use pre-existing datasets and knowledge-bases in order to derive hypotheses regarding chemical association to disease without upfront experimental design. Specifically, we asked what environmental ch
Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges
Purvesh Khatri ,Marina Sirota,Atul J. Butte
PLOS Computational Biology , 2012, DOI: 10.1371/journal.pcbi.1002375
Abstract: Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis.
A Quick Guide for Developing Effective Bioinformatics Programming Skills
Joel T. Dudley ,Atul J. Butte
PLOS Computational Biology , 2009, DOI: 10.1371/journal.pcbi.1000589
An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus
Chirag J. Patel,Jayanta Bhattacharya,Atul J. Butte
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0010746
Abstract: Type 2 Diabetes (T2D) and other chronic diseases are caused by a complex combination of many genetic and environmental factors. Few methods are available to comprehensively associate specific physical environmental factors with disease. We conducted a pilot Environmental-Wide Association Study (EWAS), in which epidemiological data are comprehensively and systematically interpreted in a manner analogous to a Genome Wide Association Study (GWAS).
Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks
Cecily J Wolfe, Isaac S Kohane, Atul J Butte
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-227
Abstract: We developed methods to systematically explore the breadth of GBA across a large and varied corpus of expression data to answer the following question: To what extent is the GBA heuristic broadly applicable to the transcriptome and conversely how broadly is GBA captured by a priori knowledge represented in the Gene Ontology (GO)? Our study provides an investigation of the functional organization of five coexpression networks using data from three mammalian organisms. Our method calculates a probabilistic score between each gene and each Gene Ontology category that reflects coexpression enrichment of a GO module. For each GO category we use Receiver Operating Curves to assess whether these probabilistic scores reflect GBA. This methodology applied to five different coexpression networks demonstrates that the signature of guilt-by-association is ubiquitous and reproducible and that the GBA heuristic is broadly applicable across the population of nine hundred Gene Ontology categories. We also demonstrate the existence of highly reproducible patterns of coexpression between some pairs of GO categories.We conclude that GBA has universal value and that transcriptional control may be more modular than previously realized. Our analyses also suggest that methodologies combining coexpression measurements across multiple genes in a biologically-defined module can aid in characterizing gene function or in characterizing whether pairs of functions operate together.From the very start of the high-throughput microarray expression revolution it was understood [1,2] that guilt-by-association was a powerful heuristic to both explain why genes might have correlated expression in a set of experiments and infer what might be the function of a gene coexpressed with genes of better known function. As gene expression data have increased in numbers and quality, a variety of investigations have been leveraged from this GBA heuristic. Analyses of gene coexpression [3-7] have demonstrated that
Quantifying the relationship between co-expression, co-regulation and gene function
Dominic J Allocco, Isaac S Kohane, Atul J Butte
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-18
Abstract: Genes with strongly correlated mRNA expression profiles are more likely to have their promoter regions bound by a common transcription factor. This effect is present only at relatively high levels of expression similarity. In order for two genes to have a greater than 50% chance of sharing a common transcription factor binder, the correlation between their expression profiles (across the 611 microarrays used in our study) must be greater than 0.84. Genes with similar functional annotations are also more likely to be bound by a common transcription factor. Combining mRNA expression data with functional annotation results in a better predictive model than using either data source alone.We demonstrate how mRNA expression data and functional annotations can be used together to estimate the probability that genes share a common regulatory mechanism. Existing microarray data and known functional annotations are sufficient to identify only a relatively small percentage of co-regulated genes.It is axiomatic in functional genomics that genes with similar mRNA expression profiles are likely to be regulated via the same mechanisms [1,2]. This hypothesis is the basis for almost all attempts to use mRNA expression data from microarray experiments to discover regulatory networks. Several investigators have provided indirect evidence for this hypothesis by clustering genes according to their mRNA expression profiles and then showing that genes in a cluster often share common upstream sequence motifs [3-5]. Ideker et al detailed examples of genes known to be regulated by a common transcription factor where the expression profiles of the co-regulated genes were highly correlated [6]. Despite supporting evidence and some known examples, the link between co-expression and co-regulation has not been directly tested or quantified on a large scale. Doing so would solidify the theoretical basis for using mRNA expression data to identify regulatory networks. It would also be useful, in a p
Non-Synonymous and Synonymous Coding SNPs Show Similar Likelihood and Effect Size of Human Disease Association
Rong Chen,Eugene V. Davydov,Marina Sirota,Atul J. Butte
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0013574
Abstract: Many DNA variants have been identified on more than 300 diseases and traits using Genome-Wide Association Studies (GWASs). Some have been validated using deep sequencing, but many fewer have been validated functionally, primarily focused on non-synonymous coding SNPs (nsSNPs). It is an open question whether synonymous coding SNPs (sSNPs) and other non-coding SNPs can lead to as high odds ratios as nsSNPs. We conducted a broad survey across 21,429 disease-SNP associations curated from 2,113 publications studying human genetic association, and found that nsSNPs and sSNPs shared similar likelihood and effect size for disease association. The enrichment of disease-associated SNPs around the 80th base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3′untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies.
Extreme Evolutionary Disparities Seen in Positive Selection across Seven Complex Diseases
Erik Corona,Joel T. Dudley,Atul J. Butte
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0012236
Abstract: Positive selection is known to occur when the environment that an organism inhabits is suddenly altered, as is the case across recent human history. Genome-wide association studies (GWASs) have successfully illuminated disease-associated variation. However, whether human evolution is heading towards or away from disease susceptibility in general remains an open question. The genetic-basis of common complex disease may partially be caused by positive selection events, which simultaneously increased fitness and susceptibility to disease. We analyze seven diseases studied by the Wellcome Trust Case Control Consortium to compare evidence for selection at every locus associated with disease. We take a large set of the most strongly associated SNPs in each GWA study in order to capture more hidden associations at the cost of introducing false positives into our analysis. We then search for signs of positive selection in this inclusive set of SNPs. There are striking differences between the seven studied diseases. We find alleles increasing susceptibility to Type 1 Diabetes (T1D), Rheumatoid Arthritis (RA), and Crohn's Disease (CD) underwent recent positive selection. There is more selection in alleles increasing, rather than decreasing, susceptibility to T1D. In the 80 SNPs most associated with T1D (p-value <7.01×10?5) showing strong signs of positive selection, 58 alleles associated with disease susceptibility show signs of positive selection, while only 22 associated with disease protection show signs of positive selection. Alleles increasing susceptibility to RA are under selection as well. In contrast, selection in SNPs associated with CD favors protective alleles. These results inform the current understanding of disease etiology, shed light on potential benefits associated with the genetic-basis of disease, and aid in the efforts to identify causal genetic factors underlying complex disease.
Likelihood ratios for genome medicine
Alexander A Morgan, Rong Chen, Atul J Butte
Genome Medicine , 2010, DOI: 10.1186/gm151
Abstract: Although there has been continuing discussion and debate over the ethical implications and clinical utility of a large-scale genotyping for an individual patient [1-3], the issue is somewhat moot. Patients are now being genotyped using either (i) measurement platforms run by several different direct-to-consumer companies that sequence nearly a million single nucleotide polymorphisms (SNPs) [4], or (ii) whole genome sequencing, which is beginning to be offered to selected individuals [5-8]. Patients are beginning to present to their healthcare provider before or during an evaluation, including an extensive genotyping scan [9]. It may appear overwhelming and a nearly impossible task to take the complexity of genetic variation and interpret it in the context of the enormous amount of literature on human genetics [10], some of which seems mercurial and contradictory. However daunting, it is incumbent upon a healthcare provider to try to help patients make informed decisions in light of the information available, and to not ignore this genetic information.Although DNA variants unique to an individual, or at least extremely rare in the general population, may have major impact on personal phenotypes and may explain much of the 'missing heritability' [11,12] of common variants, we currently have very little power to interpret the impact or predictive power of these rare variants. Additionally, individual sequence data, which are able to probe for more rare variants, are not yet as common as parallel genotyping assays, which primarily probe common variants. There is a large body of published research associating common variants with disease [13]. Admittedly, those relationships are through association, which does not necessarily indicate a direct functional relationship for the outcome or phenotype being studied. However, having a direct model of mechanism has never been a requirement for the value of a medical test. Many features used in physical examinations or laboratory
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