Erich J. Baker

On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types

BackgroundIntegrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. Bipartite graphs can be useful for representing relationships across pairs of disparate data types, with the interpretation of these relationships accomplished through an enumeration of maximal bicliques. Most previously-known techniques are generally ill-suited to this foundational task, because they are relatively inefficient and without effective scaling. In this paper, a powerful new algorithm is described that produces all maximal bicliques in a bipartite graph. Unlike most previous approaches, the new method neither places undue restrictions on its input nor inflates the problem size. Efficiency is achieved through an innovative exploitation of bipartite graph structure, and through computational reductions that rapidly eliminate non-maximal candidates from the search space. An iterative selection of vertices for consideration based on non-decreasing common neighborhood sizes boosts efficiency and leads to more balanced recursion trees.ResultsThe new technique is implemented and compared to previously published approaches from graph theory and data mining. Formal time and space bounds are derived. Experiments are performed on both random graphs and graphs constructed from functional genomics data. It is shown that the new method substantially outperforms the best previous alternatives.ConclusionsThe new method is streamlined, efficient, and particularly well-suited to the study of huge and diverse biological data. A robust implementation has been incorporated into GeneWeaver, an online tool for integrating and analyzing functional genomics experiments, available at http://geneweaver.org. The enormous increase in scalability it provides empowers users to study complex and previously unassailable gene-set associations between genes and their biological functions in a hierarchical fashion and on a genome-wide scale. This practical computational resource is adaptable to almost any applications environment in which bipartite graphs can be used to model relationships between pairs of heterogeneous entities.

Ontological Discovery Environment: A system for integrating gene-phenotype associations

The wealth of genomic technologies has enabled biologists to rapidly ascribe phenotypic characters to biological substrates. Central to effective biological investigation is the operational definition of the process under investigation. We propose an elucidation of categories of biological characters, including disease relevant traits, based on natural endogenous processes and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and current semantics such as disease names and processes. The Ontological Discovery Environment (ODE) is an Internet accessible resource for the storage, sharing, retrieval and analysis of phenotype-centered genomic data sets across species and experimental model systems. Any type of data set representing gene-phenotype relationships, such quantitative trait loci (QTL) positional candidates, literature reviews, microarray experiments, ontological or even meta-data, may serve as inputs. To demonstrate a use case leveraging the homology capabilities of ODE and its ability to synthesize diverse data sets, we conducted an analysis of genomic studies related to alcoholism. The core of ODEs gene set similarity, distance and hierarchical analysis is the creation of a bipartite network of gene-phenotype relations, a unique discrete graph approach to analysis that enables set-set matching of non-referential data. Gene sets are annotated with several levels of metadata, including community ontologies, while gene set translations compare models across species. Computationally derived gene sets are integrated into hierarchical trees based on gene-derived phenotype interdependencies. Automated set identifications are augmented by statistical tools which enable users to interpret the confidence of modeled results. This approach allows data integration and hypothesis discovery across multiple experimental contexts, regardless of the face similarity and semantic annotation of the experimental systems or species domain.

GeneKeyDB: A lightweight, gene-centric, relational database to support data mining environments

BackgroundThe analysis of biological data is greatly enhanced by existing or emerging databases. Most existing databases, with few exceptions are not designed to easily support large scale computational analysis, but rather offer exclusively a web interface to the resource. We have recognized the growing need for a database which can be used successfully as a backend to computational analysis tools and pipelines. Such database should be sufficiently versatile to allow easy system integration.ResultsGeneKeyDB is a gene-centered relational database developed to enhance data mining in biological data sets. The system provides an underlying data layer for computational analysis tools and visualization tools. GeneKeyDB relies primarily on existing database identifiers derived from community databases (NCBI, GO, Ensembl, et al.) as well as the known relationships among those identifiers. It is a lightweight, portable, and extensible platform for integration with computational tools and analysis environments.ConclusionGeneKeyDB can enable analysis tools and users to manipulate the intersections, unions, and differences among different data sets.

MuTrack: a genome analysis system for large-scale mutagenesis in the mouse

BackgroundModern biological research makes possible the comprehensive study and development of heritable mutations in the mouse model at high-throughput. Using techniques spanning genetics, molecular biology, histology, and behavioral science, researchers may examine, with varying degrees of granularity, numerous phenotypic aspects of mutant mouse strains directly pertinent to human disease states. Success of these and other genome-wide endeavors relies on a well-structured bioinformatics core that brings together investigators from widely dispersed institutions and enables them to seamlessly integrate data, observations and discussions.DescriptionMuTrack was developed as the bioinformatics core for a large mouse phenotype screening effort. It is a comprehensive collection of on-line computational tools and tracks thousands of mutagenized mice from birth through senescence and death. It identifies the physical location of mice during an intensive phenotype screening process at several locations throughout the state of Tennessee and collects raw and processed experimental data from each domain. MuTracks statistical package allows researchers to access a real-time analysis of mouse pedigrees for aberrant behavior, and subsequent recirculation and retesting. The end result is the classification of potential and actual heritable mutant mouse strains that become immediately available to outside researchers who have expressed interest in the mutant phenotype.ConclusionMuTrack demonstrates the effectiveness of using bioinformatics techniques in data collection, integration and analysis to identify unique result sets that are beyond the capacity of a solitary laboratory. By employing the research expertise of investigators at several institutions for a broad-ranging study, the TMGC has amplified the effectiveness of any one consortium member. The bioinformatics strategy presented here lends future collaborative efforts a template for a comprehensive approach to large-scale analysis.

Monkey Alcohol Tissue Research Resource: Banking Tissues for Alcohol Research

BACKGROUND An estimated 18 million adults in the United States meet the clinical criteria for diagnosis of alcohol abuse or alcoholism, a disorder ranked as the third leading cause of preventable death. In addition to brain pathology, heavy alcohol consumption is comorbid with damage to major organs including heart, lungs, liver, pancreas, and kidneys. Much of what is known about risk for and consequences of heavy consumption derive from rodent or retrospective human studies. The neurobiological effects of chronic intake in rodent studies may not easily translate to humans due to key differences in brain structure and organization between species, including a lack of higher-order cognitive functions, and differences in underlying prefrontal cortical neural structures that characterize the primate brain. Further, rodents do not voluntarily consume large quantities of ethanol (EtOH) and they metabolize it more rapidly than primates. METHODS The basis of the Monkey Alcohol Tissue Research Resource (MATRR) is that nonhuman primates, specifically monkeys, show a range of drinking excessive amounts of alcohol (>3.0 g/kg or a 12 drink equivalent per day) over long periods of time (12 to 30 months) with concomitant pathological changes in endocrine, hepatic, and central nervous system (CNS) processes. The patterns and range of alcohol intake that monkeys voluntarily consume parallel what is observed in humans with alcohol use disorders and the longitudinal experimental design spans stages of drinking from the EtOH-naïve state to early exposure through chronic abuse. Age- and sex-matched control animals self-administer an isocaloric solution under identical operant procedures. RESULTS The MATRR is a unique postmortem tissue bank that provides CNS and peripheral tissues, and associated bioinformatics from monkeys that self-administer EtOH using a standardized experimental paradigm to the broader alcohol research community. CONCLUSIONS This resource provides a translational platform from which we can better understand the disease processes associated with alcoholism.

Identifying future drinkers: behavioral analysis of monkeys initiating drinking to intoxication is predictive of future drinking classification

Background The Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well‐documented nonhuman primate (NHP) alcohol self‐administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD) individuals. Here, we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment. Methods The classification strategy uses a machine‐learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across 7 cohorts of 5 to 8 monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self‐administration. Results Predictive outcome accuracy is approximately 78% when classes are aggregated into 2 groups, “LD and BD” and “HD and VHD.” A subsequent 2‐step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average 4‐category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings. Conclusions We demonstrate that data derived from the induction phase of this ethanol self‐administration protocol have significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through 3 stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having increasingly fewer bouts with more drinks. This analysis also identifies characteristic avoidance of intoxicating topographies in future low drinkers, such as increasing number of bouts and waiting longer before the first ethanol drink.

Cross-Species Integrative Functional Genomics in GeneWeaver Reveals a Role for Pafah1b1 in Altered Response to Alcohol

Identifying the biological substrates of complex neurobehavioral traits such as alcohol dependency pose a tremendous challenge given the diverse model systems and phenotypic assessments used. To address this problem we have developed a platform for integrated analysis of high-throughput or genome-wide functional genomics studies. A wealth of such data exists, but it is often found in disparate, non-computable forms. Our interactive web-based software system, Gene Weaver (http://www.geneweaver.org), couples curated results from genomic studies to graph-theoretical tools for combinatorial analysis. Using this system we identified a gene underlying multiple alcohol-related phenotypes in four species. A search of over 60,000 gene sets in GeneWeavers database revealed alcohol-related experimental results including genes identified in mouse genetic mapping studies, alcohol selected Drosophila lines, Rattus differential expression, and human alcoholic brains. We identified highly connected genes and compared these to genes currently annotated to alcohol-related behaviors and processes. The most highly connected gene not annotated to alcohol was Pafah1b1. Experimental validation using a Pafah1b1 conditional knock-out mouse confirmed that this gene is associated with an increased preference for alcohol and an altered thermoregulatory response to alcohol. Although this gene has not been previously implicated in alcohol-related behaviors, its function in various neural mechanisms makes a role in alcohol-related phenomena plausible. By making diverse cross-species functional genomics data readily computable, we were able to identify and confirm a novel alcohol-related gene that may have implications for alcohol use disorders and other effects of alcohol.

Reuniting Families : An Online Database to Aid in the Identification of Undocumented Immigrant Remains

Abstract:  The Reuniting Families project attempts to aid federal, state and local agencies currently working towards the identification of deceased undocumented immigrants. This initiative has created a distributed on‐line database, accessible by public officials and private citizens interested in searching for missing individuals based on both phenotypic and genotypic characteristics. This broad effort includes the exhumation of individuals from geographically disparate pauper graves, the classification of their physical characteristics, and the cataloging of observed metric traits in a local relational database, to include associated articles of possession and related metadata. Concurrent with the documentation of physical forensic evidence is the analysis of mitochondrial DNA sequences. Computational techniques and scoring parameters are applied to automate the process of discovery and identification as well at to preserve information on the missing. The result is a prototype knowledgebase that may serve as a model for future efforts in international forensic science collaborations.

Increased levels of the acetaldehyde-derived DNA adduct N2 -ethyldeoxyguanosine in oral mucosa DNA from Rhesus monkeys exposed to alcohol

Alcohol is a human carcinogen. A causal link has been established between alcohol drinking and cancers of the upper aerodigestive tract, colon, liver and breast. Despite this established association, the underlying mechanisms of alcohol-induced carcinogenesis remain unclear. Various mechanisms may come into play depending on the type of cancer; however, convincing evidence supports the concept that ethanols major metabolite acetaldehyde may play a major role. Acetaldehyde can react with DNA forming adducts which can serve as biomarkers of carcinogen exposure and potentially of cancer risk. The major DNA adduct formed from this reaction is N (2)-ethylidenedeoxyguanosine, which can be quantified as its reduced form N (2)-ethyl-dG by LC-ESI-MS/MS. To investigate the potential use of N (2)-ethyl-dG as a biomarker of alcohol-induced DNA damage, we quantified this adduct in DNA from the oral, oesophageal and mammary gland tissues from rhesus monkeys exposed to alcohol drinking over their lifetimes and compared it to controls. N (2)-Ethyl-dG levels were significantly higher in the oral mucosa DNA of the exposed animals. Levels of the DNA adduct measured in the oesophageal mucosa of exposed animals were not significantly different from controls. A correlation between the levels measured in the oral and oesophageal DNA, however, was observed, suggesting a common source of formation of the DNA adducts. N (2) -Ethyl-dG was measured in mammary gland DNA from a small cohort of female animals, but no difference was observed between exposed animals and controls. These results support the hypothesis that acetaldehyde induces DNA damage in the oral mucosa of alcohol-exposed animals and that it may play role in the alcohol-induced carcinogenic process. The decrease of N (2)-ethyl-dG levels in exposed tissues further removed from the mouth also suggests a role of alcohol metabolism in the oral cavity, which may be considered separately from ethanol liver metabolism in the investigation of ethanol-related cancer risk.

PMA-treated K-562 leukemia cells mediate a TH2-specific expansion of CD4+ T cells in vitro

Highly enriched preparations of human CD3+CD4+ T-lymphocytes were stimulated with mitogen or OKT3 to determine the capacity of K-562 cells to function as accessory cells. Phorbol 12-myristate 13-acetate (PMA)-treated K-562 cells were induced to differentiate along the megakaryocytic lineage and could supplant monocyte-accessory cell function. Intracytoplasmic analysis of interleukin-4 (IL-4) and interferon-gamma (IFN-gamma) established that IL-4, and not IFN-gamma, was preferentially produced by the activated lymphocytes. This polarized stimulation is compatible with a type 2 or humoral immune response of purified T cells co-cultured with differentiated K-562 cells in vitro, and may have implications in immunoregulation due to disease progression.