Discovering joint associations between disease and gene pairs with a novel similarity test

Background

Diverse plant and animal species have B chromosomes, also known as accessory, extra or supernumerary chromosomes. Despite being widely distributed among different taxa, the genomic nature and genetic behavior of B chromosomes are still poorly understood.

Results

In this study we describe the occurrence of B chromosomes in the African cichlid fish Haplochromis obliquidens. One or two large B chromosome(s) occurring in 39.6% of the analyzed individuals (both male and female) were identified. To better characterize the karyotype and assess the nature of the B chromosomes, fluorescence in situ hybridization (FISH) was performed using probes for telomeric DNA repeats, 18S and 5S rRNA genes, SATA centromeric satellites, and bacterial artificial chromosomes (BACs) enriched in repeated DNA sequences. The B chromosomes are enriched in repeated DNAs, especially non-active 18S rRNA gene-like sequences.

Conclusion

Our results suggest that the B chromosome could have originated from rDNA bearing subtelo/acrocentric A chromosomes through formation of an isochromosome, or by accumulation of repeated DNAs and rRNA gene-like sequences in a small proto-B chromosome derived from the A complement.     

Virus-derived platforms for visualizing protein associations inside cells

Protein-protein associations are vital to cellular functions. Here we describe a helpful new method to demonstrate protein-protein associations inside cells based on the capacity of orthoreovirus protein muNS to form large cytoplasmic inclusions, easily visualized by light microscopy, and to recruit other proteins to these structures in a specific manner. We introduce this technology by the identification of a sixth orthoreovirus protein, RNA-dependent RNA polymerase lambda3, that was recruited to the structures through an association with muNS. We then established the broader utility of this technology by using a truncated, fluorescently tagged form of muNS as a fusion platform to present the mammalian tumor suppressor p53, which strongly recruited its known interactor simian virus 40 large T antigen to the muNS-derived structures. In both examples, we further localized a region of the recruited protein that is key to its recruitment. Using either endogenous p53 or a second fluorescently tagged fusion of p53 with the rotavirus NSP5 protein, we demonstrated p53 oligomerization as well as p53 association with another of its cellular interaction partners, the CREB-binding proteins, within the inclusions. Furthermore using the p53-fused fluorescent muNS platform in conjunction with three-color microscopy, we identified a ternary complex comprising p53, simian virus 40 large T antigen, and retinoblastoma protein. The new method is technically simple, uses commonly available resources, and is adaptable to high throughput formats.     

MedEvi: retrieving textual evidence of relations between biomedical concepts from Medline

Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results. AVAILABILITY: http://www.ebi.ac.uk/tc-test/textmining/medevi/     

KGraph: a system for visualizing and evaluating complex genetic associations

The KGraph is a data visualization system that has been developed to display the complex relationships between the univariate and bivariate associations among an outcome of interest, a set of covariates, and a set of genetic factors, such as single nucleotide polymorphisms (SNPs). It allows for easy viewing and interpretation of genetic associations, correlations among covariates and SNPs, and information about the replication and cross-validation of the associations. The KGraph allows the user to more easily investigate multicollinearity and confounding through visualization of the multidimensional correlation structure underlying genetic associations. It emphasizes gene-environment and gene-gene interaction, both important components of any genetic system that are often overlooked in association frameworks. AVAILABILITY: http://www.epidkardia.sph.umich.edu/software/kgrapher     

miRSel: Automated extraction of associations between microRNAs and genes from the biomedical literature

Background  

MicroRNAs have been discovered as important regulators of gene expression. To identify the target genes of microRNAs, several databases and prediction algorithms have been developed. Only few experimentally confirmed microRNA targets are available in databases. Many of the microRNA targets stored in databases were derived from large-scale experiments that are considered not very reliable. We propose to use text mining of publication abstracts for extracting microRNA-gene associations including microRNA-target relations to complement current repositories.     

Discovering disease associations by integrating electronic clinical data and medical literature

Electronic health record (EHR) systems offer an exceptional opportunity for studying many diseases and their associated medical conditions within a population. The increasing number of clinical record entries that have become available electronically provides access to rich, large sets of patients' longitudinal medical information. By integrating and comparing relations found in the EHRs with those already reported in the literature, we are able to verify existing and to identify rare or novel associations. Of particular interest is the identification of rare disease co-morbidities, where the small numbers of diagnosed patients make robust statistical analysis difficult. Here, we introduce ADAMS, an Application for Discovering Disease Associations using Multiple Sources, which contains various statistical and language processing operations. We apply ADAMS to the New York-Presbyterian Hospital's EHR to combine the information from the relational diagnosis tables and textual discharge summaries with those from PubMed and Wikipedia in order to investigate the co-morbidities of the rare diseases Kaposi sarcoma, toxoplasmosis, and Kawasaki disease. In addition to finding well-known characteristics of diseases, ADAMS can identify rare or previously unreported associations. In particular, we report a statistically significant association between Kawasaki disease and diagnosis of autistic disorder.     

PyDeT, a PyMOL plug-in for visualizing geometric concepts around proteins

The fast growing Protein Data Bank (PDB) contains a vast amount of 3-dimensional data on proteins, and nucleic-acid structures obtained by X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy. PyDeT is a PyMOL (molecular visualization software system) plug-in that visualize tessellations derived from the protein structure along with the source protein. PyDeT is released under a GNU General Public License (GPL) and is available from the authors.     

Discovering semantic features in the literature: a foundation for building functional associations

Background  

Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.     

FACTA: a text search engine for finding associated biomedical concepts

FACTA is a text search engine for MEDLINE abstracts, which is designed particularly to help users browse biomedical concepts (e.g. genes/proteins, diseases, enzymes and chemical compounds) appearing in the documents retrieved by the query. The concepts are presented to the user in a tabular format and ranked based on the co-occurrence statistics. Unlike existing systems that provide similar functionality, FACTA pre-indexes not only the words but also the concepts mentioned in the documents, which enables the user to issue a flexible query (e.g. free keywords or Boolean combinations of keywords/concepts) and receive the results immediately even when the number of the documents that match the query is very large. The user can also view snippets from MEDLINE to get textual evidence of associations between the query terms and the concepts. The concept IDs and their names/synonyms for building the indexes were collected from several biomedical databases and thesauri, such as UniProt, BioThesaurus, UMLS, KEGG and DrugBank. AVAILABILITY: The system is available at http://www.nactem.ac.uk/software/facta/     

Using contextual and lexical features to restructure and validate the classification of biomedical concepts

Background  

Biomedical ontologies are critical for integration of data from diverse sources and for use by knowledge-based biomedical applications, especially natural language processing as well as associated mining and reasoning systems. The effectiveness of these systems is heavily dependent on the quality of the ontological terms and their classifications. To assist in developing and maintaining the ontologies objectively, we propose automatic approaches to classify and/or validate their semantic categories. In previous work, we developed an approach using contextual syntactic features obtained from a large domain corpus to reclassify and validate concepts of the Unified Medical Language System (UMLS), a comprehensive resource of biomedical terminology. In this paper, we introduce another classification approach based on words of the concept strings and compare it to the contextual syntactic approach.     

Identification of key concepts in biomedical literature using a modified Markov heuristic

MOTIVATION: The recent explosion of interest in mining the biomedical literature for associations between defined entities such as genes, diseases and drugs has made apparent the need for robust methods of identifying occurrences of these entities in biomedical text. Such concept-based indexing is strongly dependent on the availability of a comprehensive ontology or lexicon of biomedical terms. However, such ontologies are very difficult and expensive to construct, and often require extensive manual curation to render them suitable for use by automatic indexing programs. Furthermore, the use of statistically salient noun phrases as surrogates for curated terminology is not without difficulties, due to the lack of high-quality part-of-speech taggers specific to medical nomenclature. RESULTS: We describe a method of improving the quality of automatically extracted noun phrases by employing prior knowledge during the HMM training procedure for the tagger. This enhancement, when combined with appropriate training data, can greatly improve the quality and relevance of the extracted phrases, thereby enabling greater accuracy in downstream literature mining tasks.     

Cross-modal associations between odors and colors

In the present study, we investigated the nature of any cross-modal associations between colors and odors. In Experiment 1, we show that participants consistently match certain odors to specific colors when asked to explicitly select from among different colors the one that best matched a given odor. In Experiment 2, we investigated the robustness of these cross-modal associations using a cross-modal variant of the implicit association test (IAT). Participants made speeded discrimination responses to a random sequence of odors (strawberry vs. spearmint) and color patches (pink vs. turquoise). On the basis of the results of Experiment 1, the assignment of these targets onto the two response keys was manipulated in order to generate compatible (e.g., responding to the pink color and to the strawberry odor with the same response key) and incompatible (e.g., responding to the pink color and to the spearmint odor with the same response key) blocks of trials. The results showed that participants responded more rapidly and accurately to odor-color pairings having a stronger association than to those having a weaker (or no) association. These results suggest that odor-color associations can be both systematic and robust. The paradigm developed here provides a novel cross-modal extension of the IAT to probe the nature of color-odor associations.     

Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations

Biomedical literature provides a rich but unstructured source of associations between chromosomal regions and biomedical concepts. By mining MEDLINE abstracts, we annotate the human genome at the level of cytogenetic bands. Our method creates a set of chromosomal aberration maps that associate cytogenetic bands to biomedical concepts from a variety of controlled vocabularies, including disease, dysmorphology, anatomy, development and Gene Ontology branches. The association between a band (e.g. 4p16.3) and a concept (e.g. microcephaly) is assessed by the statistical overrepresentation of this concept in the abstracts relating to this band. Our method is validated using existing genome annotation resources and known chromosomal aberration maps and is further illustrated through a case study on heart disease. Our chromosomal aberration maps provide diagnostics support to clinical geneticists, aid cytogeneticists to interpret and report cytogenetic findings and support researchers interested in human gene function. The method is available as a web application, aBandApart, at http://www.esat.kuleuven.be/abandapart/.     

Lay concepts in informed consent to biomedical research: the capacity to understand and appreciate risk

Persons generally must give their informed consent to participate in research. To provide informed consent persons must be given information regarding the study in simple, lay language. Consent must be voluntary, and persons giving consent must be legally competent to consent and possess the capacity to understand and appreciate the information. This paper examines the relationship between the obligation to disclose information regarding risks and the requirement that persons have the capacity to understand and appreciate the information. There has been insufficient attention to the extent to which persons must be able to understand and appreciate study information in order to have their consent deemed valid when the information is provided in simple, lay language. This paper argues that (1) the capacity to understand and appreciate information that should be deemed necessary to give valid consent should be defined by the capacity of the typical, cognitively normal adult and (2) the capacity of the typical, cognitively normal adult to understand and appreciate the concept of risk is limited. Therefore, (3) all things being equal, potential subjects must possess a limited capacity to understand and appreciate risk to be deemed competent to consent to research participation. (4) In some cases investigators ought to require that persons possess a greater than typical capacity to understand and appreciate risk.     

Reliability of statistical associations between genes and disease

Many statistical associations between a disease and alleles of specific genes have proven to be irreproducible. In part, this irreproducibility can be attributed to a lack of replication before publication and the fact that, until recently, the relationship between statistical significance and various measures of reproducibility was not widely understood. This review proposes a classification system, the Better Associations for Disease and GEnes (BADGE) system, for describing genetic associations. The BADGE classes, first class through fifth class, are based on the P value of the association. A first-class association, with P<2×10⁻⁷, is expected to be reproducible even in the absence of other evidence supporting the association. A fifth-class association corresponds to conventional statistical significance (P<5×10⁻²), which provides almost no assurance of reproducibility. Three intervening classes, described as second-, third-, and fourth-class associations, are defined by P values separated by factors of 20 or 25 from these extremes.     

Epidemiological associations between arsenic and cancer in Argentina

The population of a large central area of Argentina is affected by a syndrome designed as “regional and endemic chronic hydroarsenicism.” A number of types of neoplasms, especially of skin, urinary bladder, and of digestive system, occur with higher frequency in these areas. Drinking water in some of the affected areas contains from 0.1 to 1.2 mg/L of As.