Application Of Bioinformatics Methods Research Articles

Application of bioinformatics methods in pesticide research and development

The use of pesticides is accompanied by a number of environmental and agrotechnological problems. Many pesticides do not degrade and stay in the soil for years and have low selectivity. Massive application of pesticides with non-selective nature of action caused a gradual increase of resistance in pests due to persistent inherited changes in their DNA. This affects the efficiency of growing agricultural plants and pollution of the environment and food. Computational biology methods, which are actively developing all over the world, can help to solve this problem. Despite the fact that in Russia bioinformatics methods are used to study plant genes of animals, metagenomes of microorganisms, there are no own databases and specialized computer applications for such research and pesticide modernization. Development of domestic similar bioinformatics tools is also an urgent task. The article highlights the problem of creating new effective and environmentally friendly pesticides. The methods of bioinformatics that can be used in the research and development of pesticides are given. The stages of creating new pesticides by bioinformatics methods (review of databases, modeling of molecules, modeling of the interaction of a pesticide with a target, prediction of biological activity) are considered. A description is given of the methods for optimizing the molecular framework of pesticides, which is a change in the carbon skeleton in order to search for new active compounds and screen out many similar compounds in the chemical space. Foreign web resources used to assess the presence of pesticidal properties in substances, such as toxicity, metabolism and physico-chemical properties, and their subsequent registration as pesticides are given.

Isolation and biological characterization of human immunodeficiency virus type 1 from Cubans asymptomatic patients

Isolation and biological characterization of human immunodeficiency virus type 1 from Cubans asymptomatic patients

Translational Bioinformatics Approaches to Drug Development

A majority of therapeutic interventions occur late in the pathological process, when treatment outcome can be less predictable and effective, highlighting the need for new precise and preventive therapeutic development strategies that consider genomic and environmental context. Translational bioinformatics is well positioned to contribute to the many challenges inherent in bridging this gap between our current reactive methods of healthcare delivery and the intent of precision medicine, particularly in the areas of drug development, which forms the focus of this review. A variety of powerful informatics methods for organizing and leveraging the vast wealth of available molecular measurements available for a broad range of disease contexts have recently emerged. These include methods for data driven disease classification, drug repositioning, identification of disease biomarkers, and the creation of disease network models, each with significant impacts on drug development approaches. An important bottleneck in the application of bioinformatics methods in translational research is the lack of investigators who are versed in both biomedical domains and informatics. Efforts to nurture both sets of competencies within individuals and to increase interfield visibility will help to accelerate the adoption and increased application of bioinformatics in translational research. It is possible to construct predictive, multiscale network models of disease by integrating genotype, gene expression, clinical traits, and other multiscale measures using causal network inference methods. This can enable the identification of the "key drivers" of pathology, which may represent novel therapeutic targets or biomarker candidates that play a more direct role in the etiology of disease.

The Use of Bioinformatics for Studying HIV Evolutionary and Epidemiological History in South America

The South American human immunodeficiency virus type 1 (HIV-1) epidemic is driven by several subtypes (B, C, and F1) and circulating and unique recombinant forms derived from those subtypes. Those variants are heterogeneously distributed around the continent in a country-specific manner. Despite some inconsistencies mainly derived from sampling biases and analytical constrains, most of studies carried out in the area agreed in pointing out specificities in the evolutionary dynamics of the circulating HIV-1 lineages. In this paper, we covered the theoretical basis, and the application of bioinformatics methods to reconstruct the HIV spatial-temporal dynamics, unveiling relevant information to understand the origin, geographical dissemination and the current molecular scenario of the HIV epidemic in the continent, particularly in the countries of Southern Cone.

Identification and Functional Analysis of Regulatory Polymorphisms

Current models for the genetics of common diseases suggest that diseases are the result of the sum of variants which on their own produce a small effect on the disease phenotype. Because of this small effect, common disease variants are very difficult to find, even with the application of the powerful new methods for the study of gene variants–disease association. An alternative approach based on the functional changes due to the variants is described here. The focus is set on regulatory variants because they fit best the current models. Regulatory variants filtered by the application of bioinformatic methods are analysed in order to select those best suited for subsequent association studies. No single method can characterize the functional consequences of a variant. We compare current available and future developments of the major methods and discuss their possible application in the study of the genetics of common disease.

Genome-wide association studies getting more complicated but help is on the way

Genome-wide association studies getting more complicated but help is on the way

Pokemon expression in malignant glioma: an application of bioinformatics methods

In this report the authors review the role of bioinformatics in the design of a research project in which the molecular genetics of malignant glioma were studied. A project to characterize Pokemon expression in malignant glioma was developed, refined, and implemented using bioinformatics methods. Using the resources available from the National Center for Biotechnology Information, the messenger RNA (mRNA) sequence for Pokemon was determined. With this information and online primer design tools, novel primers were designed that would specifically amplify Pokemon mRNA by using reverse transcription-polymerase chain reaction assays. The promise of bioinformatics is in the rapid and widespread dissemination and analysis of genomic information. This information is then used in research investigating the genetic basis of disease. In this paper the authors review the bioinformatics methods used in their study of Pokemon expression in malignant glioma.

Target gene identification from expression array data by promoter analysis

DNA microchips and expression arrays yield enormous amounts of data linking cDNA sequences to gene expression patterns. This now allows the characterization of gene expression in normal and diseased tissues as well as the response of tissues to the application of therapeutic reagents. Software currently exists to analyze DNA array/chip data with respect to corresponding mRNA sequences, which facilitates the precise determination of when and where certain groups of genes are expressed. The information concerning transcriptional regulatory networks responsible for the observed expression patterns is not contained within the cDNA sequences used to generate the arrays, but resides often within the promoter sequences of the individual genes (and/or enhancers). The complete sequence of the human genome will provide the molecular basis for the identification of such regulatory regions. Promoter sequences for specific cDNAs can be obtained reliably from genomic sequences simply by exon mapping. Promoter prediction tools can also be used to locate promoters directly in the genomic sequence in many cases in which cDNAs are 5′-incomplete. Once sufficient numbers of promoter sequences have been obtained, the comparative promoter analysis of the co-regulated genes and groups of genes can be applied in order to generate models describing the higher order levels of the transcription factor binding site organization within these promoter regions. As evident from several examples, this approach can identify promoter modules responsible for the common regulation of promoters solely by the application of bioinformatics methods. Such modules represent the molecular mechanisms through which regulatory networks influence gene expression. Another advantage of this approach is that it also provides a powerful alternative for elucidating functional features of genes with no detectable sequence similarity, by linking them to other genes on the basis of their common promoter structures.