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Results for “Bioinformatics analysis”

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At least 1,225 records · Page 68Linked to original sources

PubMed2005

[Screening and cloning of the genes of protein interacting with the N-terminal protein of hepatitis B virus DNA polymerase by yeast-two hybrid technique].

OBJECTIVE: To screen and clone the genes of protein interacting with the N-terminal protein (TP) of hepatitis B virus DNA polymerase. METHODS: TP was amplified by polymerase chain reaction (PCR) and TP bait plasmid was constructed by using yeast two-hybrid system 3, then transformed into yeast AH 109. The transformed yeast was mated with yeast Y187 containing liver cDNA library plasmid in 2 x YPDA medium. Diploid yeast was plated on synthetic dropout medium (SD/-Trp-Leu-His-Ade) and that containing X-alpha-GAL for selecting two times and screening. Plasmids were extracted from blue colonies, and sequence analysis was performed by bioinformatics. RESULTS: Forty-seven clones were obtained, these clones included human P36956 sterol regulatory element binding protein-1, RNA polymerase II subunit hsRPB7 mRNA, asialoglycoprotein receptor 2, transcript variant 3, ceruloplasmin (ferroxidase), transmembrane 4 superfamily member 2 and 19 of the hypothetical proteins and so on. CONCLUSION: Genes encoding TP interacting proteins in hepatocytes were successfully cloned and the results suggest that TP has a wide variety of biological functions.

Cloning, Molecular↗

PubMed2006

Challenges in the stratification of breast tumors for tailored therapies.

Studying the molecular stratification of breast carcinoma is a real challenge considering the extreme heterogeneity of these tumors. Many patients are now treated following recommendation established at several NIH and St Gallen consensus conferences. However a significant fraction of these breast cancer patients do not need adjuvant chemotherapies while other patients receive inefficacious therapies. High density gene expression arrays have been designed to attempt to establish expression profiles that could be used as prognostic indicators or as predictive markers for response to treatment. This review is intended to discuss the potential value of these new indicators, but also the current weaknesses of these new genomic and bioinformatic approaches. The combined analysis of transcriptomic and genomic alteration data from relatively large numbers of well annotated tumor specimens may offer an opportunity to overcome the current difficulties in validating recently published non overlapping gene lists as prognostic or therapeutic indicators. There is also hope for identifying and deciphering signal transduction pathways driving tumor progression with newly developed algorithms and semi quantitative parameters obtained in simplified in vitro or in vivo models for specific transduction pathways.

Animals↗

PubMedDate not supplied

Information processing for the analysis of metabolic pathways and inborn errors.

Methods of biotechnology allow the analysis of molecular structures and molecular processes. Different molecular database systems are available which allow worldwide data access. Methods and concepts of bioinformatics are important for the analysis of these molecular data, which represent complex regulatory networks. The detection of metabolic diseases is one application of information systems, which represents molecular and medical data and allows the stimulation of metabolic processes. In this paper we present the architecture of our molecular information system.

Database Management Systems↗

PubMed2005

Use and misuse of p-values in designed and observational studies: guide for researchers and reviewers.

Analysis of scientific data involves many components, one of which is often statistical testing with the calculation of p-values. However, researchers too often pepper their papers with p-values in the absence of critical thinking about their results. In fact, statistical tests in their various forms address just one question: does an observed difference exceed that which might reasonably be expected solely as a result of sampling error and/or random allocation of experimental material? Such tests are best applied to the results of designed studies with reasonable control of experimental error and sampling error, as well as acquisition of a sufficient sample size. Nevertheless, attributing an observed difference to a specific treatment effect requires critical thinking on the part of the scientist. Observational studies involve data sets whose size is usually a matter of convenience with results that reflect a number of potentially confounding factors. In this situation, statistical testing is not appropriate and p-values may be misleading; other more modern statistical tools should be used instead, including graphic analysis, computer-intensive methods, regression trees, and other procedures broadly classified as bioinformatics, data mining, and exploratory data analysis. In this review, the utility of p-values calculated from designed experiments and observational studies are discussed, leading to the formation of a decision tree to aid researchers and reviewers in understanding both the benefits and limitations of statistical testing.

Clinical Trials as Topic↗

PubMed2002

Analysis of complex methylation data.

The development of multiple DNA methylation analysis techniques, including higher-throughput assays, has resulted in data structures of increasing complexity and diversity. Here, we discuss the general principles of DNA methylation analysis and propose a nomenclature for the various types of methylation analysis. We briefly outline several DNA methylation analysis techniques and discuss how these different technologies affect the structure of the resulting methylation data. We then describe the basic statistics and bioinformatic principles relevant to the analysis of simple and complex methylation data.

5-Methylcytosine↗

PubMed2006

Proteomic and computational methods in systems modeling of cellular signaling.

Cellular signaling lies at the core of cellular behavior, and is central for the understanding of many pathologic conditions. To comprehend how signal transduction is orchestrated at the molecular level remains the ultimate challenge for cell biology. In the last years there has been a revolution in the development of high-throughput methodologies in proteomics and genomics, which have provided extensive knowledge about expression profiles and molecular interaction-networks. However, these methods have typically provided qualitative and static information. This is about to turn, and several high-throughput methods are now available that provide quantitative and temporal information. These data are well suited for analysis by computational methods and bioinformatics, which are becoming increasingly valuable tools to grasp the complexity of cellular networks. At present, several cellular pathways have been modeled in silico and the analysis provides new understanding of the underlying properties that contribute to their dynamic features. Here, we review methodologies that are used for in silico modeling as well as methods to obtain large-scale quantitative data, and discuss how they can be integrated to generate powerful and predictive models of cellular processes. We argue that the generation of such models provide powerful tools to understand how systems properties emerges in healthy and pathologic states, and to generate efficient strategies for pharmacological intervention.

Computational Biology↗

PubMed2006

EPD in its twentieth year: towards complete promoter coverage of selected model organisms.

The Eukaryotic Promoter Database (EPD) is an annotated non-redundant collection of eukaryotic POL II promoters, experimentally defined by a transcription start site (TSS). Access to promoter sequences is provided by pointers to positions in the corresponding genomes. Promoter evidence comes from conventional TSS mapping experiments for individual genes, or, starting from release 73, from mass genome annotation projects. Subsets of promoter sequences with customized 5' and 3' extensions can be downloaded from the EPD website. The focus of current development efforts is to reach complete promoter coverage for important model organisms as soon as possible. To speed up this process, a new class of preliminary promoter entries has been introduced as of release 83, which requires less stringent admission criteria. As part of a continuous integration process, new web-based interfaces have been developed, which allow joint analysis of promoter sequences with other bioinformatics resources developed by our group, in particular programs offered by the Signal Search Analysis Server, and gene expression data stored in the CleanEx database. EPD can be accessed at http://www.epd.isb-sib.ch.

Animals↗

PubMed2000

Graphically-enabled integration of bioinformatics tools allowing parallel execution.

Rapid analysis of large amounts of genomic data is of great biological as well as medical interest. This type of analysis will greatly benefit from the ability to rapidly assemble a set of related analysis programs and to exploit the power of parallel computing. TurboGenomics, which is a software package currently in its alpha-testing phase, allows integration of heterogeneous software components to be done graphically. In addition, the tool is capable of making the integrated components run in parallel. To demonstrate these abilities, we use the tool to develop a Web-based application that allows integrated access to a set of large-scale sequence data analysis programs used by a transposon-insertion based yeast genome project. We also contrast the differences in building such an application with and without using the TurboGenomics software.

Computational Biology↗

PubMed2004

Sequential extracellular matrix-focused and baited-global cluster analysis of serial transcriptomic profiles identifies candidate modulators of renal tubulointerstitial fibrosis in murine adriamycin-induced nephropathy.

Transcriptome analysis using microarray technology represents a powerful unbiased approach for delineating pathogenic mechanisms in disease. Here molecular mechanisms of renal tubulointerstitial fibrosis (TIF) were probed by monitoring changes in the renal transcriptome in a glomerular disease-dependent model of TIF (adriamycin nephropathy) using Affymetrix (mu74av2) microarray coupled with sequential primary biological function-focused and secondary "baited"-global cluster analysis of gene expression profiles. Primary cluster analysis focused on mRNAs encoding matrix proteins and modulators of matrix turnover as classified by Onto-Compare and Gene Ontology and identified both molecules and pathways already implicated in the pathogenesis of TIF (e.g. transforming growth factor beta1-CTGF-fibronectin-1 pathway) and novel TIF-associated genes (e.g. SPARC and Matrilin-2). Specific gene expression patterns identified by primary extracellular matrix-focused cluster analysis were then used as bioinformatic bait in secondary global clustering, with which to search the renal transcriptome for novel modulators of TIF. Among the genes clustering with ECM proteins in the latter analysis were endoglin, clusterin, and gelsolin. In several notable cases (e.g. claudin-1 and meprin-1beta) the pattern of gene expression identified in adriamycin nephropathy in vivo was replicated during transdifferentiation of renal tubule epithelial cells to a fibroblast-like phenotype in vitro on exposure to transforming growth factor-beta and epidermal growth factor suggesting a role in fibrogenesis. The further exploration of these complex gene networks should shed light on the core molecular pathways that underpin TIF in renal disease.

Animals↗

PubMed2003

Using multiple sequence correlation analysis to characterize functionally important protein regions.

Protein co-evolution under structural and functional constraints necessitates the preservation of important interactions. Identifying functionally important regions poses many obstacles in protein engineering efforts. In this paper, we present a bioinformatics-inspired approach (residue correlation analysis, RCA) for predicting functionally important domains from protein family sequence data. RCA is comprised of two major steps: (i) identifying pairs of residue positions that mutate in a coordinated manner, and (ii) using these results to identify protein regions that interact with an uncommonly high number of other residues. We hypothesize that strongly correlated pairs result not only from contacting pairs, but also from residues that participate in conformational changes involved during catalysis or important interactions necessary for retaining functionality. The results show that highly mobile loops that assist in ligand association/dissociation tend to exhibit high correlation. RCA results exhibit good agreement with the findings of experimental and molecular dynamics studies for the three protein families that are analyzed: (i) DHFR (dihydrofolate reductase), (ii) cyclophilin, and (iii) formyl-transferase. Specifically, the specificity (percentage of correct predictions) in all three cases is substantially higher than those obtained by entropic measures or contacting residue pairs. In addition, we use our approach in a predictive fashion to identify important regions of a transmembrane amino acid transporter protein for which there is limited structural and functional information available.

Amino Acid Sequence↗

PubMed2005

[Alignment-free biomolecular sequence comparison method].

Biosequence analysis is the primary research field of bioinformatics. In this field, useful information can be extracted by comparison analysis methods. Among them, sequence alignment is the most common comparison method. However the sequence comparison by alignment, which assumes conservation of contiguity between homologous segments, is at odds with genetic recombination. Especially for the multisequence alignment, there exists the difficulty in the complexity of calculation. Therefore, alignment-free sequence comparison methods are required. In this paper, two main categories of alignment-free sequence comparison methods are reviewed. The first one is based on the word (oligomer) frequency and its distribution. The sequences are compared using the distances defined in a Cartesian space by the frequency vectors. In the second category, sequences are compared using Kolmogorov complexity and chaos theory.

Algorithms↗

PubMed2004

Copasetic analysis: a framework for the blind analysis of microarray imagery.

From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePix when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis.

Artificial Intelligence↗

PubMed2006

Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics.

BACKGROUND: A large number of bioinformatics applications in the fields of bio-sequence analysis, molecular evolution and population genetics typically share input/output methods, data storage requirements and data analysis algorithms. Such common features may be conveniently bundled into re-usable libraries, which enable the rapid development of new methods and robust applications. RESULTS: We present Bio++, a set of Object Oriented libraries written in C++. Available components include classes for data storage and handling (nucleotide/amino-acid/codon sequences, trees, distance matrices, population genetics datasets), various input/output formats, basic sequence manipulation (concatenation, transcription, translation, etc.), phylogenetic analysis (maximum parsimony, markov models, distance methods, likelihood computation and maximization), population genetics/genomics (diversity statistics, neutrality tests, various multi-locus analyses) and various algorithms for numerical calculus. CONCLUSION: Implementation of methods aims at being both efficient and user-friendly. A special concern was given to the library design to enable easy extension and new methods development. We defined a general hierarchy of classes that allow the developer to implement its own algorithms while remaining compatible with the rest of the libraries. Bio++ source code is distributed free of charge under the CeCILL general public licence from its website http://kimura.univ-montp2.fr/BioPP.

Algorithms↗

PubMed2023

Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations.

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

Computational Biology↗

PubMed2004

Convergent analysis of cDNA and short oligomer microarrays, mouse null mutants and bioinformatics resources to study complex traits.

Gene expression data sets have recently been exploited to study genetic factors that modulate complex traits. However, it has been challenging to establish a direct link between variation in patterns of gene expression and variation in higher order traits such as neuropharmacological responses and patterns of behavior. Here we illustrate an approach that combines gene expression data with new bioinformatics resources to discover genes that potentially modulate behavior. We have exploited three complementary genetic models to obtain convergent evidence that differential expression of a subset of genes and molecular pathways influences ethanol-induced conditioned taste aversion (CTA). As a first step, cDNA microarrays were used to compare gene expression profiles of two null mutant mouse lines with difference in ethanol-induced aversion. Mice lacking a functional copy of G protein-gated potassium channel subunit 2 (Girk2) show a decrease in the aversive effects of ethanol, whereas preproenkephalin (Penk) null mutant mice show the opposite response. We hypothesize that these behavioral differences are generated in part by alterations in expression downstream of the null alleles. We then exploited the WebQTL databases to examine the genetic covariance between mRNA expression levels and measurements of ethanol-induced CTA in BXD recombinant inbred (RI) strains. Finally, we identified a subset of genes and functional groups associated with ethanol-induced CTA in both null mutant lines and BXD RI strains. Collectively, these approaches highlight the phosphatidylinositol signaling pathway and identify several genes including protein kinase C beta isoform and preproenkephalin in regulation of ethanol- induced conditioned taste aversion. Our results point to the increasing potential of the convergent approach and biological databases to investigate genetic mechanisms of complex traits.

Animals↗

PubMed2005

Gene methylation data--a new challenge for bioinformaticians?

OBJECTIVES: Changes in the status of DNA methylation, known as epigenetic alterations, are among the most common molecular alterations in human neoplasia. For the first time, we reported on the analysis of fecal DNA from patients with CRC to determine the feasibility, sensitivity and specificity of this approach. We want to present basic information about DNA methylation analysis in the context of bioinformatics, the study design and several statistical experiences with gene methylation data. Additionally we outline chances and new research questions in the field of DNA methylation. METHODS: We present current approaches to DNA methylation analysis based on one reference study. Its study design and the statistical analysis is reflected in the context of biomarker development. Finally we outline perspectives and research questions for statisticians and bioinformaticians. RESULTS: Identification of at least three genes as potential DNA methylation-based tumor marker genes (SFRP2, SFRP5, PGR). CONCLUSIONS: DNA methylation analysis is a rising topic in molecular genetics. Gene methylation will push the extension of biobanks to include new types of genetic data. Study design and statistical methods for the detection of methylation biomarkers must be improved. For the purpose of establishing methylation analysis as a new diagnostic/prognostic tool the adaptation of several approaches has become a challenging field of research activity.

Biomarkers, Tumor↗

PubMed2005

GenClust: a genetic algorithm for clustering gene expression data.

BACKGROUND: Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. RESULTS: GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, compact and easy to update; (b) it can be used naturally in conjunction with data driven internal validation methods. We have experimented with the FOM methodology, specifically conceived for validating clusters of gene expression data. The validity of GenClust has been assessed experimentally on real data sets, both with the use of validation measures and in comparison with other algorithms, i.e., Average Link, Cast, Click and K-means. CONCLUSION: Experiments show that none of the algorithms we have used is markedly superior to the others across data sets and validation measures; i.e., in many cases the observed differences between the worst and best performing algorithm may be statistically insignificant and they could be considered equivalent. However, there are cases in which an algorithm may be better than others and therefore worthwhile. In particular, experiments for GenClust show that, although simple in its data representation, it converges very rapidly to a local optimum and that its ability to identify meaningful clusters is comparable, and sometimes superior, to that of more sophisticated algorithms. In addition, it is well suited for use in conjunction with data driven internal validation measures and, in particular, the FOM methodology.

Algorithms↗

PubMed2003

Bioinformatics research on inter-racial difference in drug metabolism I. Analysis on frequencies of mutant alleles and poor metabolizers on CYP2D6 and CYP2C19.

The enzyme activities of CYP2D6 and CYP2C19 show a genetic polymorphism, and the frequency of poor metabolizers (PMs) on these enzymes depends on races. In the present study, the frequencies of mutant alleles and PMs in each race were analyzed based on information from published studies, considering the genetic polymorphisms of CYP2D6 and CYP2C19 as the causal factors of racial and inter-individual differences in pharmacokinetics. As a result, it was shown that there were racial differences in the frequencies of each mutant allele and PMs. The frequencies of PMs on CYP2D6 are 1.9% of Asians and 7.7% of Caucasians, and those of PMs on CYP2C19 are 15.8% of Asians and 2.2% of Caucasians. Based on the results, it was suggested that there would be racial differences in the frequencies of PM subjects whose blood concentrations might be higher for drugs metabolized by these enzymes. Additionally, it was suggested that enzyme activities would vary according to the number of functional alleles even in subjects judged to be extensive metabolizers (EMs). In the bridging study, genetic information regarding CYP2D6 and CYP2C19 of the subjects will help extrapolate foreign clinical data to a domestic population.

Journal Article↗