PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Bioinformatics analysis”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59Linked to original sources
PubMed2005

ESTs from brain and testis of White Leghorn and red junglefowl: annotation, bioinformatic classification of unknown transcripts and analysis of expression levels.

We report the generation, assembly and annotation of expressed sequence tags (ESTs) from four chicken cDNA libraries, constructed from brain and testis tissue dissected from red junglefowl and White Leghorn. 21,285 5'-end ESTs were generated and assembled into 2,813 contigs and 9,737 singletons, giving 12,549 tentative unique transcripts. The transcripts were annotated using BLAST by matching to known chicken genes or to putative homologues in other species using the major gene/protein databases. The results for these similarity searches are available on www.sbc.su.se/~arve/chicken. 4,129 (32.9%) of the transcripts remained without a significant match to gene/protein databases, a proportion of unmatched transcripts similar to earlier non-mammalian EST studies. To estimate how many of these transcripts may represent novel genes, they were studied for the presence of coding sequence. It was shown that most of the unique chicken transcripts do not contain coding parts of genes, but it was estimated that at least 400 of the transcripts contain coding sequence, indicating that 3.2% of avian genes belong to previously unknown gene families. Further BLAST search against dbEST left 1,649 (13.1%) of the transcripts unmatched to any library. The number of completely unmatched transcripts containing coding sequence was estimated at 180, giving a measure of the number of putative novel chicken genes identified in this study. 84.3% of the identified transcripts were found only in testis tissue, which has been poorly studied in earlier chicken EST studies. Large differences in expression levels were found between the brain and testis libraries for a large number of transcripts, and among the 525 most frequently represented transcripts, there were at least 20 transcripts with significant difference in expression levels between red junglefowl and White Leghorn.

Animals↗
PubMed2003

Simple intrasequence difference (SID) analysis: an original method to highlight and rank sub-structural interfaces in protein folds. Application to the folds of bovine pancreatic trypsin inhibitor, phospholipase A2, chymotrypsin and carboxypeptidase A.

We present Simple Intrasequence Difference (SID) analysis, a novel bioinformatic technique designed to help comprehend the properties of protein fold topologies. The analysis grades numerically every residue position in a given protein 3D structure according to the topological situation of the position in the folded chain. This results in an expression of the potential contribution of each residue position and its vicinity towards the integrity of the molecular conformation. Contiguous highly graded residues delineate the sub-structural interfaces that arise from the presence within the molecular fold of discrete domains and sub-domains. This comprehensive rendering of the internal arrangement of chain interfacing helps predict the potential for site-specific inductions (e.g. via mutations or ligand binding) of conformational change in the fold. Whereas SID analysis of single folds can convey an idea of the basic potential for topological adjustment in the protein family, comparative SID analysis of related folds focuses attention on those areas of the family fold where evolutionary changes, activation events and ligand binding have had the most topological impact. For demonstration, SID analysis is applied to the folds of pancreatic trypsin inhibitor (Kunitz), phospholipase A(2), chymotrypsin and carboxypeptidase A. We find that many of the potentially vulnerable sub-structural interfaces tend to be protected in the fold interior, in many cases stabilised by disulfide bridges spanning the interface. However, the most prominent interfaces tend to be externally accessible, without remedial stabilisation by disulfide bridges. These latter interfaces are associated so closely with the known functional sites that alterations to the interfacial juxtapositions should influence recognition and catalytic behaviour directly. This shows how side chain mutations, chemical modifications and binding events remote from the sites can nevertheless adjust, via interfacial realignment, the conformations and emergent properties of the sites. The close association also provides clear opportunities for interfacial rearrangements to follow intermolecular recognition events in the sites, facilitating translation of the binding into adjustment of the molecular conformation in areas distant from the sites. As a direct consequence of the topological arrangements, a large proportion of the molecular structure has the capacity to shape the character of the functional sites and, conversely, binding at these sites has the potential to radiate influence to the rest of the molecule. For the enzymes considered, the evidence is consistent with the possibility that primary and secondary binding by the substrate enhances catalytic efficiency by imposing conformational change upon the catalytic centre via adjustments to the fold. This influence may be expressed as favourable adjustment of the catalytic geometry, transition state ensemble, energy propagation pathway, or as a physical strain exerted on the substrate bond to be cleaved. The scale of the adjustments, and their importance to the mechanisms, may have been seriously underestimated.

Amino Acid Sequence↗
PubMed2006

Receptor-binding sites: bioinformatic approaches.

It is increasingly clear that both transient and long-lasting interactions between biomacromolecules and their molecular partners are the most fundamental of all biological mechanisms and lie at the conceptual heart of protein function. In particular, the protein-binding site is the most fascinating and important mechanistic arbiter of protein function. In this review, I examine the nature of protein-binding sites found in both ligand-binding receptors and substrate-binding enzymes. I highlight two important concepts underlying the identification and analysis of binding sites. The first is based on knowledge: when one knows the location of a binding site in one protein, one can "inherit" the site from one protein to another. The second approach involves the a priori prediction of a binding site from a sequence or a structure. The full and complete analysis of binding sites will necessarily involve the full range of informatic techniques ranging from sequence-based bioinformatic analysis through structural bioinformatics to computational chemistry and molecular physics. Integration of both diverse experimental and diverse theoretical approaches is thus a mandatory requirement in the evaluation of binding sites and the binding events that occur within them.

Binding Sites↗
PubMed2001

BioWAP, mobile Internet service for bioinformatics.

UNLABELLED: We have developed a new Internet service, which provides mobile access to bioinformatics databases and software tools. The BioWAP service facilitates access to basic bioinformatics databases and analysis tools from everywhere without a PC or a laptop computer. Both open source bioinformatics program suites and Internet services, which are not designed for mobile Internet access, were utilized in the BioWAP service. AVAILABILITY: The BioWAP service starting page can be browsed with any WAP terminal from http://bioinf.uta.fi/wml/welcome.wml.

Computational Biology↗
PubMed2006

Evolving research trends in bioinformatics.

The cross-disciplinary nature of bioinformatics entails co-evolution with other biomedical disciplines, whereby some bioinformatics applications become popular in certain disciplines and, in turn, these disciplines influence the focus of future bioinformatics development efforts. We observe here that the growth of computational approaches within various biomedical disciplines is not merely a reflection of a general extended usage of computers and the Internet, but due to the production of useful bioinformatics databases and methods for the rest of the biomedical scientific community. We have used the abstracts stored both in the MEDLINE database of biomedical literature and in NIH-funded project grants, to quantify two effects. First, we examine the biomedical literature as a whole and find that the use of computational methods has become increasingly prevalent across biomedical disciplines over the past three decades, while use of databases and the Internet have been rapidly increasing over the past decade. Second, we study the recent trends in the use of bioinformatics topics. We observe that molecular sequence databases are a widely adopted contribution in biomedicine from the field of bioinformatics, and that microarray analysis is one of the major new topics engaged by the bioinformatics community. Via this analysis, we were able to identify areas of rapid growth in the use of informatics to aid in curriculum planning, development of computational infrastructure and strategies for workforce education and funding.

Computational Biology↗
PubMed2001

Bioinformatics issues for automating the annotation of genomic sequences.

The rapid explosion in the amount of biological data being generated worldwide is surpassing efforts to manage analysis of the data. As part of an ongoing project to automate and manage bioinformatics analysis, the authors have designed and implemented a simple automated annotation system, which is described in this paper. The system is applied to existing GenBank/DDBJ/EMBL entries and compared with existing annotations to illustrate not only potential errors but also that they are generally not up-to-date, as a result of new versions of analysis tools and updates of genomic repositories. We highlight the important Bioinformatics issues of storage and management of information to ensure data and results are kept up-to-date in light of new information becoming available. Surprisingly, from just four database entries, a significant number of new features were found. We describe the results as well as identify important issues that need to be addressed in order to automate the re-analysis/re-annotation of genomic sequences within a reasonable timeframe.

Computational Biology↗
PubMed2004

Poxvirus bioinformatics.

Biochemical and functional analysis of poxvirus genomes, genes, and proteins has entered a new era with the recent sequencing of more than 30 poxvirus genomes. The management and analysis of this volume of sequence data in an efficient and effective manner requires specialized computer software. This chapter describes a number of bioinformatics techniques useful for analyzing poxvirus genomes. Some of the software discussed here have been developed by members of the Poxvirus Bioinformatics Resource Center (PBRC; funded by National Institutes of Health [NIH]) specifically for use with poxvirus genomes. These programs or, more accurately, suites of programs have many functions dedicated to poxvirus genome characterization. Significantly, this software has been designed with ease of use at a single location as the major goal.

Computational Biology↗
PubMed2005

Emerging issues of the expression profiling technologies for the study of gynecologic cancer.

Evaluation of the prognostic parameters of gynecologic cancer has shown their failure for classification according to the clinical behavior or the prediction of its outcome. This weakness has important implications on prognosis and treatment. The increasing understanding of the complexity of the human genome, coupled with the development of high throughput analysis techniques and bioinformatics tools, has changed our concepts on cancer biology, by shifting our targets to a global analysis of the transcriptome and the proteome, linking genes and their products into functional pathways. These approaches permit the documentation of expression patterns of thousands of genes within a cell. With the use of DNA microarray technology, it is feasible to identify signature patterns of expression in tumor samples that faithfully correlate with its biology, providing accurate prognosis for each cancer patient and thus a rational customized treatment. At this stage, there is a need for systematic studies for the validation of these novel approaches. In this review, we provide a basic background of the concept of the technology, highlight several emerging issues from their applications on gynecologic cancer, discuss a series of important themes and problems regarding their interpretation and relevance for the clinicians, and comment on future areas of research.

Computational Biology↗
PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗
PubMed2025

Systematically investigating and identifying bacteriocins in the human gut microbiome.

Human gut microbiota produces unmodified bacteriocins, natural antimicrobial peptides that protect against pathogens and regulate host physiology. However, current bioinformatic tools limit the comprehensive investigation of bacteriocins' biosynthesis, obstructing research into their biological functions. Here, we introduce IIBacFinder, a superior analysis pipeline for identifying unmodified class II bacteriocins. Through large-scale bioinformatic analysis and experimental validation, we demonstrate their widespread distribution across the bacterial kingdom, with most being habitat specific. Analyzing over 280,000 bacterial genomes, we reveal the diverse potential of human gut bacteria to produce these bacteriocins. Guided by meta-omics analysis, we synthesized 26 hypothetical bacteriocins from gut commensal species, with 16 showing antibacterial activities. Further ex vivo tests show minimal impact of narrow-spectrum bacteriocins on human fecal microbiota. Our study highlights the huge biosynthetic potential of unmodified bacteriocins in the human gut, paving the way for understanding their biological functions and health implications.

Humans↗
PubMed2003

AnaBench: a Web/CORBA-based workbench for biomolecular sequence analysis.

BACKGROUND: Sequence data analyses such as gene identification, structure modeling or phylogenetic tree inference involve a variety of bioinformatics software tools. Due to the heterogeneity of bioinformatics tools in usage and data requirements, scientists spend much effort on technical issues including data format, storage and management of input and output, and memorization of numerous parameters and multi-step analysis procedures. RESULTS: In this paper, we present the design and implementation of AnaBench, an interactive, Web-based bioinformatics Analysis workBench allowing streamlined data analysis. Our philosophy was to minimize the technical effort not only for the scientist who uses this environment to analyze data, but also for the administrator who manages and maintains the workbench. With new bioinformatics tools published daily, AnaBench permits easy incorporation of additional tools. This flexibility is achieved by employing a three-tier distributed architecture and recent technologies including CORBA middleware, Java, JDBC, and JSP. A CORBA server permits transparent access to a workbench management database, which stores information about the users, their data, as well as the description of all bioinformatics applications that can be launched from the workbench. CONCLUSION: AnaBench is an efficient and intuitive interactive bioinformatics environment, which offers scientists application-driven, data-driven and protocol-driven analysis approaches. The prototype of AnaBench, managed by a team at the Université de Montréal, is accessible on-line at: http://malawimonas.bcm.umontreal.ca:8091/anabench. Please contact the authors for details about setting up a local-network AnaBench site elsewhere.

Computational Biology↗
PubMed2006

Metabolomics technology and bioinformatics.

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.

Animals↗
PubMed2006

SWAKK: a web server for detecting positive selection in proteins using a sliding window substitution rate analysis.

We present a bioinformatic web server (SWAKK) for detecting amino acid sites or regions of a protein under positive selection. It estimates the ratio of non-synonymous to synonymous substitution rates (K(A)/K(S)) between a pair of protein-coding DNA sequences, by sliding a 3D window, or sphere, across one reference structure. The program displays the results on the 3D protein structure. In addition, for comparison or when a reference structure is unavailable, the server can also perform a sliding window analysis on the primary sequence. The SWAKK web server is available at http://oxytricha.princeton.edu/SWAKK/.

Amino Acid Substitution↗
PubMed2026

Exploring the Genetic Link between Irritable Bowel Syndrome and Polycystic Ovary Syndrome: Bidirectional Mendelian Randomization and Machine Learning Approaches.

BACKGROUND: Research has shown a certain correlation between polycystic ovary syndrome (PCOS) and irritable bowel syndrome (IBS). The study aims to determine the directionality and underlying biological processes influencing the relationship between these two disorders. METHODS: We explored the causal relationship between IBS and PCOS by conducting a comprehensive bidirectional Mendelian randomization (MR) analysis using five different methods and conducted robustness assessments. We extracted differentially expressed genes from the IBS and PCOS datasets for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Additionally, we developed a protein-protein interaction (PPI) network and applied the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) methodologies to pinpoint key diagnostic markers. Diagnostic efficacy was further assessed through Receiver Operating Characteristic (ROC) curve analysis for selected genes. Finally, single-sample gene set enrichment analysis (ssGSEA) was carried out to examine immune cell infiltration in IBS and PCOS. RESULTS: MR analysis identified a causal effect of PCOS on IBS (IVW, OR = 1.034, 95% CI: 1.003-1.065, P = 0.029). Conversely, no relationship between IBS and PCOS was observed in the reverse analysis. Furthermore, integrative bioinformatics and machine learning analyses identified CD14 and CASP1 as key diagnostic biomarkers for both IBS and PCOS, which were significantly associated with immune cell infiltration. CONCLUSION: MR analysis has demonstrated a significant positive causal relationship between PCOS and IBS, though the reverse causality from IBS to PCOS appeared non-significant. The genes CD14 and CASP1 emerged as potential shared diagnostic markers between these two conditions.

Polycystic Ovary Syndrome↗
PubMed2006

AMDA: an R package for the automated microarray data analysis.

BACKGROUND: Microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and new experiments are constantly being performed. In spite of this fact, few and limited tools exist for quickly and easily analyzing the results. Microarray analysis can be challenging for researchers without the necessary training and it can be time-consuming for service providers with many users. RESULTS: To address these problems we have developed an automated microarray data analysis (AMDA) software, which provides scientists with an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA is free and it is available as an R package. It is based on the Bioconductor project that provides a number of powerful bioinformatics and microarray analysis tools. This automated pipeline integrates different functions available in the R and Bioconductor projects with newly developed functions. AMDA covers all of the steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation. CONCLUSION: AMDA is freely available as an R package under the GPL license. The package as well as an example analysis report can be downloaded in the Services/Bioinformatics section of the Genopolis http://www.genopolis.it/.

Algorithms↗
PubMed2004

Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes.

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.

Amino Acid Sequence↗