Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “bioinformatic database”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 1,405 records · Page 78Linked to original sources
PubMed2006

Postgenomic bioinformatic analysis of yeast artificial chromosome sequence.

The free availability of multiple genomic sequences represents one of the greatest advances in biology of the new millennium, and promises to revolutionize our ability to determine and treat the causes of human disease. This chapter highlights a number of basic, freely available, and user-friendly bioinformatic techniques that can be used to predict the functional genetic contents of specific yeast artificial chromosome (YAC) clones. The content of this chapter is written for the level of graduate students, who may be relatively inexperienced with the use of computers for analyzing DNA sequences. The basic instructions that allow the identification of the genomic sequence of interest and to download this sequence onto a personal computer from an online database are presented. Simple instructions are also given on how to perform basic sequence manipulations, how to use online tools to design polymerase chain reaction primers, and how to map restriction sites. Also described are more complicated programs that rapidly and efficiently perform genome alignments that, in addition to predicting the location of protein coding sequences, allow the prediction of functional genomic sequences, such as cis regulatory elements and scaffold/matrix attachment sites. The availability of genomic sequences and the rapidly expanding numbers of predictive programs that allow the predictive analysis of these sequences promises to greatly facilitate the use of YAC clones in the search for the causes of disease.

Chromosomes, Artificial, Yeast↗
PubMed2026

MicroRNA-181a-5p promotes papillary thyroid carcinoma progress via the PTEN/AKT pathway.

The objective of this investigation was to determine the expression profile and latent mechanism of microRNA-181a-5p (miR-181a-5p) in the genesis and progression of papillary thyroid cancer (PTC). MiR-181a-5p was discovered to be upregulated in PTC tissues and cells in this study, as confirmed by RT‒qPCR and The Cancer Genome Atlas database. Notably, in PTC patients, the miR-181a-5p level was linked to tumor size and thyroid capsule invasion. A series of experiments demonstrated that miR-181a-5p upregulation in PTC cells notably enhanced proliferation, motility, and invasion, whereas suppressing miR-181a-5p hindered these functions. Western blotting revealed that miR-181a-5p suppressed PTEN expression, boosting the activation of phosphorylated AKT (P-AKT). According to predictive bioinformatics research and luciferase reporter gene tests, miR-181a-5p may target a specific binding site on the PTEN 3'UTR. To sum up, this study indicated that miR-181a-5p promoted PTC progression through the PTEN/Akt pathway. This investigation reveals a potential mechanism for PTC progression and provides a foundation for clinical therapies.

MicroRNAs↗
PubMed2005

Structural relatedness of plant food allergens with specific reference to cross-reactive allergens: an in silico analysis.

BACKGROUND: The body of sequence and structural information on allergens and the sequence analysis of whole plant genomes are facilitating the application of bioinformatic approaches to identifying and defining plant allergens. OBJECTIVE: An in silico approach was used to quantify the distribution of plant food allergen sequences across protein families and to develop and apply a novel means of assessing conserved surface features important for IgE cross-reactivity. METHODS: Plant food allergen sequences were classified into Pfam families on the basis of sequence homology. Contact surface areas of selected proteins were calculated with MOLMOL by using a 1.4-A probe, corrected by removing contributions from IgE inaccessible main chains and side chains forming the ligand binding sites. RESULTS: A set of 129 food allergen sequences were classified into only 20 of 3849 possible Pfam families, with 4 families accounting for more than 65% of food allergens. Structural bioinformatic analysis of conserved exterior main chains and amino acid side chains in cross-reactive homologues of Bet v 1 and nonspecific lipid transfer proteins showed higher levels of similarity than shown by simple sequence comparisons. Thus, 75% of the Mal d 1 surface is likely to bind anti-Bet v 1 antibodies, compared with a sequence identity of approximately 56%. CONCLUSION: Most plant food allergens belong to only 4 structural families, indicating that conserved structures and biological activities may play a role in determining or promoting allergenic properties. Structural bioinformatic analysis shows that conservation of 3-dimensional structure should be included in any assessment of potential IgE cross-reactivity in, for example, novel proteins.

Allergens↗
PubMed2004

Accurate classification of protein structural families using coherent subgraph analysis.

Protein structural annotation and classification is an important problem in bioinformatics. We report on the development of an efficient subgraph mining technique and its application to finding characteristic substructural patterns within protein structural families. In our method, protein structures are represented by graphs where the nodes are residues and the edges connect residues found within certain distance from each other. Application of subgraph mining to proteins is challenging for a number reasons: (1) protein graphs are large and complex, (2) current protein databases are large and continue to grow rapidly, and (3) only a small fraction of the frequent subgraphs among the huge pool of all possible subgraphs could be significant in the context of protein classification. To address these challenges, we have developed an information theoretic model called coherent subgraph mining. From information theory, the entropy of a random variable X measures the information content carried by X and the Mutual Information (MI) between two random variables X and Y measures the correlation between X and Y. We define a subgraph X as coherent if it is strongly correlated with every sufficiently large sub-subgraph Y embedded in it. Based on the MI metric, we have designed a search scheme that only reports coherent subgraphs. To determine the significance of coherent protein subgraphs, we have conducted an experimental study in which all coherent subgraphs were identified in several protein structural families annotated in the SCOP database (Murzin et al, 1995). The Support Vector Machine algorithm was used to classify proteins from different families under the binary classification scheme. We find that this approach identifies spatial motifs unique to individual SCOP families and affords excellent discrimination between families.

Algorithms↗
PubMed2003

GeneViTo: visualizing gene-product functional and structural features in genomic datasets.

BACKGROUND: The availability of increasing amounts of sequence data from completely sequenced genomes boosts the development of new computational methods for automated genome annotation and comparative genomics. Therefore, there is a need for tools that facilitate the visualization of raw data and results produced by bioinformatics analysis, providing new means for interactive genome exploration. Visual inspection can be used as a basis to assess the quality of various analysis algorithms and to aid in-depth genomic studies. RESULTS: GeneViTo is a JAVA-based computer application that serves as a workbench for genome-wide analysis through visual interaction. The application deals with various experimental information concerning both DNA and protein sequences (derived from public sequence databases or proprietary data sources) and meta-data obtained by various prediction algorithms, classification schemes or user-defined features. Interaction with a Graphical User Interface (GUI) allows easy extraction of genomic and proteomic data referring to the sequence itself, sequence features, or general structural and functional features. Emphasis is laid on the potential comparison between annotation and prediction data in order to offer a supplement to the provided information, especially in cases of "poor" annotation, or an evaluation of available predictions. Moreover, desired information can be output in high quality JPEG image files for further elaboration and scientific use. A compilation of properly formatted GeneViTo input data for demonstration is available to interested readers for two completely sequenced prokaryotes, Chlamydia trachomatis and Methanococcus jannaschii. CONCLUSIONS: GeneViTo offers an inspectional view of genomic functional elements, concerning data stemming both from database annotation and analysis tools for an overall analysis of existing genomes. The application is compatible with Linux or Windows ME-2000-XP operating systems, provided that the appropriate Java Runtime Environment is already installed in the system.

Bacterial Proton-Translocating ATPases↗
PubMed2020

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

BACKGROUND: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. METHOD: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). RESULTS: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. CONCLUSION: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Adenocarcinoma of Lung↗
PubMed2005

Bioinformatics challenges in proteomics.

A little after the genomic revolution had been celebrated, it seemed as if a competition began to found new -omics disciplines that ultimately all have the same goal, the understanding of biological function. There are many similar definitions for proteomics that can be summarized as follows: proteomics is a large-scale study of structure and function of proteins in an organism or cell. Importantly, the proteome is much more variable than the genome through its interactions with the genome and secondary modifications. It differs depending on the tissue and stage in life-cycle. Hence, proteomics is a very diverse discipline that uses a variety of experimental set-ups and targets in order to elucidate function. Its dissociation from other disciplines can only remain artificial. The bioinformatics applied to proteomics are equally varied. In this review we will focus mainly on a few areas of bioinformatics that seem to us as particularly noteworthy or characteristic for proteomics research, for example in 2DE analysis or mass spectrometry. Another important task of bioinformatics is the prediction of functional properties. We will summarize the approaches taken in order to predict protein networks, which are based on the extensive integration of several kinds of -omics data. We will give a short overview of a demanding field in computational biology, the analysis and prediction of protein 3D structures. In order to provide a broader perspective we will close this review with a generalized description of activities and databases in the realm of proteomics.

Animals↗
PubMed2005

An integrated strategy for the optimization of microarray data interpretation.

The completion of a microarray experiment represents just a starting point toward understanding the biology of interest. A follow-up strategy is needed to fully elucidate the functional significance of microarray-derived measurements of differential expression. Given the fact that no single approach can fully unravel the fundamental biology that is typically quite complex, the follow-up strategy must be integrated at multiple levels encompassing bioinformatics, genomics, and proteomics. In this review, we discuss an integrative approach, which can be used to prioritize microarray-derived candidate genes, define their functions, and place them in the context of the biological system being studied.

Animals↗
PubMed2006

Combining bioinformatics and phylogenetics to identify large sets of single-copy orthologous genes (COSII) for comparative, evolutionary and systematic studies: a test case in the euasterid plant clade.

We report herein the application of a set of algorithms to identify a large number (2869) of single-copy orthologs (COSII), which are shared by most, if not all, euasterid plant species as well as the model species Arabidopsis. Alignments of the orthologous sequences across multiple species enabled the design of "universal PCR primers," which can be used to amplify the corresponding orthologs from a broad range of taxa, including those lacking any sequence databases. Functional annotation revealed that these conserved, single-copy orthologs encode a higher-than-expected frequency of proteins transported and utilized in organelles and a paucity of proteins associated with cell walls, protein kinases, transcription factors, and signal transduction. The enabling power of this new ortholog resource was demonstrated in phylogenetic studies, as well as in comparative mapping across the plant families tomato (family Solanaceae) and coffee (family Rubiaceae). The combined results of these studies provide compelling evidence that (1) the ancestral species that gave rise to the core euasterid families Solanaceae and Rubiaceae had a basic chromosome number of x=11 or 12.2) No whole-genome duplication event (i.e., polyploidization) occurred immediately prior to or after the radiation of either Solanaceae or Rubiaceae as has been recently suggested.

Algorithms↗
PubMed2003

Characterization of the human heart mitochondrial proteome.

To gain a better understanding of the critical role of mitochondria in cell function, we have compiled an extensive catalogue of the mitochondrial proteome using highly purified mitochondria from normal human heart tissue. Sucrose gradient centrifugation was employed to partially resolve protein complexes whose individual protein components were separated by one-dimensional PAGE. Total in-gel processing and subsequent detection by mass spectrometry and rigorous bioinformatic analysis yielded a total of 615 distinct protein identifications. All protein pI values, molecular weight ranges, and hydrophobicities were represented. The coverage of the known subunits of the oxidative phosphorylation machinery within the inner mitochondrial membrane was >90%. A significant proportion of identified proteins are involved in signaling, RNA, DNA, and protein synthesis, ion transport, and lipid metabolism. The biochemical roles of 19% of the identified proteins have not been defined. This database of proteins provides a comprehensive resource for the discovery of novel mitochondrial functions and pathways.

Adolescent↗
PubMed2002

A new method to estimate ligand-receptor energetics.

In the discovery of new drugs, lead identification and optimization have assumed critical importance given the number of drug targets generated from genetic, genomics, and proteomic technologies. High-throughput experimental screening assays have been complemented recently by "virtual screening" approaches to identify and filter potential ligands when the characteristics of a target receptor structure of interest are known. Virtual screening mandates a reliable procedure for automatic ranking of structurally distinct ligands in compound library databases. Computing a rank score requires the accurate prediction of binding affinities between these ligands and the target. Many current scoring strategies require information about the target three-dimensional structure. In this study, a new method to estimate the free binding energy between a ligand and receptor is proposed. We extend a central idea previously reported (Bock, J. R., and Gough, D. A. (2001) Predicting protein-protein interactions from primary structure. Bioinformatics 17, 455-460; Bock, J. R., and Gough, D. A. (2002) Whole-proteome interaction mining. Bioinformatics, in press) that uses simple descriptors to represent biomolecules as input examples to train a support vector machine (Smola, A. J., and Schölkopf, B. (1998) A Tutorial on Support Vector Regression, Neuro-COLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK) and the application of the trained system to previously unseen pairs, estimating their propensity for interaction. Here we seek to learn the function that maps features of a receptor-ligand pair onto their equilibrium free binding energy. These features do not comprise any direct information about the three-dimensional structures of ligand or target. In cross-validation experiments, it is demonstrated that objective measurements of prediction error rate and rank-ordering statistics are competitive with those of several other investigations, most of which depend on three-dimensional structural data. The size of the sample (n = 2,671) indicates that this approach is robust and may have widespread applicability beyond restricted families of receptor types. It is concluded that newly sequenced proteins, or those for which three-dimensional crystal structures are not easily obtained, can be rapidly analyzed for their binding potential against a library of ligands using this methodology.

Drug Design↗
PubMed2001

Genomic approaches to elucidating the pathophysiology of renal diseases.

The physiological and pathological processes of the kidney as a whole can now be analyzed with a molecular precision at a genomic-scale. Using massively parallel cDNA microarray technology, the mRNA expression of thousands of genes can be quantified simultaneously. The advantages of microarray analyses include the ability to examine the interaction of several genes or the entire genome in a single experiment. Bioinformatics approaches such as data mining through mathematical condensation of the massive gene expression profiles are essential for elucidating molecular and biological logic underlying gene expression programs. Genes that encode similar protein components are often coordinately regulated. Recent application of gene expression profiling to the normal human renal cortical tissue, experimental in vitro and in vivo models has shown that cellular activation is accompanied by changes of hundreds of genes in parallel. The databases of gene expression emerging from these studies will be used to interpret the pathological changes in gene expression that accompany a variety of human renal diseases.

Gene Expression Profiling↗
PubMed2006

The pitfalls of proteomics experiments without the correct use of bioinformatics tools.

The elucidation of the entire genomic sequence of various organisms, from viruses to complex metazoans, most recently man, is undoubtedly the greatest triumph of molecular biology since the discovery of the DNA double helix. Over the past two decades, the focus of molecular biology has gradually moved from genomes to proteomes, the intention being to discover the functions of the genes themselves. The postgenomic era stimulated the development of new techniques (e.g. 2-DE and MS) and bioinformatics tools to identify the functions, reactions, interactions and location of the gene products in tissues and/or cells of living organisms. Both 2-DE and MS have been very successfully employed to identify proteins involved in biological phenomena (e.g. immunity, cancer, host-parasite interactions, etc.), although recently, several papers have emphasised the pitfalls of 2-DE experiments, especially in relation to experimental design, poor statistical treatment and the high rate of 'false positive' results with regard to protein identification. In the light of these perceived problems, we review the advantages and misuses of bioinformatics tools - from realisation of 2-DE gels to the identification of candidate protein spots - and suggest some useful avenues to improve the quality of 2-DE experiments. In addition, we present key steps which, in our view, need to be to taken into consideration during such analyses. Lastly, we present novel biological entities named 'interactomes', and the bioinformatics tools developed to analyse the large protein-protein interaction networks they form, along with several new perspectives of the field.

Amino Acid Sequence↗
PubMed2024

Bioinformatics analysis to identify the relationship between human papillomavirus-associated cervical cancer, toll-like receptors and exomes: A genetic epidemiology study.

INTRODUCTION: Genetic variants may influence Toll-like receptor (TLR) signaling in the immune response to human papillomavirus (HPV) infection and lead to cervical cancer. In this study, we investigated the pattern of TLR expression in the transcriptome of HPV-positive and HPV-negative cervical cancer samples and looked for variants potentially related to TLR gene alterations in exomes from different populations. MATERIALS AND METHODS: A cervical tissue sample from 28 women, which was obtained from the Gene Expression Omnibus database, was used to examine TLR gene expression. Subsequently, the transcripts related to the TLRs that showed significant gene expression were queried in the Genome Aggregation Database to search for variants in more than 5,728 exomes from different ethnicities. RESULTS: Cancer and HPV were found to be associated (p<0.0001). TLR1(p = 0.001), TLR3(p = 0.004), TLR4(221060_s_at)(p = 0.001), TLR7(p = 0.001;p = 0.047), TLR8(p = 0.002) and TLR10(p = 0.008) were negatively regulated, while TLR4(1552798_at)(p<0.0001) and TLR6(p = 0.019) were positively regulated in HPV-positive patients (p<0.05). The clinical significance of the variants was statistically significant for TLR1, TLR3, TLR6 and TLR8 in association with ethnicity. Genetic variants in different TLRs have been found in various ethnic populations. Variants of the TLR gene were of the following types: TLR1(5_prime_UTR), TLR4(start_lost), TLR8(synonymous;missense) and TLR10(3_prime_UTR). The "missense" variant was found to have a risk of its clinical significance being pathogenic in South Asian populations (OR = 56,820[95%CI:40,206,80,299]). CONCLUSION: The results of this study suggest that the variants found in the transcriptomes of different populations may lead to impairment of the functional aspect of TLRs that show significant gene expression in cervical cancer samples caused by HPV.

Humans↗
PubMed2003

Proteome analysis using isoelectric focusing in immobilized pH gradient gels followed by mass spectrometry.

Over the past several years, a large effort has been focused on improvements of two-dimensional (2-D) gel electrophoresis-based proteomics technology, and on development of novel approaches for proteome analysis. Here, we describe the application of an alternative strategy for the analysis of complex proteomes. The strategy combines isoelectric focusing in immobilized pH gradient strips (in-gel IEF), mass spectrometry (MS), and bioinformatics. A protein mixture is separated by in-gel IEF, and the entire strip is cut into a set of gel sections. Proteins in each gel section are digested with trypsin, and the tryptic peptides are subjected to liquid chromatography-nanoelectrospray-quadrupole ion-trap tandem mass spectrometry (LC-ESI-MS/MS). The LC-ESI-MS/MS data are used to identify the proteins through searches of a protein sequence database. Using this in-gel IEF-LC-MS/MS strategy, we have identified 127 proteins from a human pituitary. This study demonstrates the potential of the in-gel IEF-LC-MS/MS approach for analyses of complex mammalian proteomes.

Computational Biology↗
PubMed2006

Bioinformatics for study of autoimmunity.

Recent years have witnessed an explosive growth in available biological data pertaining to autoimmunity research. This includes a tremendous quantity of sequence data (biological structures, genetic and physical maps, pathways, etc.) generated by genome and proteome projects plus extensive clinical and epidemiological data. Autoimmunity research stands to greatly benefit from this data so long as appropriate strategies are available to enable full access to and utilization of this data. The quantity and complexity of this biological data necessitates use of advanced bioinformatics strategies for its efficient retrieval, analysis and interpretation. Major progress has been made in development of specialized tools for storage, analysis and modeling of immunological data, and this has led to development of a whole new field know as immunoinformatics. With advances in novel high-throughput immunology technologies immunoinformatics is transforming understanding of how the immune system functions. This paper reviews advances in the field of immunoinformatics pertinent to autoimmunity research including databases, tools in genomics and proteomics, tools for study of B- and T-cell epitopes, integrative approaches, and web servers.

Allergy and Immunology↗
PubMed2001

Mining literature for protein-protein interactions.

MOTIVATION: A central problem in bioinformatics is how to capture information from the vast current scientific literature in a form suitable for analysis by computer. We address the special case of information on protein-protein interactions, and show that the frequencies of words in Medline abstracts can be used to determine whether or not a given paper discusses protein-protein interactions. For those papers determined to discuss this topic, the relevant information can be captured for the Database of Interacting PROTEINS: Furthermore, suitable gene annotations can also be captured. RESULTS: Our Bayesian approach scores Medline abstracts for probability of discussing the topic of interest according to the frequencies of discriminating words found in the abstract. More than 80 discriminating words (e.g. complex, interaction, two-hybrid) were determined from a training set of 260 Medline abstracts corresponding to previously validated entries in the Database of Interacting Proteins. Using these words and a log likelihood scoring function, approximately 2000 Medline abstracts were identified as describing interactions between yeast proteins. This approach now forms the basis for the rapid expansion of the Database of Interacting Proteins.

Algorithms↗
PubMed2005

A new progressive-iterative algorithm for multiple structure alignment.

MOTIVATION: Multiple structure alignments are becoming important tools in many aspects of structural bioinformatics. The current explosion in the number of available protein structures demands multiple structural alignment algorithms with an adequate balance of accuracy and speed, for large scale applications in structural genomics, protein structure prediction and protein classification. RESULTS: A new multiple structural alignment program, MAMMOTH-mult, is described. It is demonstrated that the alignments obtained with the new method are an improvement over previous manual or automatic alignments available in several widely used databases at all structural levels. Detailed analysis of the structural alignments for a few representative cases indicates that MAMMOTH-mult delivers biologically meaningful trees and conservation at the sequence and structural levels of functional motifs in the alignments. An important improvement over previous methods is the reduction in computational cost. Typical alignments take only a median time of 5 CPU seconds in a single R12000 processor. MAMMOTH-mult is particularly useful for large scale applications. AVAILABILITY: http://ub.cbm.uam.es/mammoth/mult.

Algorithms↗