共查询到20条相似文献，搜索用时 31 毫秒
1.
Jose L Izquierdo-García Ignacio Rodríguez Angelos Kyriazis Palmira Villa Pilar Barreiro Manuel Desco Jesús Ruiz-Cabello 《BMC bioinformatics》2009,10(1):363
Background
Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The "Metabonomic Package" has been developed for metabonomics research as open-source software and uses the R statistical libraries. 相似文献2.
Background
Polyketides are secondary metabolites of microorganisms with diverse biological activities, including pharmacological functions such as antibiotic, antitumor and agrochemical properties. Polyketides are synthesized by serialized reactions of a set of enzymes called polyketide synthase(PKS)s, which coordinate the elongation of carbon skeletons by the stepwise condensation of short carbon precursors. Due to their importance as drugs, the volume of data on polyketides is rapidly increasing and creating a need for computational analysis methods for efficient polyketide research. Moreover, the increasing use of genetic engineering to research new kinds of polyketides requires genome wide analysis. 相似文献3.
Sebastian Wolf Stephan Schmidt Matthias Müller-Hannemann Steffen Neumann 《BMC bioinformatics》2010,11(1):148
Background
Mass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites. 相似文献4.
Background
Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale. 相似文献5.
Jan Hummel Michaela Niemann Stefanie Wienkoop Waltraud Schulze Dirk Steinhauser Joachim Selbig Dirk Walther Wolfram Weckwerth 《BMC bioinformatics》2007,8(1):216
Background
In the last decade, techniques were established for the large scale genome-wide analysis of proteins, RNA, and metabolites, and database solutions have been developed to manage the generated data sets. The Golm Metabolome Database for metabolite data (GMD) represents one such effort to make these data broadly available and to interconnect the different molecular levels of a biological system [1]. As data interpretation in the light of already existing data becomes increasingly important, these initiatives are an essential part of current and future systems biology. 相似文献6.
Background
Metabolome analysis with GC/MS has meanwhile been established as one of the "omics" techniques. Compound identification is done by comparison of the MS data with compound libraries. Mass spectral libraries in the field of metabolomics ought to connect the relevant mass traces of the metabolites to other relevant data, e.g. formulas, chemical structures, identification numbers to other databases etc. Since existing solutions are either commercial and therefore only available for certain instruments or not capable of storing such information, there is need to provide a software tool for the management of such data. 相似文献7.
8.
Hiroshi Tsugawa Yuki Tsujimoto Masanori Arita Takeshi Bamba Eiichiro Fukusaki 《BMC bioinformatics》2011,12(1):131
Background
The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns". 相似文献9.
Background
Phenylpropanoids are the precursors to a range of important plant metabolites such as the cell wall constituent lignin and the secondary metabolites belonging to the flavonoid/stilbene class of compounds. The latter class of plant natural products has been shown to function in a wide range of biological activities. During the last few years an increasing number of health benefits have been associated with these compounds. In particular, they demonstrate potent antioxidant activity and the ability to selectively inhibit certain tyrosine kinases. Biosynthesis of many medicinally important plant secondary metabolites, including stilbenes, is frequently not very well understood and under tight spatial and temporal control, limiting their availability from plant sources. As an alternative, we sought to develop an approach for the biosynthesis of diverse stilbenes by engineered recombinant microbial cells. 相似文献10.
Background
When accurate models for the divergent evolution of protein sequences are integrated with complementary biological information, such as folded protein structures, analyses of the combined data often lead to new hypotheses about molecular physiology. This represents an excellent example of how bioinformatics can be used to guide experimental research. However, progress in this direction has been slowed by the lack of a publicly available resource suitable for general use. 相似文献11.
12.
Patrick J. C. Tardivel Cécile Canlet Gaëlle Lefort Marie Tremblay-Franco Laurent Debrauwer Didier Concordet Rémi Servien 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):109
Introduction
Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.Objectives
In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.Methods
A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.Results
The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.Conclusion
ASICS is a completely automated procedure to identify and quantify metabolites in 1H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.13.
Alexander Pérez-Ruiz Margarida Julià-Sapé Guillem Mercadal Iván Olier Carles Majós Carles Arús 《BMC bioinformatics》2010,11(1):581
Background
Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored. 相似文献14.
Wouter Meuleman Judith YMN Engwegen Marie-Christine W Gast Jos H Beijnen Marcel JT Reinders Lodewyk FA Wessels 《BMC bioinformatics》2008,9(1):88
Background
Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result. 相似文献15.
Background
Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required. 相似文献16.
17.
Background
Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods are suitable for learning from graph-based data such as biological networks, as they only require the abstraction of the similarities between objects into the kernel matrix. One key issue in kernel methods is the selection of a good kernel function. Diffusion kernels, the discretization of the familiar Gaussian kernel of Euclidean space, are commonly used for graph-based data. 相似文献18.
Wenling E Chang Keri Sarver Brandon W Higgs Timothy D Read Nichole ME Nolan Carol E Chapman Kimberly A Bishop-Lilly Shanmuga Sozhamannan 《BMC bioinformatics》2011,12(1):109
Background
OmniLog™ phenotype microarrays (PMs) have the capability to measure and compare the growth responses of biological samples upon exposure to hundreds of growth conditions such as different metabolites and antibiotics over a time course of hours to days. In order to manage the large amount of data produced from the OmniLog™ instrument, PheMaDB (Phenotype Microarray DataBase), a web-based relational database, was designed. PheMaDB enables efficient storage, retrieval and rapid analysis of the OmniLog™ PM data. 相似文献19.
Exploiting structural and topological information to improve prediction of RNA-protein binding sites
Background
RNA-protein interactions are important for a wide range of biological processes. Current computational methods to predict interacting residues in RNA-protein interfaces predominately rely on sequence data. It is, however, known that interface residue propensity is closely correlated with structural properties. In this paper we systematically study information obtained from sequences and structures and compare their contributions in this prediction problem. Particularly, different geometrical and network topological properties of protein structures are evaluated to improve interface residue prediction accuracy. 相似文献20.