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At least 1,297 records · Page 72Linked to original sources
PubMed2004

pdb-care (PDB carbohydrate residue check): a program to support annotation of complex carbohydrate structures in PDB files.

BACKGROUND: Carbohydrates are involved in a variety of fundamental biological processes and pathological situations. They therefore have a large pharmaceutical and diagnostic potential. Knowledge of the 3D structure of glycans is a prerequisite for a complete understanding of their biological functions. The largest source of biomolecular 3D structures is the Protein Data Bank. However, about 30% of all 1663 PDB entries (version September 2003) containing carbohydrates comprise errors in glycan description. Unfortunately, no software is currently available which aligns the 3D information with the reported assignments. It is the aim of this work to fill this gap. RESULTS: The pdb-care program http://www.glycosciences.de/tools/pdb-care/ is able to identify and assign carbohydrate structures using only atom types and their 3D atom coordinates given in PDB-files. Looking up a translation table where systematic names and the respective PDB residue codes are listed, both assignments are compared and inconsistencies are reported. Additionally, the reliability of reported and calculated connectivities for molecules listed within the HETATOM records is checked and unusual values are reported. CONCLUSION: Frequent use of pdb-care will help to improve the quality of carbohydrate data contained in the PDB. Automatic assignment of carbohydrate structures contained in PDB entries will enable the cross-linking of glycobiology resources with genomic and proteomic data collections.

Carbohydrate Conformation↗
PubMed2005

Text detective: a rule-based system for gene annotation in biomedical texts.

BACKGROUND: The identification of mentions of gene or gene products in biomedical texts is a critical step in the development of text mining applications in biosciences. The complexity and ambiguity of gene nomenclature makes this a very difficult task. METHODS: Here we present a novel approach based on a combination of carefully designed rules and several lexicons of biological concepts, implemented in the Text Detective system. Text Detective is able to normalize the results of gene mentions found by offering the appropriate database reference. RESULTS: In BioCreAtIvE evaluation, Text Detective achieved results of 84% precision, 71% recall for task 1A, and 79% precision, 71% recall for mouse genes in task 1B.

Biomedical Research↗
PubMed2005

Automatically annotating documents with normalized gene lists.

BACKGROUND: Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information extraction and database curation systems. Here we present two separate solutions to this problem. The first is primarily based on standard pattern matching and information extraction techniques. The second and more novel solution uses a statistical classifier to recognize valid gene matches from a list of known gene synonyms. RESULTS: We compare the results of the two systems, analyze their merits and argue that the classification based system is preferable for many reasons including performance, simplicity and robustness. Our best systems attain a balanced precision and recall in the range of 74%-92%, depending on the organism.

Animals↗
PubMed2006

Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm.

BACKGROUND: Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept. RESULTS: High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae. CONCLUSION: More than 1.6 million molecular formulae in the range 0-500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.

Algorithms↗
PubMed2006

Array2BIO: from microarray expression data to functional annotation of co-regulated genes.

BACKGROUND: There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility. RESULTS: Array2BIO converts raw intensities into probe expression values, automatically maps those to genes, and subsequently identifies groups of co-expressed genes using two complementary approaches: (1) comparative analysis of signal versus control and (2) clustering analysis of gene expression across different conditions. The identified genes are assigned to functional categories based on Gene Ontology classification and KEGG protein interaction pathways. Array2BIO reliably handles low-expressor genes and provides a set of statistical methods for quantifying expression levels, including Benjamini-Hochberg and Bonferroni multiple testing corrections. An automated interface with the ECR Browser provides evolutionary conservation analysis for the identified gene loci while the interconnection with Crème allows prediction of gene regulatory elements that underlie observed expression patterns. CONCLUSION: We have developed Array2BIO - a web based tool for rapid comprehensive analysis of Affymetrix microarray expression data, which also allows users to link expression data to Dcode.org comparative genomics tools and integrates a system for translating co-expression data into mechanisms of gene co-regulation. Array2BIO is publicly available at http://array2bio.dcode.org.

Algorithms↗
PubMed2005

Cross genome phylogenetic analysis of human and Drosophila G protein-coupled receptors: application to functional annotation of orphan receptors.

BACKGROUND: The cell-membrane G-protein coupled receptors (GPCRs) are one of the largest known superfamilies and are the main focus of intense pharmaceutical research due to their key role in cell physiology and disease. A large number of putative GPCRs are 'orphans' with no identified natural ligands. The first step in understanding the function of orphan GPCRs is to identify their ligands. Phylogenetic clustering methods were used to elucidate the chemical nature of receptor ligands, which led to the identification of natural ligands for many orphan receptors. We have clustered human and Drosophila receptors with known ligands and orphans through cross genome phylogenetic analysis and hypothesized higher relationship of co-clustered members that would ease ligand identification, as related receptors share ligands with similar structure or class. RESULTS: Cross-genome phylogenetic analyses were performed to identify eight major groups of GPCRs dividing them into 32 clusters of 371 human and 113 Drosophila proteins (excluding olfactory, taste and gustatory receptors) and reveal unexpected levels of evolutionary conservation across human and Drosophila GPCRs. We also observe that members of human chemokine receptors, involved in immune response, and most of nucleotide-lipid receptors (except opsins) do not have counterparts in Drosophila. Similarly, a group of Drosophila GPCRs (methuselah receptors), associated in aging, is not present in humans. CONCLUSION: Our analysis suggests ligand class association to 52 unknown Drosophila receptors and 95 unknown human GPCRs. A higher level of phylogenetic organization was revealed in which clusters with common domain architecture or cellular localization or ligand structure or chemistry or a shared function are evident across human and Drosophila genomes. Such analyses will prove valuable for identifying the natural ligands of Drosophila and human orphan receptors that can lead to a better understanding of physiological and pathological roles of these receptors.

Amino Acid Sequence↗