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At least 1,639 records · Page 91Linked to original sources
PubMed2006

Functional annotation of the Arabidopsis P450 superfamily based on large-scale co-expression analysis.

Cytochrome P450 mono-oxygenases play prominent roles in a diverse set of metabolic pathways, but the function of most of these enzymes remains obscure. A bottleneck in the functional genomics of this superfamily constitutes hypothesis generation to identify potential substrates (or substrate classes) individual P450s may act on. We used publicly available large-scale expression data to perform co-expression analysis comparing the expression matrix of each P450 with those from more than 4000 selected genes across thousands of microarrays. Based on functional annotations of co-expressed genes from a diverse set of databases, co-expressed pathways were thus identified for each P450. Using this approach, most P450s with known functions were placed into their respective pathways, thereby proofing the concept. As examples, pathway mapping results identifying novel P450s potentially acting on flower-specific monoterpenes and root-specific triterpenes are described. Co-expression results for all Arabidopsis P450s will be presented as a web resource on the 'CYPedia' web pages (http://ibmp.u-strasbg.fr/CYPedia/).

Arabidopsis↗
PubMed2006

Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data.

UNLABELLED: Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments. The software was applied to recent genome-wide RNAz surveys of mammals, urochordates, and nematodes. AVAILABILITY: The program RNAmicro is available as source code and can be downloaded from http://www.bioinf.uni-leipzig/Software/RNAmicro.

Algorithms↗
PubMed2025

A Comprehensive Bioinformatics Approach to Analysis of Variants: Variant Calling, Annotation, and Prioritization.

Next-Generation Sequencing (NGS), also known as high-throughput sequencing technologies, has enabled rapid and efficient sequencing of large amounts of DNA and RNA. These technologies have revolutionized the field of genomics, transcriptomics, and proteomics and have been widely used in cancer research, leading to advances in clinical diagnosis and treatment. Improvements in the NGS technologies enabled millions of fragments to be sequenced simultaneously in a time- and cost-effective manner and resulted in large amount of genomic data which require efficient analysis methods. Analysis of the genomic data requires both efficient computer resources and bioinformatics approaches. This chapter details a comprehensive computational approach and analysis steps for genomic data analysis.

Computational Biology↗
PubMed2006

Identification of functional modules in a PPI network by clique percolation clustering.

Large-scale experiments and data integration have provided the opportunity to systematically analyze and comprehensively understand the topology of biological networks and biochemical processes in cells. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we apply a graph clustering algorithm based on clique percolation clustering to detect overlapping network modules of a protein-protein interaction (PPI) network. Our analysis of the yeast Sacchromyces cerevisiae suggests that most of the detected modules correspond to one or more experimentally functional modules and half of these annotated modules match well with experimentally determined protein complexes. Our method of analysis can of course be applied to protein-protein interaction data for any species and even other biological networks.

Algorithms↗
PubMed2005

Identification and distribution of protein families in 120 completed genomes using Gene3D.

Using a new protocol, PFscape, we undertake a systematic identification of protein families and domain architectures in 120 complete genomes. PFscape clusters sequences into protein families using a Markov clustering algorithm (Enright et al., Nucleic Acids Res 2002;30:1575-1584) followed by complete linkage clustering according to sequence identity. Within each protein family, domains are recognized using a library of hidden Markov models comprising CATH structural and Pfam functional domains. Domain architectures are then determined using DomainFinder (Pearl et al., Protein Sci 2002;11:233-244) and the protein family and domain architecture data are amalgamated in the Gene3D database (Buchan et al., Genome Res 2002;12:503-514). Using Gene3D, we have investigated protein sequence space, the extent of structural annotation, and the distribution of different domain architectures in completed genomes from all kingdoms of life. As with earlier studies by other researchers, the distribution of domain families shows power-law behavior such that the largest 2,000 domain families can be mapped to approximately 70% of nonsingleton genome sequences; the remaining sequences are assigned to much smaller families. While approximately 50% of domain annotations within a genome are assigned to 219 universal domain families, a much smaller proportion (< 10%) of protein sequences are assigned to universal protein families. This supports the mosaic theory of evolution whereby domain duplication followed by domain shuffling gives rise to novel domain architectures that can expand the protein functional repertoire of an organism. Functional data (e.g. COG/KEGG/GO) integrated within Gene3D result in a comprehensive resource that is currently being used in structure genomics initiatives and can be accessed via http://www.biochem.ucl.ac.uk/bsm/cath/Gene3D/.

Amino Acid Sequence↗
PubMed2006

High performance GRID based implementation for genomics and protein analysis.

Starting from the genomic and proteomic sequence data, a complex computational infrastructure as been established with the objective to develop a GRID based system to to automate the analysis, prediction and annotation processes of genomic DNA. To support of this type of analysis, several algorithms as been used to recognize biological signals involved in the identification of genes and proteins. The system implemented can be use to analyse the content of the large number of genomic sequences. For this reason, the system realized is capable of using a computational architecture specifically designed for intensive computing based on GRID technologies developed throughout the BIOINFOGRID European project. We developed a GRID based workflow to correlate different kind of Bioinformatics data, going from the Genomics Nucleotide to the Protein Sequence. The first step in the workflow consists of submitting a nucleotide sequence that is elaborated by a specific software for gene prediction. In particular this tool performs a search in the nucleotide sequence to find out the key components of gene. The predicted gene is then translated in the corresponding protein sequence. Based on protein sequence is then possible to identify the domains that characterize the protein functionality using specific tools of domain prediction. Protein domains classification are very important in the analysis of the macromolecular functionality. To analyze a whole protein family from large genome of various organism means to elaborate a large amount of data that requires huge computational resources. To analyze all this data we suggest the use of a high performance platform based on grid technology. We have implemented our applications on a wide area grid platform for scientific applications [http://www.grid.it and http://grid-it.cnaf.infn.it] composed of about 1000 CPU's. The grid infrastructure consists in a collection of computing elements and storage elements that jointly concur to define a platform for high performance elaboration. In this study a grid based application is presented to compute the protein domain analysis in a distributed way. This approach has high performance because the protein domains are checked with different software in parallel in different grid sites.

Computational Biology↗
PubMed2005

Comparative linkage analysis and visualization of high-density oligonucleotide SNP array data.

BACKGROUND: The identification of disease-associated genes using single nucleotide polymorphisms (SNPs) has been increasingly reported. In particular, the Affymetrix Mapping 10 K SNP microarray platform uses one PCR primer to amplify the DNA samples and determine the genotype of more than 10,000 SNPs in the human genome. This provides the opportunity for large scale, rapid and cost-effective genotyping assays for linkage analysis. However, the analysis of such datasets is nontrivial because of the large number of markers, and visualizing the linkage scores in the context of genome maps remains less automated using the current linkage analysis software packages. For example, the haplotyping results are commonly represented in the text format. RESULTS: Here we report the development of a novel software tool called CompareLinkage for automated formatting of the Affymetrix Mapping 10 K genotype data into the "Linkage" format and the subsequent analysis with multi-point linkage software programs such as Merlin and Allegro. The new software has the ability to visualize the results for all these programs in dChip in the context of genome annotations and cytoband information. In addition we implemented a variant of the Lander-Green algorithm in the dChipLinkage module of dChip software (V1.3) to perform parametric linkage analysis and haplotyping of SNP array data. These functions are integrated with the existing modules of dChip to visualize SNP genotype data together with LOD score curves. We have analyzed three families with recessive and dominant diseases using the new software programs and the comparison results are presented and discussed. CONCLUSIONS: The CompareLinkage and dChipLinkage software packages are freely available. They provide the visualization tools for high-density oligonucleotide SNP array data, as well as the automated functions for formatting SNP array data for the linkage analysis programs Merlin and Allegro and calling these programs for linkage analysis. The results can be visualized in dChip in the context of genes and cytobands. In addition, a variant of the Lander-Green algorithm is provided that allows parametric linkage analysis and haplotyping.

Family Health↗
PubMed2004

New challenges in gene expression data analysis and the extended GEPAS.

Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.bioinfo.cnio.es.

Gene Expression Profiling↗
PubMed2006

Extracting gene networks for low-dose radiation using graph theoretical algorithms.

Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from the graph correspond to sets of genes for which significant edges are present between all members of the set, representing potential members of common or interacting pathways. Clique membership can be used to infer function about poorly annotated genes, based on the known functions of better-annotated genes with which they share clique membership (i.e., "guilt-by-association"). We illustrate our method by applying it to microarray data collected from the spleens of mice exposed to low-dose ionizing radiation. Differential analysis is used to identify sets of genes whose interactions are impacted by radiation exposure. The correlation graph is also queried independently of clique to extract edges that are impacted by radiation. We present several examples of multiple gene interactions that are altered by radiation exposure and thus represent potential molecular pathways that mediate the radiation response.

Algorithms↗
PubMed2025

CaXML: Chemistry-informed machine learning explains mutual changes between protein conformations and calcium ions in calcium-binding proteins using structural and topological features.

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of CaXML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

Machine Learning↗
PubMed2006

ASPIC: a web resource for alternative splicing prediction and transcript isoforms characterization.

Alternative splicing (AS) is now emerging as a major mechanism contributing to the expansion of the transcriptome and proteome complexity of multicellular organisms. The fact that a single gene locus may give rise to multiple mRNAs and protein isoforms, showing both major and subtle structural variations, is an exceptionally versatile tool in the optimization of the coding capacity of the eukaryotic genome. The huge and continuously increasing number of genome and transcript sequences provides an essential information source for the computational detection of genes AS pattern. However, much of this information is not optimally or comprehensively used in gene annotation by current genome annotation pipelines. We present here a web resource implementing the ASPIC algorithm which we developed previously for the investigation of AS of user submitted genes, based on comparative analysis of available transcript and genome data from a variety of species. The ASPIC web resource provides graphical and tabular views of the splicing patterns of all full-length mRNA isoforms compatible with the detected splice sites of genes under investigation as well as relevant structural and functional annotation. The ASPIC web resource-available at http://www.caspur.it/ASPIC/--is dynamically interconnected with the Ensembl and Unigene databases and also implements an upload facility.

Algorithms↗
PubMed2006

Proteomic analysis of the mouse mammary gland is a powerful tool to identify novel proteins that are differentially expressed during mammary development.

After lactation, the mouse mammary gland undergoes apoptosis and tissue remodelling as the gland reverts to its prepregnant state. This complex change was investigated using 2-DE. An integrated database was produced from lactation and involution proteomes. Forty-four molecular cluster indexes (MCIs) that showed altered expression from lactation to involution were selected for MS analysis. Of these, 32 gave protein annotations, 18 of which were unequivocal proteins. Selected proteins were then studied across all of development, including pregnancy, using data integrated from another proteome database. Two proteins, the RNA polymerase B transcription factor 3 (BTF3) and the minichromosome maintenance protein 3 (MCM3), although initially selected on the basis of the lactation/involution criteria, had expression profiles that indicated an additional role in mammary development and were further analysed. BTF3, a transcription factor previously not described in the mammary gland, was up-regulated strongly in pregnancy, indicating an involvement in alveolar growth. MCM3's expression was greatest in pregnancy and late involution, decreasing through lactation. Immunohistochemistry localised MCM3 to the mammary epithelium, where a greater proportion of cells stained than for the proliferation marker Ki67. MCM3 expression during lactation may identify cells that are licensed to repopulate the gland during cell loss in lactation and following involution.

Animals↗
PubMed1993

Deciding to terminate treatment: a practical guide for physicians.

Providing house officers and practicing physicians with annotated, concise, practical guidelines for decisions to terminate treatment is the objective of this report. The study selection and data extraction focused on statutes, regulations, court decisions, medicolegal analyses, clinical studies, and position papers addressing termination-of-treatment issues. To foster a systematic approach, we developed a laminated, pocket-sized card containing a series of questions to be asked by any physician confronted with termination-of-treatment decisions. Systematic identification and deliberate assessment of (1) brain death; (2) the nature, extent, cause, prognosis, and reversibility of impairment; (3) the type of treatment to be withheld or withdrawn; (4) the futility of any proposed intervention; (5) the capacity of the patient for health care decision-making; (6) the evidence of patient's wishes; (7) the proper roles of family members, surrogate decision makers, and other health professionals (eg, ethics committees); and (8) applicable policies, ethics, laws, and potential conflicts of interest will enhance efficiency and add value to the decision-making process at the end of life.

Brain Death↗
PubMed2004

Data mining the protein data bank: automatic detection and assignment of carbohydrate structures.

Knowledge of the 3D structure of glycans is a prerequisite for a complete understanding of the biological processes glycoproteins are involved in. However, due to a lack of standardised nomenclature, carbohydrate compounds are difficult to locate within the Protein Data Bank (PDB). Using an algorithm that detects carbohydrate structures only requiring element types and atom coordinates, we were able to detect 1663 entries containing a total of 5647 carbohydrate chains. The majority of chains are found to be N-glycosidically bound. Noncovalently bound ligands are also frequent, while O-glycans form a minority. About 30% of all carbohydrate containing PDB entries comprise one or several errors. The automatic assignment of carbohydrate structures in PDB entries will improve the cross-linking of glycobiology resources with genomic and proteomic data collections, which will be an important issue of the upcoming glycomics projects. By aiding in detection of erroneous annotations and structures, the algorithm might also help to increase database quality.

Algorithms↗
PubMed2003

New trends in bioinformatics: from genome sequence to personalized medicine.

Molecular medicine requires the integration and analysis of genomic, molecular, cellular, as well as clinical data and it thus offers a remarkable set of challenges to bioinformatics. Bioinformatics nowadays has an essential role both, in deciphering genomic, transcriptomic, and proteomic data generated by high-throughput experimental technologies, and in organizing information gathered from traditional biology and medicine. The evolution of bioinformatics, which started with sequence analysis and has led to high-throughput whole genome or transcriptome annotation today, is now going to be directed towards recently emerging areas of integrative and translational genomics, and ultimately personalized medicine.Therefore considerable efforts are required to provide the necessary infrastructure for high-performance computing, sophisticated algorithms, advanced data management capabilities, and-most importantly-well trained and educated personnel to design, maintain and use these environments. This review outlines the most promising trends in bioinformatics, which may play a major role in the pursuit of future biological discoveries and medical applications.

Computational Biology↗
PubMed2000

Identification and biochemical characterization of an Arabidopsis indole-3-acetic acid glucosyltransferase.

Biochemical characterization of recombinant gene products following a phylogenetic analysis of the UDP-glucosyltransferase (UGT) multigene family of Arabidopsis has identified one enzyme (UGT84B1) with high activity toward the plant hormone indole-3-acetic acid (IAA) and three related enzymes (UGT84B2, UGT75B1, and UGT75B2) with trace activities. The identity of the IAA conjugate has been confirmed to be 1-O-indole acetyl glucose ester. A sequence annotated as a UDP-glucose:IAA glucosyltransferase (IAA-UGT) in the Arabidopsis genome and expressed sequence tag data bases given its similarity to the maize iaglu gene sequence showed no activity toward IAA. This study describes the first biochemical analysis of a recombinant IAA-UGT and provides the foundation for future genetic approaches to understand the role of 1-O-indole acetyl glucose ester in Arabidopsis.

Arabidopsis↗
PubMed2004

From masking repeats to identifying functional repeats in the mouse transcriptome.

The back-to-back release of the mouse genome and the functionally annotated RIKEN mouse full-length cDNA collection was an important milestone in mammalian genomics. Yet much of the data remain to be explored in terms of biological effects and mechanisms. For example, interspersed repeats account for 39 per cent of the mouse genome sequence and 11 per cent of representative transcripts. A considerable number of transposable repeat elements are still active and propagating in mouse compared with human. While existing repeat databases and tools assist the classification of repeats or identification of new repeats, there is little bioinformatic support towards exploring the extent and role of repeats in transcriptional variation, modulation of protein function, or gene regulatory events. Since the mouse is used as a model organism to study human genes and their disease associations, this review focuses on information extraction and collation that captures the functional context of repeats in mouse transcripts to facilitate the biological interpretation and extrapolation of findings to the human.

Animals↗
PubMed2002

JenPep: a database of quantitative functional peptide data for immunology.

MOTIVATION: The compilation of quantitative binding data underlies attempts to derive tools for the accurate prediction of epitopes in cellular immunology and is part of our concerted goal to develop practical computational vaccinology. RESULTS: JenPep is a family of relational databases supporting the growing community of immunoinformaticians. It contains quantitative data on peptide binding to Major Histocompatibility Complexes (MHCs) and to Transmembrane Peptide Transporter (TAP), as well as an annotated list of T-cell epitopes. AVAILABILITY: The database is available via the Internet. An HTML interface allowing searching of the database can be found at the following address: http://www.jenner.ac.uk/JenPep.

ATP-Binding Cassette Transporters↗