BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

Background  

Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use.     

AmpliBASE MT: a Mycobacterium tuberculosis diversity knowledgebase

AmpliBASE MT is an online databank of high-resolution DNA fingerprints representing fluorescent amplified fragment length polymorphism (FAFLP) profiles or amplitypes developed for the Mycobacterium tuberculosis complex strains from 48 different countries. AmpliBASE MT is based on a relational database management system that is hyperlinked to visualize genotyping results in the form of DNA fingerprint images for individual strains. A flexible search system based on systematic comparisons of fragment sizes in base pairs allows inter-laboratory comparison of FAFLP profiles. Besides this, the database also displays previously published data on IS6110 profiles, spoligotypes, MIRU-VNTRs and large sequence polymorphisms along with the FAFLP records that will give the overall comparisons. Being the first of its kind, AmpliBASE MT is expected to be a very helpful tool in strengthening the concept of 'geographic genomics' and will be very helpful to molecular epidemiologists and those interested in diagnostic development for tuberculosis.     

Analysis and comparison of metabolic pathway databases

Enormous amounts of data result from genome sequencing projects and new experimental methods. Within this tremendous amount of genomic data 30-40 per cent of the genes being identified in an organism remain unknown in terms of their biological function. As a consequence of this lack of information the overall schema of all the biological functions occurring in a specific organism cannot be properly represented. To understand the functional properties of the genomic data more experimental data must be collected. A pathway database is an effort to handle the current knowledge of biochemical pathways and in addition can be used for interpretation of sequence data. Some of the existing pathway databases can be interpreted as detailed functional annotations of genomes because they are tightly integrated with genomic information. However, experimental data are often lacking in these databases. This paper summarises a list of pathway databases and some of their corresponding biological databases, and also focuses on information about the content and the structure of these databases, the organisation of the data and the reliability of stored information from a biological point of view. Moreover, information about the representation of the pathway data and tools to work with the data are given. Advantages and disadvantages of the analysed databases are pointed out, and an overview to biological scientists on how to use these pathway databases is given.     

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

Background  

Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.     

Bioinformatics models in drug abuse and Neuro-AIDS: Using and developing databases

The magnitude of the problems of drug abuse and Neuro-AIDS warrants the development of novel approaches for testing hypotheses in diagnosis and treatment ranging from cell culture models to developing databases. In this study, cultured neurons were treated with/without HIV-TAT, ENV, or cocaine in a 2x2x2 expression study design. RNA was purified, labeled, and expression data were produced and analyzed using ANOVA. Thus, we identified 35 genes that were significantly expressed across treatment conditions. A diagram is presented showing examples of molecular relationships involving a significantly expressed gene in the current study (SOX2). Also, we use this information to discuss examples of gene expression interactions as a means to portray significance and complexity of gene expression studies in Drug Abuse and Neuro-AIDS. Furthermore, we discuss here that critical interactions remain undetected, which may be unravelled by developing robust database systems containing large datasets and gleaned information from collaborating scientists . Hence, we are developing a public domain database we named The Agora database , that will served as a shared infrastructure to query, deposit, and review information related to drug abuse and dementias including Neuro-AIDS. A workflow of this database is also outlined in this paper.     

PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes

In recent years, microRNAs have been shown to play important roles in physiological as well as malignant processes. The PhenomiR database provides data from 542 studies that investigate deregulation of microRNA expression in diseases and biological processes as a systematic, manually curated resource. Using the PhenomiR dataset, we could demonstrate that, depending on disease type, independent information from cell culture studies contrasts with conclusions drawn from patient studies.     

Alterations of metabolic genes and metabolites in cancer

Altered metabolic regulation has long been observed in human cancer and broadly used in the clinic for tumor detection. Two recent findings--the direct regulation of metabolic enzymes by frequently mutated cancer genes and frequent mutations of several metabolic enzymes themselves in cancer--have renewed interest in cancer metabolism. Supporting a causative role of altered metabolic enzymes in tumorigenesis, abnormal levels of several metabolites have been found to play a direct role in cancer development. The alteration of metabolic genes and metabolites offer not only new biomarkers for diagnosis and prognosis, but also potential new targets for cancer therapy.     

Linear models of simple cells: correspondence to real cell responses and space spanning properties

Despite their limitations, linear filter models continue to be used to simulate the receptive field properties of cortical simple cells. For theoreticians interested in large scale models of visual cortex, a family of self-similar filters represents a convenient way in which to characterise simple cells in one basic model. This paper reviews research on the suitability of such models, and goes on to advance biologically motivated reasons for adopting a particular group of models in preference to all others. In particular, the paper describes why the Gabor model, so often used in network simulations, should be dropped in favour of a Cauchy model, both on the grounds of frequency response and mutual filter orthogonality.     

代谢途径数据库简介

复杂的代谢途径分支比线性的基因、基因组和蛋白质序列更难以表示和搜索,介绍了INTERNET上常见的代谢途径数据库,并对其站点、内容和功能以及使用技巧作了简要的阐述。     

Validation of metabolic pathway databases based on chemical substructure search

Metabolic pathway databases such as KEGG contain information on thousands of biochemical reactions drawn from the biomedical literature. Ensuring consistency of such large metabolic pathways is essential to their proper use. In this paper, we present a new method to determine consistency of an important class of biochemical reactions. Our method exploits the knowledge of the atomic rearrangement pattern in biochemical reactions, to reduce the automatic atom mapping problem to a series of chemical substructure searches between the substrate and the product of a biochemical reaction. As an illustrative application, we describe the exhaustive validation of a substantial portion from the latest release of the KEGG LIGAND database.     

Aluminum, a neurotoxin which affects diverse metabolic reactions

Experimental evidence is summarized to support the hypothesis that chronic exposure to low levels of aluminum may lead to neurological disorders. These disorders result from defective phosphorylation--dephosphorylation reactions, reduced glucose utilization and site-specific damage inflicted by free radicals produced by altered iron metabolism. The brain is a highly compartmentalized organ. Therefore, a co-localization of critical mass of metabolic errors rather than a single event may be essential to precipitate a neural disease. Aluminum appears to participate in formulating this critical mass. Patients with dialysis dementia get partial relief by desferroxamine which chelates aluminum. However, it also chelates iron and therefore limits its applicability. While the specific chelator for aluminum is yet to be made available, exercising a caution in aluminum intake appears prudent.     

MEMOSys: Bioinformatics platform for genome-scale metabolic models

Background  

Recent advances in genomic sequencing have enabled the use of genome sequencing in standard biological and biotechnological research projects. The challenge is how to integrate the large amount of data in order to gain novel biological insights. One way to leverage sequence data is to use genome-scale metabolic models. We have therefore designed and implemented a bioinformatics platform which supports the development of such metabolic models.     

Biomine: predicting links between biological entities using network models of heterogeneous databases

ABSTRACT: BACKGROUND: Biological databases contain large amounts of data concerning the functions and associationsof genes and proteins. Integration of data from several such databases into a single repositorycan aid the discovery of previously unknown connections spanning multiple types ofrelationships and databases. RESULTS: Biomine is a system that integrates cross-references from several biological databases into agraph model with multiple types of edges, such as protein interactions, gene-diseaseassociations and gene ontology annotations. Edges are weighted based on their type,reliability, and informativeness. We present Biomine and evaluate its performance in linkprediction, where the goal is to predict pairs of nodes that will be connected in the future,based on current data. In particular, we formulate protein interaction prediction and diseasegene prioritization tasks as instances of link prediction. The predictions are based on aproximity measure computed on the integrated graph. We consider and experiment withseveral such measures, and perform a parameter optimization procedure where different edgetypes are weighted to optimize link prediction accuracy. We also propose a novel method fordisease-gene prioritization, defined as finding a subset of candidate genes that cluster togetherin the graph. We experimentally evaluate Biomine by predicting future annotations in thesource databases and prioritizing lists of putative disease genes. CONCLUSIONS: The experimental results show that Biomine has strong potential for predicting links when aset of selected candidate links is available. The predictions obtained using the entire Biominedataset are shown to clearly outperform ones obtained using any single source of data alone,when different types of links are suitably weighted. In the gene prioritization task, anestablished reference set of disease-associated genes is useful, but the results show that underfavorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and8.1 million relations between them, with focus on human genetics. Some of its functionalitiesare available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching forand visualizing connections between given biological entities.