Bioinformatics database infrastructure for biotechnology research

Many databases are available that provide valuable data resources for the biotechnological researcher. According to their core data, they can be divided into different types. Some databases provide primary data, like all published nucleotide sequences, others deal with protein sequences. In addition to these two basic types of databases, a huge number of more specialized resources are available, like databases about protein structures, protein identification, special features of genes and/or proteins, or certain organisms. Furthermore, some resources offer integrated views on different types of data, allowing the user to do easy customized queries over large datasets and to compare different types of data.     

蛋白质相互作用数据库

随着“蛋白质组学”的蓬勃发展和人类对生物大分子功能机制的知识积累,涌现出海量的蛋白质相互作用数据。随之,研究者开发了300多个蛋白质相互作用数据库,用于存储、展示和数据的重利用。蛋白质相互作用数据库是系统生物学、分子生物学和临床药物研究的宝贵资源。本文将数据库分为3类：（1）综合蛋白质相互作用数据库;（2）特定物种的蛋白质相互作用数据库;（3）生物学通路数据库。重点介绍常用的蛋白质相互作用数据库,包括BioGRID、STRING、IntAct、MINT、DIP、IMEx、HPRD、Reactome和KEGG等。     

MyDas,an Extensible Java DAS Server

A large number of diverse, complex, and distributed data resources are currently available in the Bioinformatics domain. The pace of discovery and the diversity of information means that centralised reference databases like UniProt and Ensembl cannot integrate all potentially relevant information sources. From a user perspective however, centralised access to all relevant information concerning a specific query is essential. The Distributed Annotation System (DAS) defines a communication protocol to exchange annotations on genomic and protein sequences; this standardisation enables clients to retrieve data from a myriad of sources, thus offering centralised access to end-users.We introduce MyDas, a web server that facilitates the publishing of biological annotations according to the DAS specification. It deals with the common functionality requirements of making data available, while also providing an extension mechanism in order to implement the specifics of data store interaction. MyDas allows the user to define where the required information is located along with its structure, and is then responsible for the communication protocol details.     

Molecular Genetics Information System (MOLGENIS): alternatives in developing local experimental genomics databases

MOTIVATION: Genomic research laboratories need adequate infrastructure to support management of their data production and research workflow. But what makes infrastructure adequate? A lack of appropriate criteria makes any decision on buying or developing a system difficult. Here, we report on the decision process for the case of a molecular genetics group establishing a microarray laboratory. RESULTS: Five typical requirements for experimental genomics database systems were identified: (i) evolution ability to keep up with the fast developing genomics field; (ii) a suitable data model to deal with local diversity; (iii) suitable storage of data files in the system; (iv) easy exchange with other software; and (v) low maintenance costs. The computer scientists and the researchers of the local microarray laboratory considered alternative solutions for these five requirements and chose the following options: (i) use of automatic code generation; (ii) a customized data model based on standards; (iii) storage of datasets as black boxes instead of decomposing them in database tables; (iv) loosely linking to other programs for improved flexibility; and (v) a low-maintenance web-based user interface. Our team evaluated existing microarray databases and then decided to build a new system, Molecular Genetics Information System (MOLGENIS), implemented using code generation in a period of three months. This case can provide valuable insights and lessons to both software developers and a user community embarking on large-scale genomic projects. AVAILABILITY: http://www.molgenis.nl     

Reactome pathway analysis to enrich biological discovery in proteomics data sets

Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

MBIS--an integrated system for the retrieval and analyses of sequence data from nucleic acids and proteins.

A computer-based system termed MBIS (the Molecular Biological Information Service), written in FORTRAN77 and Digital Command Language (DCL) and running on a Digital Equipment Corporation VAX computer under the VMS operating system (V4.1) is in use at the Division of Molecular Biology. MBIS consists of three main sections: 1) The utility section, used by the system's manager to tailor the five commonly available databases so that they are useable by the applications programmes running on the system; 2) The retrieval section, used to find and extract specific sequences or bibliographic information, and 3) The analytical section, used to analyse and compare sequences either extracted from the databases or input by the user. The nucleotide databases maintained are GenBank, EMBL and PIR (Protein Identification Resource, National Biomedical Research Foundation) and the peptide databases are PIR and NEWAT. In addition, users can originate and maintain their own databases. Those programmes which feature graphics output are compatible with most emulators of the Tektronix 4010 terminal.     

SciDBMaker: new software for computer-aided design of specialized biological databases

Background  

The exponential growth of research in molecular biology has brought concomitant proliferation of databases for stocking its findings. A variety of protein sequence databases exist. While all of these strive for completeness, the range of user interests is often beyond their scope. Large databases covering a broad range of domains tend to offer less detailed information than smaller, more specialized resources, often creating a need to combine data from many sources in order to obtain a complete picture. Scientific researchers are continually developing new specific databases to enhance their understanding of biological processes.     

Integrated web visualizations for protein-protein interaction databases

Background

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.

Results

We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015.

Conclusions

Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.     

Évaluation des thérapeutiques ciblées en tomographie par émission de positons

Thanks to breakthroughs in drug design, new kinds of treatment in oncology have been developed. These new molecules target usually a precise molecular pathway proved to be involved in the development of a malignant disease. This led to the concept of targeted therapy. Therefore, the accurate selection of patients who may experience a clinical benefit of such treatments and the way to assess the response are still challenging issues. Molecular imaging with radiolabeled compounds seemed to be a very promising tool, as for example PET with ¹⁸F FluoroDeoxyGlucose (FDG), which allows to assess and to predict the response to a tyrosine kinase inhibitors more efficiently than conventional imaging tools. FDG is only a surrogate marker of cell proliferation. The common tools (clinical and radiological assessment) are no longer sufficient to predict the clinical efficacy of these new drugs. Molecular imaging should be added in the design of clinical trials in order to detect earlier pharmacodynamic effects, to select responding patients and to provide proofs of efficacy of these non-cytotoxic compounds. Molecular imaging databases have to be created and cross-matched to tumor sample collections, providing consequently new “dynamic” pathological resources.     

Path PPI: an integrated dataset of human pathways and protein-protein interactions

Integration of pathway and protein-protein interaction(PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, Bio Carta, Reactome, Net Path, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, Int Act, Bio GRID, MINT and DIP, to develop integrated dataset(named Path PPI). More detailed interaction type and modification information on protein interactions can be preserved in Path PPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values(OAB) among these pathway databases were less than 5%, and these databases must be used conjunctively. The Path PPI data was provided at http://proteomeview. hupo.org.cn/Path PPI/Path PPI.html.     

Navigating public microarray databases

With the ever-escalating amount of data being produced by genome-wide microarray studies, it is of increasing importance that these data are captured in public databases so that researchers can use this information to complement and enhance their own studies. Many groups have set up databases of expression data, ranging from large repositories, which are designed to comprehensively capture all published data, through to more specialized databases. The public repositories, such as ArrayExpress at the European Bioinformatics Institute contain complete datasets in raw format in addition to processed data, whilst the specialist databases tend to provide downstream analysis of normalized data from more focused studies and data sources. Here we provide a guide to the use of these public microarray resources.     

Teaching the Fundamentals of Biological Data Integration Using Classroom Games

This article aims to introduce the nature of data integration to life scientists. Generally, the subject of data integration is not discussed outside the field of computational science and is not covered in any detail, or even neglected, when teaching/training trainees. End users (hereby defined as wet-lab trainees, clinicians, lab researchers) will mostly interact with bioinformatics resources and tools through web interfaces that mask the user from the data integration processes. However, the lack of formal training or acquaintance with even simple database concepts and terminology often results in a real obstacle to the full comprehension of the resources and tools the end users wish to access. Understanding how data integration works is fundamental to empowering trainees to see the limitations as well as the possibilities when exploring, retrieving, and analysing biological data from databases. Here we introduce a game-based learning activity for training/teaching the topic of data integration that trainers/educators can adopt and adapt for their classroom. In particular we provide an example using DAS (Distributed Annotation Systems) as a method for data integration.     

Gene2EST: a BLAST2 server for searching expressed sequence tag (EST) databases with eukaryotic gene-sized queries

Expressed sequence tags (ESTs) are randomly sequenced cDNA clones. Currently, nearly 3 million human and 2 million mouse ESTs provide valuable resources that enable researchers to investigate the products of gene expression. The EST databases have proven to be useful tools for detecting homologous genes, for exon mapping, revealing differential splicing, etc. With the increasing availability of large amounts of poorly characterised eukaryotic (notably human) genomic sequence, ESTs have now become a vital tool for gene identification, sometimes yielding the only unambiguous evidence for the existence of a gene expression product. However, BLAST-based Web servers available to the general user have not kept pace with these developments and do not provide appropriate tools for querying EST databases with large highly spliced genes, often spanning 50 000-100 000 bases or more. Here we describe Gene2EST (http://woody.embl-heidelberg.de/gene2est/), a server that brings together a set of tools enabling efficient retrieval of ESTs matching large DNA queries and their subsequent analysis. RepeatMasker is used to mask dispersed repetitive sequences (such as Alu elements) in the query, BLAST2 for searching EST databases and Artemis for graphical display of the findings. Gene2EST combines these components into a Web resource targeted at the researcher who wishes to study one or a few genes to a high level of detail.     

Farm animal genome databases

The requirements for bioinformatics resources to support genome research in farm animals is reviewed.The resources developed to meet these needs are described. Resource databases and associated tools have been developed to handle experimental data. Several of these systems serve the needs of multinational collaborations. Genome databases have been established to provide contemporary summaries of the status of genome maps in a range of farm and domestic animals along with experimental details and citations. New resources and tools will be required to address the informatics needs of emerging technologies such as microarrays. However, continued investment is also required to maintain the currency and utility of the current systems, especially the genome databases.