Genome Warehouse: A Public Repository Housing Genome-scale Data

The Genome Warehouse (GWH) is a public repository housing genome assembly data for a wide range of species and delivering a series of web services for genome data submission, storage, release, and sharing. As one of the core resources in the National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB; https://ngdc.cncb.ac.cn), GWH accepts both full and partial (chloroplast, mitochondrion, and plasmid) genome sequences with different assembly levels, as well as an update of existing genome assemblies. For each assembly, GWH collects detailed genome-related metadata of biological project, biological sample, and genome assembly, in addition to genome sequence and annotation. To archive high-quality genome sequences and annotations, GWH is equipped with a uniform and standardized procedure for quality control. Besides basic browse and search functionalities, all released genome sequences and annotations can be visualized with JBrowse. By May 21, 2021, GWH has received 19,124 direct submissions covering a diversity of 1108 species and has released 8772 of them. Collectively, GWH serves as an important resource for genome-scale data management and provides free and publicly accessible data to support research activities throughout the world. GWH is publicly accessible at https://ngdc.cncb.ac.cn/gwh.     

The Genome Sequence DataBase: towards an integrated functional genomics resource.

During 1998 the primary focus of the Genome Sequence DataBase (GSDB; http://www.ncgr.org/gsdb ) located at the National Center for Genome Resources (NCGR) has been to improve data quality, improve data collections, and provide new methods and tools to access and analyze data. Data quality has been improved by extensive curation of certain data fields necessary for maintaining data collections and for using certain tools. Data quality has also been increased by improvements to the suite of programs that import data from the International Nucleotide Sequence Database Collaboration (IC). The Sequence Tag Alignment and Consensus Knowledgebase (STACK), a database of human expressed gene sequences developed by the South African National Bioinformatics Institute (SANBI), became available within the last year, allowing public access to this valuable resource of expressed sequences. Data access was improved by the addition of the Sequence Viewer, a platform-independent graphical viewer for GSDB sequence data. This tool has also been integrated with other searching and data retrieval tools. A BLAST homology search service was also made available, allowing researchers to search all of the data, including the unique data, that are available from GSDB. These improvements are designed to make GSDB more accessible to users, extend the rich searching capability already present in GSDB, and to facilitate the transition to an integrated system containing many different types of biological data.     

用于基因数据挖掘的基因表达数据库GEO

使用高通量方法学来检测基因表达情况在最近几年已非常普遍。微集芯片技术可同时定量成千上万的基因转录本。基因表达综合数据库（Gene Expression Omnibus 简称GEO）是目前最大的而且完全公开的高通量分子丰度数据库,主要储存基因表达数据。该数据库以一个灵活开放的设计理念,允许用户或科研人员来递呈,保存和检索多种不同类型的数据。本文综合描述一下近年来该数据库在基因表达数据挖掘中的应用,同时介绍一些通过使用用户友好网络界面能有效探索、查询和再现数百个实验和数百万个基因表达谱的工具,以方便数据进行挖掘和可视化。登录GEO公用数据库的网址为：http://www.ncbi.nlm.nih.gov/geo.     

生命与健康大数据中心资源

生命与健康多组学数据是生命科学研究和生物医学技术发展的重要基础。然而,我国缺乏生物数据管理和共享平台,不但无法满足国内日益增长的生物医学及相关学科领域的研究发展需求,而且严重制约我国生物大数据整合共享与转化利用。鉴于此,中国科学院北京基因组研究所于2016年初成立生命与健康大数据中心(BIG Data Center, BIGD),围绕国家人口健康和重要战略生物资源,建立生物大数据管理平台和多组学数据资源体系。本文重点介绍BIGD的生命与健康大数据资源系统,主要包括组学原始数据归档库、基因组数据库、基因组变异数据库、基因表达数据库、甲基化数据库、生物信息工具库和生命科学维基知识库,提供生物大数据汇交、整合与共享服务,为促进我国生命科学数据管理、推动国家生物信息中心建设奠定重要基础。     

2019新型冠状病毒信息库

2019年12月在中国武汉开始爆发的新型肺炎已造成全球25个国家/地区的31516人感染、638人死亡(截止2020年2月7日16时),引起该肺炎的病毒被世界卫生组织命名为2019新型冠状病毒(2019-nCoV)。为促进2019-nCoV数据共享应用并及时向全球公众提供病毒的相关信息,国家生物信息中心(CNCB)/国家基因组科学数据中心(NGDC)建立了2019新型冠状病毒信息库(2019nCoVR,https://bigd.big.ac.cn/ncov)。该信息库整合了来自德国全球流感病毒数据库、美国国家生物技术信息中心、深圳(国家)基因库、国家微生物科学数据中心及CNCB/NGDC等机构公开发布的2019-nCoV核苷酸和蛋白质序列数据、元信息、学术文献、新闻动态、科普文章等信息,开展了不同冠状病毒株的基因组序列变异分析并提供可视化展示。同时,2019nCoVR无缝对接CNCB/NGDC的相关数据库,提供新测序病毒株系的基因组原始测序数据、组装后序列的在线汇交、管理与共享、国际数据库同步发布等数据服务。本文对2019nCoVR数据汇交、管理、发布及使用等进行全面阐述,以方便用户了解该信息库各项功能及数据状况,为加速开展病毒的分类溯源、变异演化、快速检测、药物研发以及新型肺炎的精准预防与治疗等研究提供重要基础。     

How Do Astronomers Share Data? Reliability and Persistence of Datasets Linked in AAS Publications and a Qualitative Study of Data Practices among US Astronomers

We analyze data sharing practices of astronomers over the past fifteen years. An analysis of URL links embedded in papers published by the American Astronomical Society reveals that the total number of links included in the literature rose dramatically from 1997 until 2005, when it leveled off at around 1500 per year. The analysis also shows that the availability of linked material decays with time: in 2011, 44% of links published a decade earlier, in 2001, were broken. A rough analysis of link types reveals that links to data hosted on astronomers'' personal websites become unreachable much faster than links to datasets on curated institutional sites. To gauge astronomers'' current data sharing practices and preferences further, we performed in-depth interviews with 12 scientists and online surveys with 173 scientists, all at a large astrophysical research institute in the United States: the Harvard-Smithsonian Center for Astrophysics, in Cambridge, MA. Both the in-depth interviews and the online survey indicate that, in principle, there is no philosophical objection to data-sharing among astronomers at this institution. Key reasons that more data are not presently shared more efficiently in astronomy include: the difficulty of sharing large data sets; over reliance on non-robust, non-reproducible mechanisms for sharing data (e.g. emailing it); unfamiliarity with options that make data-sharing easier (faster) and/or more robust; and, lastly, a sense that other researchers would not want the data to be shared. We conclude with a short discussion of a new effort to implement an easy-to-use, robust, system for data sharing in astronomy, at theastrodata.org, and we analyze the uptake of that system to-date.     

Intra-consortium data sharing in multi-national, multi-institutional genomic studies: gaps and guidance

Growing investments in health research by governments and charitable organizations have fueled an increase in collaborative research projects between investigators from affluent and developing countries. Current international guidelines are silent on common intra-consortium data-sharing issues that arise in the context of such collaborations. A lack of guidance on intra-consortium data sharing threatens to undermine the success of crucial research ventures. In this work we outline some of the practical problems commonly faced by investigators working in multi-institutional, international genomic collaborations and offer recommendations on these issues. A data sharing policy should be prospectively negotiated and concluded between collaborators as early as possible. Sponsors of research, including those from developing countries, should issue detailed guidance on the above and related issues as doing so will facilitate research and catalyze scientific progress.     

On the importance of being finished

The publication of an increasing number of draft genome sequences presents problems that will only be resolved by improved search tools and by complete finishing of the sequences - and their deposition in publicly accessible databases.     

Unpublished genomic data–how to share?

The field of genomics is often cited as the branch of biology that has led the way in data sharing. In most cases, sequencing data are made publicly available immediately after generation and often before the data generators have completed their analyses. Although the pros of such openness cannot be denied, problems can arise when unpublished genomic data are shared. In this editorial we touch on these issues and discuss the roles and responsibilities of the data generators, data users and journal editors.     

Data sharing across biobanks: epistemic values,data mutability and data incommensurability

Despite the centrality of epistemic issues in biobank knowledge generation, there is currently a lacuna in research addressing the epistemic assumptions of biobank science and data sharing. The article addresses this lacuna. Using the insights of philosophical and sociological analysis, we examine standardization and harmonization and central challenges biobank data-sharing faces. We defend two central epistemic values, namely “spatial and temporal translatability” and “epistemic adequacy” which foster effective biobank knowledge generation. The first refers to the way in which biobank data need to be re-usable and re-purposable by bioscience researchers who did not create them. Given the perennial issues of data mutability and incommensurability, we also propose “epistemic adequacy.” In uncovering the social and epistemic foundations of biobank science, we emphasize issues essential for achieving effective and transparent biobank practice and productive communication and engagement with the public about the nature, potential and limits of biobanks.     

LOX-DB-- database on lipoxygenases

SUMMARY: Lipoxygenases are a family of enzymes involved in a variety of human diseases like inflammation, asthma, artherosclerosis and cancer. The lipoxygenases database (LOX-DB) aims to be a web accessible compendium of information in particular on the mammalian members of this multigene family. This resource includes molecular structures, reference data, tools for structural and computational analysis as well as links to related information maintained by others. The data can be retrieved by the use of various search options and analyzed applying publicly available visualization tools. AVAILABILITY: LOX-DB is available at http://www.dkfz-heidelberg.de/spec/lox-db/     

Circular Dichroism Spectral Data and Metadata in the Protein Circular Dichroism Data Bank (PCDDB): A Tutorial Guide to Accession and Deposition

The Protein Circular Dichroism Data Bank (PCDDB) is a web-based resource containing circular dichroism (CD) and synchrotron radiation circular dichroism spectral and associated metadata located at http://pcddb.cryst.bbk.ac.uk. This resource provides a freely available, user-friendly means of accessing validated CD spectra and their associated experimental details and metadata, thereby enabling broad usage of this material and new developments across the structural biology, chemistry, and bioinformatics communities. The resource also enables researchers utilizing CD as an experimental technique to have a means of storing their data at a secure site from which it is easily retrievable, thereby making their results publicly accessible, a current requirement of many grant-funding agencies world-wide, as well as meeting the data-sharing requirements for journal publications. This tutorial provides extensive information on searching, accessing, and downloading procedures for those who wish to utilize the data available in the data bank, and detailed information on deposition procedures for creating and validating entries, including comprehensive explanations of their contents and formats, for those who wish to include their data in the data bank. Chirality 24:751-763, 2012. ? 2012 Wiley Periodicals, Inc.     

MGED standards: work in progress

The Microarray Gene Expression Data (MGED) society is an international organization established in 1999 for facilitating sharing of functional genomics and proteomics array data. To facilitate microarray data sharing, the MGED society has been working in establishing the relevant data standards. The three main components (which will be described in more detail later) of MGED standards are Minimum Information About a Microarray Experiment (MIAME), a document that outlines the minimum information that should be reported about a microarray experiment to enable its unambiguous interpretation and reproduction; MAGE, which consists of three parts, The Microarray Gene Expression Object Model (MAGE-OM), an XML-based document exchange format (MAGE-ML), which is derived directly from the object model, and the supporting tool kit MAGEstk; and MO, or MGED Ontology, which defines sets of common terms and annotation rules for microarray experiments, enabling unambiguous annotation and efficient queries, data analysis and data exchange without loss of meaning. We discuss here how these standards have been established, how they have evolved, and how they are used.     

myProMS, a web server for management and validation of mass spectrometry-based proteomic data

Curation and interpretation of protein databank-search results by human experts are key aspects of MS-based proteomic data acquisition. These tasks are often overlooked due to the vast amount of data to inspect. We have developed myProMS, a web server designed to ease search results validation and interpretation by improving data organization, mining and sharing between MS specialists and biologists during MS-based collaborative projects. A demo is accessible at http://bioinfo.curie.fr/myproms.     

Complete genomes in WWW Entrez: data representation and analysis.

MOTIVATION: The large amount of genome sequence data now publicly available can be accessed through the National Center for Biotechnology Information (NCBI) Entrez search and retrieval system, making it possible to explore data of a breadth and scope exceeding traditional flatfile views. RESULTS: Here we report recent improvements for completely sequenced genomes from viruses, bacteria, and yeast. Flexible web based views, precomputed relationships, and immediate access to analytical tools provide scientists with a portal into the new insights to be gained from completed genome sequences. AVAILABILITY: Entrez Genomes can be accessed on the World Wide Web at http://www.ncbi.nlm.nih.gov/Entrez/Genome/ org.html.     

Patenting of university and non-university public research organisations in Germany: evidence from patent applications for medical research results

Background

Patents are one of the most important forms of intellectual property. They grant a time-limited exclusivity on the use of an invention allowing the recuperation of research costs. The use of patents is fiercely debated for medical innovation and especially controversial for publicly funded research, where the patent holder is an institution accountable to public interest. Despite this controversy, for the situation in Germany almost no empirical information exists. The purpose of this study is to examine the amount, types and trends of patent applications for health products submitted by German public research organisations.

Methods/Principal Findings

We conducted a systematic search for patent documents using the publicly accessible database search interface of the German Patent and Trademark Office. We defined keywords and search criteria and developed search patterns for the database request. We retrieved documents with application date between 1988 and 2006 and processed the collected data stepwise to compile the most relevant documents in patent families for further analysis. We developed a rationale and present individual steps of a systematic method to request and process patent data from a publicly accessible database. We retrieved and processed 10194 patent documents. Out of these, we identified 1772 relevant patent families, applied for by 193 different universities and non-university public research organisations. 827 (47%) of these patent families contained granted patents. The number of patent applications submitted by universities and university-affiliated institutions more than tripled since the introduction of legal reforms in 2002, constituting almost half of all patent applications and accounting for most of the post-reform increase. Patenting of most non-university public research organisations remained stable.

Conclusions

We search, process and analyse patent applications from publicly accessible databases. Internationally mounting evidence questions the viability of policies to increase commercial exploitation of publicly funded research results. To evaluate the outcome of research policies a transparent evidence base for public debate is needed in Germany.     

ALFRED: An allele frequency database for anthropology

The deluge of data from the human genome project (HGP) presents new opportunities for molecular anthropologists to study human variation through the promise of vast numbers of new polymorphisms (e.g., single nucleotide polymorphisms or SNPs). Collecting the resulting data into a single, easily accessible resource will be important to facilitate this research. We created a prototype Web-accessible database named ALFRED (ALelle FREquency Database, http://alfred.med.yale.edu/alfred/) to store and make publicly available allele frequency data on diverse polymorphic sites for many populations. In constructing this database, we considered many different concerns relating to the types of information needed for anthropology, population genetics, molecular genetics, and statistics, as well as issues of data integrity and ease of access to data. We also developed links to other Web-based databases as well as procedures for others to make links to the data in ALFRED. Here we present an overview of the issues considered and provisional solutions, as well as an example of data already available. It is our hope that this database will be useful for research and teaching in a wide range of fields, and that colleagues from various fields will contribute to making ALFRED an important resource for many studies as yet unforeseen.     

Asking nicely: Best practices for requesting data

Compiling disparate datasets into publicly available composite databases helps natural resource communities explore ecological trends and effectively manage across spatiotemporal scales. Though some studies have reported on the database construction phase, fewer have evaluated the data acquisition and distribution process. To facilitate future data sharing collaborations, Louisiana State University surveyed data providers and requestors to understand the characteristics of effective data requests and sharing. Data providers were largely U.S. natural resource agency personnel, and they reported that unclear data requests, privacy issues, and rigid timelines and formats were the greatest barriers toward providing data, but that they were motivated by improving science and collaboration. Data requestors identified challenges such as evolving needs, standardization issues, and insufficient resources (time and funding) as barriers to compiling data for these types of efforts. In a time of big data, open access, and collaboration, significant scientific advances can be made with effective requests and inclusion of data sets into larger and more powerful databases.