ALFRED: an allele frequency database for diverse populations and DNA polymorphisms--an update

ALFRED (the ALelle FREquency Database) is designed to store and disseminate frequencies of alleles at human polymorphic sites for multiple populations, primarily for the population genetics and molecular anthropology communities. Currently ALFRED has information on over 180 polymorphic sites for more than 70 populations. Since our initial release of the database we have focussed on increasing the quantity and quality of data, making reciprocal links between ALFRED and other related databases, and providing useful tools to make the data more comprehensible to the end user. ALFRED is accessible from the Kidd Lab home page (http://info.med.yale. edu/genetics/kkidd/) or from ALFRED directly (http://alfred.med.yale. edu/alfred/index.asp).     

ALFRED: an allele frequency database for diverse populations and DNA polymorphisms

We have developed a publicly accessible database (ALFRED, the ALlele FREquency Database) that catalogues allele frequency data for a wide range of population samples and DNA polymorphisms. This database is web-accessible through our laboratory (Kidd Lab) Web site: http://info.med.yale.edu/genetics/kkidd. ALFRED currently contains data on 60 populations and 156 genetic systems including single nucleotide polymorphisms (SNPs), short tandem repeat polymorphisms (STRPs), variable number of tandem repeats (VNTRs) and insertion-deletion polymorphisms. While data are not available for all population-DNA polymorphism combinations, over 2000 allele frequency tables have been entered. Our database is designed (i) to address our specific research requirements as well as broader scientific objectives; (ii) to allow researchers and interested educators to easily navigate and retrieve data of interest to them; and (iii) to integrate links to other related public databases such as dbSNP, GenBank and PubMed.     

ALFRED: the ALelle FREquency Database. Update

Elaboration of ALFRED (http://alfred.med.yale.edu) is being continued in two directions. One of which is developing tools for efficiently annotating the entries and checking the integrity of the data already in the database while the other is to increase the quantity and accessibility of data. Information contained in ALFRED such as, polymorphic sites, number of populations and frequency tables (one sample typed for one site) has significantly increased.     

TGB: the tobacco genetics and breeding database

The germplasm of the genus Nicotiana contains more than 5,000 accessions and plays an important role in modern biological research. Tobacco can be used as a model system to develop methodologies for plant transformation and for investigating gene function. In order to develop the study of Nicotiana, a large quantity of data on germplasm, sequences, molecular markers and genetically modified tobacco was required for in-depth and systematic collation and research. It became necessary to establish a special database for tobacco genetics and breeding. The tobacco genetics and breeding (TGB, http://yancao.sdau.edu.cn/tgb) database was developed with the aim of bringing together tobacco genetics and breeding. The database has three main features: (1) a materials database with information on 1,472 Nicotiana germplasm accessions, as well as updated genomic and expressed sequence tag (EST) data available from the public database; (2) a molecular markers database containing a total of 12,388 potential intron polymorphisms 10,551 EST-simple sequence repeat (EST-SSR) and 66,297 genomic-SSR markers; and (3) an applications database with genetic maps and some genetically modified studies in tobacco. The TGB database also makes Basic Local Alignment Search Tool and primer designing tools publicly available. As far as can be ascertained, the TGB database is the first tobacco genetics and breeding database to be created, and all this comprehensive information will aid basic research into Nicotiana and other related plants. It will serve as an excellent resource for the online tobacco research community.     

Advancing ecological understandings through technological transformations in noninvasive genetics

Noninvasive genetic approaches continue to improve studies in molecular ecology, conservation genetics and related disciplines such as forensics and epidemiology. Noninvasive sampling allows genetic studies without disturbing or even seeing the target individuals. Although noninvasive genetic sampling has been used for wildlife studies since the 1990s, technological advances continue to make noninvasive approaches among the most used and rapidly advancing areas in genetics. Here, we review recent advances in noninvasive genetics and how they allow us to address important research and management questions thanks to improved techniques for DNA extraction, preservation, amplification and data analysis. We show that many advances come from the fields of forensics, human health and domestic animal health science, and suggest that molecular ecologists explore literature from these fields. Finally, we discuss how the combination of advances in each step of a noninvasive genetics study, along with fruitful areas for future research, will continually increase the power and role of noninvasive genetics in molecular ecology and conservation genetics.     

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/     

Predictome: a database of putative functional links between proteins

The current deluge of genomic sequences has spawned the creation of tools capable of making sense of the data. Computational and high-throughput experimental methods for generating links between proteins have recently been emerging. These methods effectively act as hypothesis machines, allowing researchers to screen large sets of data to detect interesting patterns that can then be studied in greater detail. Although the potential use of these putative links in predicting gene function has been demonstrated, a central repository for all such links for many genomes would maximize their usefulness. Here we present Predictome, a database of predicted links between the proteins of 44 genomes based on the implementation of three computational methods—chromosomal proximity, phylogenetic profiling and domain fusion—and large-scale experimental screenings of protein–protein interaction data. The combination of data from various predictive methods in one database allows for their comparison with each other, as well as visualization of their correlation with known pathway information. As a repository for such data, Predictome is an ongoing resource for the community, providing functional relationships among proteins as new genomic data emerges. Predictome is available at http://predictome.bu.edu.     

Post-GWAS: where next? More samples,more SNPs or more biology?

The power of genome-wide association studies (GWAS) rests on several foundations: (i) there is a significant amount of additive genetic variation, (ii) individual causal polymorphisms often have sizable effects and (iii) they segregate at moderate-to-intermediate frequencies, or will be effectively ‘tagged'' by polymorphisms that do. Each of these assumptions has recently been questioned. (i) Why should genetic variation appear additive given that the underlying molecular networks are highly nonlinear? (ii) A new generation of relatedness-based analyses directs us back to the nearly infinitesimal model for effect sizes that quantitative genetics was long based upon. (iii) Larger effect causal polymorphisms are often low frequency, as selection might lead us to expect. Here, we review these issues and other findings that appear to question many of the foundations of the optimism GWAS prompted. We then present a roadmap emerging as one possible future for quantitative genetics. We argue that in future GWAS should move beyond purely statistical grounds. One promising approach is to build upon the combination of population genetic models and molecular biological knowledge. This combined treatment, however, requires fitting experimental data to models that are very complex, as well as accurate capturing of the uncertainty of resulting inference. This problem can be resolved through Bayesian analysis and tools such as approximate Bayesian computation—a method growing in popularity in population genetic analysis. We show a case example of anterior–posterior segmentation in Drosophila, and argue that similar approaches will be helpful as a GWAS augmentation, in human and agricultural research.     

A prototype object database for mitochondrial DNA variation

Surveys of biochemical and molecular genetic variation in natural populations have generated a wealth of data, but this valuable resource has not been adequately preserved. We hope to prevent further loss by establishing a community database for population genetic surveys. We explored the feasibility of a population genetics database by developing a prototype for animal mitochondrial DNA (mtDNA) surveys. This prototype includes the specification of a format for data files that are to be submitted to the database, an open-source object database that encapsulates data with methods to display and analyze data, and a website where data can be retrieved in either its original form or extensible markup language (XML). Data from more than 50 published surveys of mtDNA variation were retrieved from the literature and entered into the database. We hope that the population genetics community will support this project by contributing both data and expertise.     

A new versatile database created for geneticists and breeders to link molecular and phenotypic data in perennial crops: the AppleBreed DataBase

OBJECTIVE: AppleBreed DataBase (DB) aims to store genotypic and phenotypic data from multiple pedigree verified plant populations (crosses, breeding selections and commercial cultivars) so that they are easily accessible for geneticists and breeders. It will help in elucidating the genetics of economically important traits, in identifying molecular markers associated with agronomic traits, in allele mining and in choosing the best parental cultivars for breeding. It also provides high traceability of data over generations, years and localities. AppleBreed DB could serve as a generic database design for other perennial crops with long economic lifespans, long juvenile periods and clonal propagation. RESULTS: AppleBreed DB is organized as a relational database. The core element is the GENOTYPE entity, which has two sub-classes at the physical level: TREE and DNA-SAMPLE. This approach facilitates all links between plant material, phenotypic and molecular data. The entities TREE, DNA-SAMPLE, PHENOTYPE and MOLECULAR DATA allow multi-annual observations to be stored as individual samples of individual trees, even if the nature of these observations differs greatly (e.g. molecular data on parts of the apple genome, physico-chemical measurements of fruit quality traits, and evaluation of disease resistance). AppleBreed DB also includes synonyms for cultivars and pedigrees. Finally, it can be loaded and explored through the web, and comes with tools to present basic statistical overviews and with validation procedures for phenotypic and marker data to certify data quality. AppleBreed DB was developed initially as a tool for scientists involved in apple genetics within the framework of the European project, 'High-quality Disease Resistance in Apples for Sustainable Agriculture' (HiDRAS), but it is also applicable to many other perennial crops.     

SEMEDA: ontology based semantic integration of biological databases

MOTIVATION: Many molecular biological databases are implemented on relational Database Management Systems, which provide standard interfaces like JDBC and ODBC for data and metadata exchange. By using these interfaces, many technical problems of database integration vanish and issues related to semantics remain, e.g. the use of different terms for the same things, different names for equivalent database attributes and missing links between relevant entries in different databases. RESULTS: In this publication, principles and methods that were used to implement SEMEDA (Semantic Meta Database) are described. Database owners can use SEMEDA to provide semantically integrated access to their databases as well as to collaboratively edit and maintain ontologies and controlled vocabularies. Biologists can use SEMEDA to query the integrated databases in real time without having to know the structure or any technical details of the underlying databases. AVAILABILITY: SEMEDA is available at http://www-bm.ipk-gatersleben.de/semeda/. Database providers who intend to grant access to their databases via SEMEDA are encouraged to contact the authors.     

Public informatics resources for rice and other grasses

As an emerging model system, rice will benefit from an informatics infrastructure which organizes genome data and makes it available worldwide. RiceGenes and other Internet-accessible resources are evolving to meet these goals. Grass crops such as rice, maize, millet, sorghum and wheat are closely related but are represented by independent database projects; interlinking these resources would create a broad view of grass genetics and make it easier to compare data across genomes. The future success of grass informatics depends on the development of new comparative mapping displays as well as the participation of the research community in assembling and curating comparative map data.     

Textpresso site‐specific recombinases: A text‐mining server for the recombinase literature including Cre mice and conditional alleles

Textpresso Site Specific Recombinases ( http://ssrc.genetics.uga.edu/ ) is a text‐mining web server for searching a database of more than 9,000 full‐text publications. The papers and abstracts in this database represent a wide range of topics related to site‐specific recombinase (SSR) research tools. Included in the database are most of the papers that report the characterization or use of mouse strains that express Cre recombinase as well as papers that describe or analyze mouse lines that carry conditional (floxed) alleles or SSR‐activated transgenes/knockins. The database also includes reports describing SSR‐based cloning methods such as the Gateway or the Creator systems, papers reporting the development or use of SSR‐based tools in systems such as Drosophila, bacteria, parasites, stem cells, yeast, plants, zebrafish, and Xenopus as well as publications that describe the biochemistry, genetics, or molecular structure of the SSRs themselves. Textpresso Site Specific Recombinases is the only comprehensive text‐mining resource available for the literature describing the biology and technical applications of SSRs. genesis 47:842–846, 2009. © 2009 Wiley‐Liss, Inc.     

widgetcon: A website and program for quick conversion among common population genetic data formats

One of the most tedious steps in genetic data analyses is the reformatting data generated with one program for use with other applications. This conversion is necessary because comprehensive evaluation of the data may be based on different algorithms included in diverse software, each requiring a distinct input format. A platform‐independent and freely available program or a web‐based tool dedicated to such reformatting can save time and efforts in data processing. Here, we report widgetcon , a website and a program which has been developed to quickly and easily convert among various molecular data formats commonly used in phylogenetic analysis, population genetics, and other fields. The web‐based service is available at https://www.widgetcon.net . The program and the website convert the major data formats in four basic steps in less than a minute. The resource will be a useful tool for the research community and can be updated to include more formats and features in the future.     

PEAS V1.0: a package for elementary analysis of SNP data

We have developed a software package named PEAS to facilitate analyses of large data sets of single nucleotide polymorphisms (SNPs) for population genetics and molecular phylogenetics studies. PEAS reads SNP data in various formats as input and is versatile in data formatting; using PEAS, it is easy to create input files for many popular packages, such as STRUCTURE, frappe, Arlequin, Haploview, LDhat, PLINK, EIGENSOFT, PHASE, fastPHASE, MEGA and PHYLIP. In addition, PEAS fills up several analysis gaps in currently available computer programs in population genetics and molecular phylogenetics. Notably, (i) It calculates genetic distance matrices with bootstrapping for both individuals and populations from genome-wide high-density SNP data, and the output can be streamlined to MEGA and PHYLIP programs for further processing; (ii) It calculates genetic distances from STRUCTURE output and generates MEGA file to reconstruct component trees; (iii) It provides tools to conduct haplotype sharing analysis for phylogenetic studies based on high-density SNP data. To our knowledge, these analyses are not available in any other computer program. PEAS for Windows is freely available for academic users from http://www.picb.ac.cn/~xushua/index.files/Download_PEAS.htm.     

孟德尔豌豆基因克隆的研究进展及其在遗传学教学中的应用

150多年前, 孟德尔进行了豌豆7对相对性状的杂交试验, 发现了遗传学的两个基本规律。1900年, 孟德尔定律被重新发现以后, 人们从生理生化、细胞和分子水平等不同层次上对豌豆的这7个性状进行了深入研究。近年, 随着分子生物学技术的发展, 已有种子形状(R)、茎的长度(Le)、子叶颜色(I)和花的颜色(A)等4个性状的基因被克隆; 未成熟豆荚的颜色(Gp)、花的着生位置(Fa)和豆荚形状(V)的基因已被定位在各自的连锁群上。4个孟德尔基因的鉴定和克隆加深了人们对基因概念的理解：如基因功能的多样性、在分子水平上基因变异原因的多样性、显性和隐性的分子实质等。在遗传学教学中, 把孟德尔基因克隆和研究的最新进展介绍给学生, 在分子水平上诠释经典遗传规律, 有助于提高学生的学习兴趣, 帮助学生全面把握从形式遗传学到分子遗传学的内容和遗传学的发展方向。     

MeTaDoR: a comprehensive resource for membrane targeting domains and their host proteins

MOTIVATION: Protein-lipid interactions play a central role in cellular signaling and membrane trafficking and at the core of these interactions are domains specialized in lipid binding and membrane targeting. Considering the importance of these domains, we have created MeTaDoR, a comprehensive resource dedicated to membrane targeting domains (MTDs). RESULT: MeTaDoR begins with a brief introduction about all the important MTDs including their subcellular localization and structural features. Sequences of all known MTDs are then provided in two formats: standard Prosite format and a parsed tab-delimited format that provides a manually curated classification into binding or non-binding. Structures of all MTDs and host proteins known so far are provided with links to PDB and Pfam databases. Membrane-binding orientation of these proteins, whether experimentally determined or proposed, is also provided with links to the appropriate literature. To facilitate molecular dynamics studies of these proteins, the force-field parameters for many non-standard lipids that commonly interact with these proteins are also provided. Finally, an online server for predicting membrane-binding proteins and a search function with various search fields are included. The resource is publicly available and will be updated on a regular basis.     

On the relationship between a resource based measure of geodiversity and broad scale biodiversity patterns

Geodiversity, (diversity of the geosphere) incorporates many of the environmental patterns and processes that are considered drivers of biodiversity. Components of geodiversity (climate, topography, geology and hydrology) can be considered in terms of their resource giving potential, where resources are taken as energy, water, space and nutrients. The total amount of these resources, along with their spatial and temporal variation, is herein proposed as a compound index of geodiversity that has the potential to model broad scale biodiversity patterns. This paper outlines potential datasets that could be used to represent geodiversity, and then reviews the theoretical links between each element of the proposed compound index of geodiversity (overall resource availability, temporal variation and spatial variation in those resources) and broad-scale patterns of biodiversity. Support for the influence of each of the elements of geodiversity on overall biodiversity patterns was found in the literature, although the majority of relevant research focuses on resource availability, particularly available energy. The links between temporal and spatial variation in resources and biodiversity have been less thoroughly investigated in the literature. For the most part, it was reported that overall resource availability, temporal variation and spatial variation in those resources do not act in isolation in terms of controlling biodiversity. Overall there are sufficient datasets to calculate the proposed compound index of geodiversity, and evidence in the literature for links between the geographical distribution of biodiversity and each of the elements of the compound index defined. Since data for measuring geodiversity is more spatially consistent and widely available (thanks to satellite remote sensing) geodiversity has potential as a conservation planning tool, especially where biological data are not available or sparsely distributed.