Proteomic databases and software on the web

In the wake of sequencing projects, protein function analysis is evolving fast, from the careful design of assays that address specific questions to 'large-scale' proteomics technologies that yield proteome-wide maps of protein expression or interaction.As these new technologies depend heavily on information storage, representation and analysis, existing databases and software tools are being adapted, while new resources are emerging.This paper describes the proteomics databases and software available through the World-Wide Web, focusing on their present use and applicability.As the resource situation is highly transitory, trends and probable evolutions are discussed whenever applicable.     

Homoeologous relationships of rice, wheat and maize chromosomes

A set of cDNA clones, which had previously been mapped onto wheat chromosomes, was genetically mapped onto the chromosomes of rice. The resulting comparative maps make it possible to estimate the degree of linkage conservation between these two species. A number of chromosomal rearrangements, some of which must have involved interchromosomal translocations, differentiate the rice and wheat genomes. However, synteny of a large proportion of the loci appears to be conserved between the two species. The results of this study, combined with those from a recently published comparative map of the rice and maize genomes, suggest that rice, wheat and maize share extensive homoeologies in a number of regions in their genomes. Some chromosomes (e.g. chromosome 4 in rice, chromosomes 2 and 2S in wheat and maize, respectively) may have escaped major rearrangement since the divergence of these species from their last common ancestor. Comparative maps for rice, wheat and maize should make it possible to begin uniting the genetics of these species and allow for transfer of mapping information (including centromere positions) and molecular marker resources (e.g. RFLP probes) between species. In addition, such maps should shed light on the nature of chromosome evolution that accompanied the radiation of grasses in the early stages of plant diversification.     

The Next-Generation Flora iFlora

The 21st century has witnessed a rapid development in technologies of molecular biology and computer informatics. Fundamental changes have taken place in means and methods in which humans take cognition of the world. Based on the currently available eFlora and combining this with elements of next generation sequencing techniques, DNA sequence data, geographical information system data and computer information technology, the next-generation Flora (iFlora) is bursting. Through a series of key technological innovations and integrations, the main objective of iFlora is to construct the next-generation Flora, which will fulfill the function of accurately and rapidly identifying species and acquiring species related digital information. iFlora will greatly advance the development of plant taxonomy, phylogenetics, evolutionary biology, ecology, biogeography, conservation biology and other related disciplines. Furthermore, iFlora will be a valuable tool for biodiversity conservation and sustainable utilization of biological resources, ecological security, public education and services, and will profoundly promote public understanding of biodiversity. The application of iFlora will tremendously nurture and boost the taxasphere and bioliterate world, and will be a new focal point that may reshape modern botany at the global and regional levels.     

Physical molecular maps of wheat chromosomes

In bread wheat, a set of 527 simple sequence repeats (SSRs) were tried on 164 deletion lines, leading to a successful mapping of 270 SSRs on 313 loci covering all 21 chromosomes. A maximum of 119 loci (38%) were located on B subgenome, and a minimum of 90 loci (29%) mapped on D subgenome. Similarly, homoeologous group 7 carried a maximum of 61 loci (19%), and group 4 carried a minimum of 22 loci (7%). Of the cited 270 SSRs, 39 had multiple loci, but only eight of these detected homoeologous loci. Linear order of loci in physical maps largely corresponded with those in the genetic maps. Apparently, distances between each of only 26 pairs of loci significantly differed from the corresponding distances on genetic maps. Some loci, which were genetically mapped close to the centromere, were physically located distally, while other loci that were mapped distally in the genetic maps were located in the proximal bins in the physical maps. This suggested that although the linear order of the loci was largely conserved, variation does exist between genetic and physical distances.Electronic Supplementary Material Supplementary material is available for this article at .     

Assessing and improving the transferability of current global spatial prediction models

Aim

Global-scale maps of the environment are an important source of information for researchers and decision makers. Often, these maps are created by training machine learning algorithms on field-sampled reference data using remote sensing information as predictors. Since field samples are often sparse and clustered in geographic space, model prediction requires a transfer of the trained model to regions where no reference data are available. However, recent studies question the feasibility of predictions far beyond the location of training data.

Innovation

We propose a novel workflow for spatial predictive mapping that leverages recent developments in this field and combines them in innovative ways with the aim of improved model transferability and performance assessment. We demonstrate, evaluate and discuss the workflow with data from recently published global environmental maps.

Main conclusions

Reducing predictors to those relevant for spatial prediction leads to an increase of model transferability and map accuracy without a decrease of prediction quality in areas with high sampling density. Still, reliable gap-free global predictions were not possible, highlighting that global maps and their evaluation are hampered by limited availability of reference data.     

The landscape for epigenetic/epigenomic biomedical resources

Recent advances in molecular biology and computational power have seen the biomedical sector enter a new era, with corresponding development of Bioinformatics as a major discipline. Generation of enormous amounts of data has driven the need for more advanced storage solutions and shared access through a range of public repositories. The number of such biomedical resources is increasing constantly and mining these large and diverse data sets continues to present real challenges. This paper attempts a general overview of currently available resources, together with remarks on their data mining and analysis capabilities. Of interest here is the recent shift in focus from genetic to epigenetic/epigenomic research and the emergence and extension of resource provision to support this both at local and global scale. Biomedical text and numerical data mining are both considered, the first dealing with automated methods for analyzing research content and information extraction, and the second (broadly) with pattern recognition and prediction. Any summary and selection of resources is inherently limited, given the spectrum available, but the aim is to provide a guideline for the assessment and comparison of currently available provision, particularly as this relates to epigenetics/epigenomics.     

The landscape for epigenetic/epigenomic biomedical resources

Recent advances in molecular biology and computational power have seen the biomedical sector enter a new era, with corresponding development of Bioinformatics as a major discipline. Generation of enormous amounts of data has driven the need for more advanced storage solutions and shared access through a range of public repositories. The number of such biomedical resources is increasing constantly and mining these large and diverse data sets continues to present real challenges. This paper attempts a general overview of currently available resources, together with remarks on their data mining and analysis capabilities. Of interest here is the recent shift in focus from genetic to epigenetic/epigenomic research and the emergence and extension of resource provision to support this both at local and global scale. Biomedical text and numerical data mining are both considered, the first dealing with automated methods for analyzing research content and information extraction, and the second (broadly) with pattern recognition and prediction. Any summary and selection of resources is inherently limited, given the spectrum available, but the aim is to provide a guideline for the assessment and comparison of currently available provision, particularly as this relates to epigenetics/epigenomics.     

Exploration and comparison of molecular mechanisms across diseases using MINERVA Net

Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease‐related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open‐access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.     

OpenPIP: An Open-source Platform for Hosting,Visualizing and Analyzing Protein Interaction Data

Knowing which proteins interact with each other is essential information for understanding how most biological processes at the cellular and organismal level operate and how their perturbation can cause disease. Continuous technical and methodological advances over the last two decades have led to many genome-wide systematically-generated protein–protein interaction (PPI) maps. To help store, visualize, analyze and disseminate these specialized experimental datasets via the web, we developed the freely-available Open-source Protein Interaction Platform (openPIP) as a customizable web portal designed to host experimental PPI maps. Such a portal is often required to accompany a paper describing the experimental data set, in addition to depositing the data in a standard repository. No coding skills are required to set up and customize the database and web portal. OpenPIP has been used to build the databases and web portals of two major protein interactome maps, the Human and Yeast Reference Protein Interactome maps (HuRI and YeRI, respectively). OpenPIP is freely available as a ready-to-use Docker container for hosting and sharing PPI data with the scientific community at http://openpip.baderlab.org/ and the source code can be downloaded from https://github.com/BaderLab/openPIP/.     

Web servicing the biological office

Biologists routinely use Microsoft Office applications for standard analysis tasks. Despite ubiquitous internet resources, information needed for everyday work is often not directly and seamlessly available. Here we describe a very simple and easily extendable mechanism using Web Services to enrich standard MS Office applications with internet resources. We demonstrate its capabilities by providing a Web-based thesaurus for biological objects, which maps names to database identifiers and vice versa via an appropriate synonym list. The client application ProTag makes these features available in MS Office applications using Smart Tags and Add-Ins. AVAILABILITY: http://services.bio.ifi.lmu.de/prothesaurus/     

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.     

Current status of the ovine genome map

Genome maps in livestock species have been under development for the last decade. While the sheep map is one of the least advanced for livestock, the amount of available information is noteworthy, in light of the paucity of funding and personnel devoted to this project. These limited resources have been strategically aligned to take advantage of information from the human, mouse and bovine mapping and sequencing efforts. The resulting ovine linkage and physical maps have greatly enhanced the search for genes controlling important traits in sheep. In order to improve the efficiency of these investigations, it is imperative that efforts on the sheep comparative map be continued.     

Correlated mutations and residue contacts in proteins

The maintenance of protein function and structure constrains the evolution of amino acid sequences. This fact can be exploited to interpret correlated mutations observed in a sequence family as an indication of probable physical contact in three dimensions. Here we present a simple and general method to analyze correlations in mutational behavior between different positions in a multiple sequence alignment. We then use these correlations to predict contact maps for each of 11 protein families and compare the result with the contacts determined by crystallography. For the most strongly correlated residue pairs predicted to be in contact, the prediction accuracy ranges from 37 to 68% and the improvement ratio relative to a random prediction from 1.4 to 5.1. Predicted contact maps can be used as input for the calculation of protein tertiary structure, either from sequence information alone or in combination with experimental information. © 1994 John Wiley & Sons, Inc.     

A complete small molecule dataset from the protein data bank

A complete set of 6300 small molecule ligands was extracted from the protein data bank, and deposited online in PubChem as data source 'SMID'. This set's major improvement over prior methods is the inclusion of cyclic polypeptides and branched polysaccharides, including an unambiguous nomenclature, in addition to normal monomeric ligands. Only the best available example of each ligand structure is retained, and an additional dataset is maintained containing co-ordinates for all examples of each structure. Attempts are made to correct ambiguous atomic elements and other common errors, and a perception algorithm was used to determine bond order and aromaticity when no other information was available.     

Statistical methods for temporal and space–time analysis of community composition data

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran''s eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.     

The chromosome 4 workshop report

In the concluding session the compilation and availability of available resources on chromosome 4 were discussed. A listing of these resources is available from Jeff Murray at the University of Iowa for anyone with an interest in human chromosome 4. Rick Myers stressed the commitment of the UCSF Genome Center to make resources available to the mapping community as a whole as soon as they have been identified and characterized. There was general agreement that such a philosophy will facilitate the rapid generation and integration of the maps from several groups currently working in this area. The chromosome 4 committee for the Human Gene Mapping workshops was scheduled to meet in August of 1991, and a second chromosome 4-specific meeting is to be held in Leiden on June 13 and 14, 1992 (contact Gert-Jan van Ommen for information). Future venues for the meeting are planned to alternate between countries with groups participating in mapping projects on this chromosome and initially will focus particularly on groups with chromosome wide mapping interests such as EUROGEM and the Human Genome Center.