Organisation of biotechnological information into knowledge

The success of biotechnological research, development and marketing depends to a large extent on the international transfer of information and on the ability to organise biotechnology information into knowledge. To increase the efficiency of information-based approaches, an information strategy has been developed and consists of the following stages: definition of the problem, its structure and sub-problems; acquisition of data by targeted processing of computer-supported bibliographic, numeric, textual and graphic databases; analysis of data and building of specialized in-house information systems; information processing for structuring data into systems, recognition of trends and patterns of knowledge, particularly by information synthesis using the concept of information density; design of research hypotheses; testing hypotheses in the laboratory and/or pilot plant; repeated evaluation and optimization of hypotheses by information methods and testing them by further laboratory work. The information approaches are illustrated by examples from the university-industry joint projects in biotechnology, biochemistry and agriculture.The author is with the International Center for Chemical Studies, Faculty of Science and Technology, University of Ljubljana FNT-Kü Vegova 4. P.O. Box 18/1, 61001 Ljubljana, Slovenia     

Inference of functional properties from large-scale analysis of enzyme superfamilies

As increasingly large amounts of data from genome and other sequencing projects become available, new approaches are needed to determine the functions of the proteins these genes encode. We show how large-scale computational analysis can help to address this challenge by linking functional information to sequence and structural similarities using protein similarity networks. Network analyses using three functionally diverse enzyme superfamilies illustrate the use of these approaches for facile updating and comparison of available structures for a large superfamily, for creation of functional hypotheses for metagenomic sequences, and to summarize the limits of our functional knowledge about even well studied superfamilies.     

Data-intensive science applied to broad-scale citizen science

Identifying ecological patterns across broad spatial and temporal extents requires novel approaches and methods for acquiring, integrating and modeling massive quantities of diverse data. For example, a growing number of research projects engage continent-wide networks of volunteers ('citizen-scientists') to collect species occurrence data. Although these data are information rich, they present numerous challenges in project design, implementation and analysis, which include: developing data collection tools that maximize data quantity while maintaining high standards of data quality, and applying new analytical and visualization techniques that can accurately reveal patterns in these data. Here, we describe how advances in data-intensive science provide accurate estimates in species distributions at continental scales by identifying complex environmental associations.     

Is applied nucleation a straightforward,cost‐effective forest restoration approach? Counter‐response to Holl and Zahawi (2018)

Holl and Zahawi (2018) agree on a number of approaches that we describe in a local case study of tropical montane cloud forest restoration in Mexico. However, they contend our argument that most applied nucleation projects have taken place in tropical lowlands as a mistake. They also provide data on the per‐hectare cost of restoration projects in Costa Rica and compare it to a higher cost figure of plantation‐style reforestation, a view contrary to ours. Last, Holl and Zahawi recognize that applied nucleation requires specialized personnel, but imply that the amount of training needed for this endeavor is not very different than what is required to implement other forest recovery projects. In this counter‐response, we provide some refinements to our original opinion and offer further information in support to our perspective.     

Advances in flux balance analysis

Biology is going through a paradigm shift from reductionist to holistic, systems-based approaches. The complete genome sequence for a number of organisms is available and the analysis of genome sequence data is proving very useful. Thus, genome sequencing projects and bioinformatic analyses are leading to a complete 'parts catalog' of the molecular components in many organisms. The next challenge will be to reconstruct and simulate overall cellular functions based on the extensive reductionist information. Recent advances have been made in the area of flux balance analysis, a mathematical modeling approach often utilized by metabolic engineers to quantitatively simulate microbial metabolism.     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.     

The Disclosure of Genetic Information: A Human Research Ethics Perspective

Increasing emphasis on genetic research means that growing numbers of human research projects in Australia will involve complex issues related to genetic privacy, familial information and genetic epidemiology. The Office of Population Health Genomics (Department of Health, Western Australia) hosted an interactive workshop to explore the ethical issues involved in the disclosure of genetic information, where researchers and members of human research ethics committees (HRECs) were asked to consider several case studies from an ethical perspective. Workshop participants used a variety of approaches to examine the complex ethical issues encountered, but did not consistently refer to the values and principles outlined in the National Statement on Ethical Conduct in Human Research (NHMRC 2007) or apply rational ethical approaches. Overall, the data suggested that both researchers and HREC members may benefit from further education and support regarding the application of ethical frameworks to the issues encountered in genetic research.     

An RNAi screening platform to identify secretion machinery in mammalian cells

Integrative approaches to study protein function in a cellular context are a vital aspect of understanding human disease. Genome sequencing projects provide the basic catalogue of information with which to unravel gene function, but more systematic applications of this resource are now necessary. Here, we describe and test a platform with which it is possible to rapidly use RNA interference in cultured mammalian cells to probe for proteins involved in constitutive protein secretion. Synthetic small interfering RNA molecules are arrayed in chambered slides, then incubated with cells and an assay for secretion performed. Automated microscopy is used to acquire images from the experiments, and automated single-cell analysis rapidly provides reliable quantitative data. In test arrays of 92 siRNA spots targeting 37 prospective membrane traffic proteins, our approach identifies 7 of these as being important for the correct delivery of a secretion marker to the cell surface. Correlating these findings with other screens and bioinformatic information makes these candidates highly likely to be novel membrane traffic machinery components.     

From cancer genomes to cancer models: bridging the gaps

Cancer genome projects are now being expanded in an attempt to provide complete landscapes of the mutations that exist in tumours. Although the importance of cataloguing genome variations is well recognized, there are obvious difficulties in bridging the gaps between high‐throughput resequencing information and the molecular mechanisms of cancer evolution. Here, we describe the current status of the high‐throughput genomic technologies, and the current limitations of the associated computational analysis and experimental validation of cancer genetic variants. We emphasize how the current cancer‐evolution models will be influenced by the high‐throughput approaches, in particular through efforts devoted to monitoring tumour progression, and how, in turn, the integration of data and models will be translated into mechanistic knowledge and clinical applications.     

Computational approaches to gene prediction

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.     

Plant genome sequencing: applications for crop improvement

DNA sequencing technology is undergoing a revolution with the commercialization of second generation technologies capable of sequencing thousands of millions of nucleotide bases in each run. The data explosion resulting from this technology is likely to continue to increase with the further development of second generation sequencing and the introduction of third generation single‐molecule sequencing methods over the coming years. The question is no longer whether we can sequence crop genomes which are often large and complex, but how soon can we sequence them? Even cereal genomes such as wheat and barley which were once considered intractable are coming under the spotlight of the new sequencing technologies and an array of new projects and approaches are being established. The increasing availability of DNA sequence information enables the discovery of genes and molecular markers associated with diverse agronomic traits creating new opportunities for crop improvement. However, the challenge remains to convert this mass of data into knowledge that can be applied in crop breeding programs.     

Computer-aided biotechnology: from immuno-informatics to reverse vaccinology

Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.     

Uniform standards for genome databases in forest and fruit trees

TreeGenes and tree fruit Genome Database Resources serve the international forestry and fruit tree genomics research communities, respectively. These databases hold similar sequence data and provide resources for the submission and recovery of this information in order to enable comparative genomics research. Large-scale genotype and phenotype projects have recently spawned the development of independent tools and interfaces within these repositories to deliver information to both geneticists and breeders. The increase in next generation sequencing projects has increased the amount of data as well as the scale of analysis that can be performed. These two repositories are now working towards a similar goal of archiving the diverse, independent data sets generated from genotype/phenotype experiments. This is achieved through focused development on data input standards (templates), pipelines for the storage and automated curation, and consistent annotation efforts through the application of widely accepted ontologies to improve the extraction and exchange of the data for comparative analysis. Efforts towards standardization are not limited to genotype/phenotype experiments but are also being applied to other data types to improve gene prediction and annotation for de novo sequencing projects. The resources developed towards these goals represent the first large-scale coordinated effort in plant databases to add informatics value to diverse genotype/phenotype experiments.     

Resource dedication problem in a multi-project environment

There can be different approaches to the management of resources within the context of multi-project scheduling problems. In general, approaches to multi-project scheduling problems consider the resources as a pool shared by all projects. On the other hand, when projects are distributed geographically or sharing resources between projects is not preferred, then this resource sharing policy may not be feasible. In such cases, the resources must be dedicated to individual projects throughout the project durations. This multi-project problem environment is defined here as the resource dedication problem (RDP). RDP is defined as the optimal dedication of resource capacities to different projects within the overall limits of the resources and with the objective of minimizing a predetermined objective function. The projects involved are multi-mode resource constrained project scheduling problems with finish to start zero time lag and non-preemptive activities and limited renewable and nonrenewable resources. Here, the characterization of RDP, its mathematical formulation and two different solution methodologies are presented. The first solution approach is a genetic algorithm employing a new improvement move called combinatorial auction for RDP, which is based on preferences of projects for resources. Two different methods for calculating the projects’ preferences based on linear and Lagrangian relaxation are proposed. The second solution approach is a Lagrangian relaxation based heuristic employing subgradient optimization. Numerical studies demonstrate that the proposed approaches are powerful methods for solving this problem.     

Towards the integration of spatially and temporally resolved murine gene expression databases

Several large-scale projects are evaluating gene expression in the mouse brain, both spatially and temporally. These range from projects that cover a broad spectrum of genes and developmental stages to those with high-spatial resolution and gene coverage but for only a single developmental stage. Each project contains its own self-consistent data set and tools for analysis and mining. Preliminary efforts are under way to construct tools and an infrastructure with which the data from across these different projects can be statistically pooled and analyzed. However, many obstacles remain, and these must be addressed and overcome soon if we are to unify the data sets, otherwise the preliminary efforts will be wasted. Here, the various projects for collecting and mining this information are reviewed, some challenges in data set comparisons are discussed, and some basic proposals are made for overcoming the challenges.     

Prediction of protein function from protein sequence and structure

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.