Bioinformatics for glycomics: status, methods, requirements and perspectives

The term 'glycomics' describes the scientific attempt to identify and study all the glycan molecules - the glycome - synthesised by an organism. The aim is to create a cell-by-cell catalogue of glycosyltransferase expression and detected glycan structures. The current status of databases and bioinformatics tools, which are still in their infancy, is reviewed. The structures of glycans as secondary gene products cannot be easily predicted from the DNA sequence. Glycan sequences cannot be described by a simple linear one-letter code as each pair of monosaccharides can be linked in several ways and branched structures can be formed. Few of the bioinformatics algorithms developed for genomics/proteomics can be directly adapted for glycomics. The development of algorithms, which allow a rapid, automatic interpretation of mass spectra to identify glycan structures is currently the most active field of research. The lack of generally accepted ways to normalise glycan structures and exchange glycan formats hampers an efficient cross-linking and the automatic exchange of distributed data. The upcoming glycomics should accept that unrestricted dissemination of scientific data accelerates scientific findings and initiates a number of new initiatives to explore the data.     

GLYCOSCIENCES.de: an Internet portal to support glycomics and glycobiology research

The development of glycan-related databases and bioinformatics applications is considerably lagging behind compared with the wealth of available data and software tools in genomics and proteomics. Because the encoding of glycan structures is more complex, most of the bioinformatics approaches cannot be applied to glycan structures. No standard procedures exist where glycan structures found in various species, organs, tissues or cells can be routinely deposited. In this article the concepts of the GLYCOSCIENCES.de portal are described. It is demonstrated how an efficient structure-based cross-linking of various glycan-related data originating from different resources can be accomplished using a single user interface. The structure oriented retrieval options-exact structure, substructure, motif, composition and sugar components-are discussed. The types of available data-references, composition, spatial structures, nuclear magnetic resonance (NMR) shifts (experimental and estimated), theoretically calculated fragments and Protein Database (PDB) entries-are exemplified for Man(3.) The free availability and unrestricted use of glycan-related data is an absolute prerequisite to efficiently share distributed resources. Additionally, there is an urgent need to agree to a generally accepted exchange format as well as to a common software interface. An open access repository for glyco-related experimental data will secure that the loss of primary data will be considerably reduced.     

GenClust: A genetic algorithm for clustering gene expression data

Background  

Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering.     

GPU-powered tools boost molecular visualization

Recent advances in experimental structure determination provide a wealth of structural data on huge macromolecular assemblies such as the ribosome or viral capsids, available in public databases. Further structural models arise from reconstructions using symmetry orders or fitting crystal structures into low-resolution maps obtained by electron-microscopy or small angle X-ray scattering experiments. Visual inspection of these huge structures remains an important way of unravelling some of their secrets. However, such visualization cannot conveniently be carried out using conventional rendering approaches, either due to performance limitations or due to lack of realism. Recent developments, in particular drawing benefit from the capabilities of Graphics Processing Units (GPUs), herald the next generation of molecular visualization solutions addressing these issues. In this article, we present advances in computer science and visualization that help biologists visualize, understand and manipulate large and complex molecular systems, introducing concepts that remain little-known in the bioinformatics field. Furthermore, we compile currently available software and methods enhancing the shape perception of such macromolecular assemblies, for example based on surface simplification or lighting ameliorations.     

VECT: an automatic visual Perl programming tool for nonprogrammers

Modern high-throughput biological research produces enormous amount of data that must be processed by computers, but many biologists dealing with these data are not professional programmers. Despite increased awareness of interdisciplinary training in bioinformatics, many biologists still find it difficult to create their own computational solutions. VECT, the Visual Extraction and Conversion Tool, has been developed to assist nonprogrammers to create simple bioinformatics without having to master a programming language. VECT provides a unified graphical user interface for data extraction, data conversion, output composition, and Perl code generation. Programming using VECT is achieved by visually performing the desired data extraction, conversion, and output composition tasks using some sample user data. These tasks are then compiled by VECT into an executable Perl program, which can be saved for later use and can carry out the same computation independently of VECT. VECT is released under the GNU General Public License and is freely available for all major computing platforms including Macintosh OS X, Linux, and Microsoft Windows at www.complex.iastate.edu.     

Leveraging transformers-based language models in proteome bioinformatics

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.     

Glycomics: an integrated systems approach to structure-function relationships of glycans

In comparison with genomics and proteomics, the advancement of glycomics has faced unique challenges in the pursuit of developing analytical and biochemical tools and biological readouts to investigate glycan structure-function relationships. Glycans are more diverse in terms of chemical structure and information density than are DNA and proteins. This diversity arises from glycans' complex nontemplate-based biosynthesis, which involves several enzymes and isoforms of these enzymes. Consequently, glycans are expressed as an 'ensemble' of structures that mediate function. Moreover, unlike protein-protein interactions, which can be generally viewed as 'digital' in regulating function, glycan-protein interactions impinge on biological functions in a more 'analog' fashion that can in turn 'fine-tune' a biological response. This fine-tuning by glycans is achieved through the graded affinity, avidity and multivalency of their interactions. Given the importance of glycomics, this review focuses on areas of technologies and the importance of developing a bioinformatics platform to integrate the diverse datasets generated using the different technologies to allow a systems approach to glycan structure-function relationships.     

Context-dependent reproductive habitat selection: the interactive roles of structural complexity and cannibalistic conspecifics

Structural complexity generally reduces predation and cannibalism rates. Although the benefits from this effect vary among environmental contexts and through time, it has been the common explanation for high species abundance in complex habitats. We hypothesized that oviposition habitat selection for structural complexity depends on the expected trophic function of the progeny. In Salamandra infraimmaculata larvae, expected trophic function is dictated by their sequence of deposition. First cohorts cannibalize later-arriving cohorts, while all compete for shared prey resources. In a mesocosm experiment, we show that gravid salamanders facing conspecific-free pools preferred structurally simple habitats (no rocks), while females facing only pools with older conspecific larvae preferred complex habitats (with rocks). Context-dependent preference of habitat complexity for managing food/safety trade-offs may be extended from classic foraging patch decisions to breeding habitat selection. These trade-offs vary with dynamic larval processes such as priority effects and ontogenetic diet shifts, potentially leading to complex maternal parturition behaviours.     

The evolutionary convergence of avian lifestyles and their constrained coevolution with species' ecological niche

The fit between life histories and ecological niche is a paradigm of phenotypic evolution, also widely used to explain patterns of species co-occurrence. By analysing the lifestyles of a sympatric avian assemblage, we show that species'' solutions to environmental problems are not unbound. We identify a life-history continuum structured on the cost of reproduction along a temperature gradient, as well as habitat-driven parental behaviour. However, environmental fit and trait convergence are limited by niche filling and by within-species variability of niche traits, which is greater than variability of life histories. Phylogeny, allometry and trade-offs are other important constraints: lifetime reproductive investment is tightly bound to body size, and the optimal allocation to reproduction for a given size is not established by niche characteristics but by trade-offs with survival. Life histories thus keep pace with habitat and climate, but under the limitations imposed by metabolism, trade-offs among traits and species'' realized niche.     

No Trade-Off between Growth Rate and Temperature Stress Resistance in Four Insect Species

Although fast growth seems to be generally favored by natural selection, growth rates are rarely maximized in nature. Consequently, fast growth is predicted to carry costs resulting in intrinsic trade-offs. Disentangling such trade-offs is of great ecological importance in order to fully understand the prospects and limitations of growth rate variation. A recent study provided evidence for a hitherto unknown cost of fast growth, namely reduced cold stress resistance. Such relationships could be especially important under climate change. Against this background we here investigate the relationships between individual larval growth rate and adult heat as well as cold stress resistance, using eleven data sets from four different insect species (three butterfly species: Bicyclus anynana, Lycaena tityrus, Pieris napi; one Dipteran species: Protophormia terraenovae). Despite using different species (and partly different populations within species) and an array of experimental manipulations (e.g. different temperatures, photoperiods, feeding regimes, inbreeding levels), we were not able to provide any consistent evidence for trade-offs between fast growth and temperature stress resistance in these four insect species.     

Bioinformatics and data mining in proteomics

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.     

Bioinformatics and data mining in proteomics

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.     

Predicting sRNAs and Their Targets in Bacteria

Bacterial small RNAs (sRNAs) are an emerging class of regulatory RNAs of about 40-500 nucleotides in length and, by binding to their target mRNAs or proteins, get involved in many biological processes such as sensing environmental changes and regulating gene expression. Thus, identification of bacterial sRNAs and their targets has become an important part of sRNA biology. Current strategies for discovery of sRNAs and their targets usually involve bioinformatics prediction followed by experimental validation, emphasizing a key role for bioinformatics prediction. Here, therefore, we provided an overview on prediction methods, focusing on the merits and limitations of each class of models. Finally, we will present our thinking on developing related bioinformatics models in future.     

The challenge of measuring trade-offs in human life history research

Life history theory has become a prominent framework in the evolutionary social sciences, and the concept of trade-offs, the cornerstone of life history theory in studies on non-human taxa, has likewise been widely adopted. Yet, human life history research often assumes trade-offs without demonstrating them. This is not surprising given the practical difficulties in measuring trade-offs in long-lived animals, like humans. Four main methods are used to demonstrate trade-offs: phenotypic correlations, experimental manipulations, genetic correlations and correlated responses to selection. Here, I discuss challenges with these methods along with potential solutions. For example, individual heterogeneity within a population in quality or access to resources can mask underling trade-offs, and this can be accounted for by careful experimental manipulation or proper statistical treatment of observational data. In general, trade-offs have proven more difficult than expected to measure, and evidence across species is mixed, but strong evidence exists in some cases. I use the key trade-off between reproduction and survival to exemplify methods, challenges and solutions, and review the mixed evidence for a cost of reproduction in humans. I conclude by providing directions for future research. Promising avenues are opening thanks to recent advances in quantitative genetic and genomic methods coupled with the availability of high-quality large-scale datasets on humans from different populations, allowing the study of the evolutionary implications of life history trade-offs in humans.     

Informatics and computational strategies for the study of lipids

The ability to translate vast amounts of information, as obtained from lipidomic analysis, into the knowledge and understanding of biological phenomena is an important challenge faced by the lipidomics community. While many of the informatics and computational tools from other domains such as bioinformatics and metabolomics are also applicable to lipidomics data processing and analysis, new solutions and strategies are needed for the studies of lipidomes at the systems level. This is due to enormous functional and structural diversity of lipids as well as because of their complex regulation at multiple spatial and temporal scales. In order to better understand the lipidomes at the physiological level, lipids need to be modeled not only at the level of biological pathways but also at the level of the biophysical systems they are part of, such as cellular membranes or lipoprotein particles. Herein the current state, recent advances and new opportunities in the field of lipid bioinformatics are reviewed.     

Visualization of alpha-helical features in a density map constructed using 9 molecular images of the 1.8 MDa icosahedral core of pyruvate dehydrogenase

Strategies to achieve the highest resolutions in structures of protein complexes determined by cryo-electron microscopy generally involve averaging information from large numbers of individual molecular images. However, significant limitations are posed by heterogeneity in image quality and in protein conformation that are inherent to large data sets of images. Here, we demonstrate that the combination of iterative refinement and stringent molecular sorting is an effective method to obtain substantial improvements in map quality of the 1.8 MDa icosahedral catalytic core of the pyruvate dehydrogenase complex from Bacillus stearothermophilus. From a starting set of 42,945 images of the core complex, we show that using only the best 139 particles in the data set produces a map that is superior to those constructed with greater numbers of images, and that the location of many of the alpha-helices in the structure can be unambiguously visualized in a map constructed from as few as 9 particles.     

Neural optimization: Understanding trade-offs with Pareto theory

Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural ’bauplan’ has to account for multiple objectives simultaneously, including computational function, as well as additional factors such as robustness to environmental changes and energetic limitations. Oftentimes these objectives compete, and quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems from biophysically detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.     

Bioinformatics analysis of alternative splicing

Over the past few years, the analysis of alternative splicing using bioinformatics has emerged as an important new field, and has significantly changed our view of genome function. One exciting front has been the analysis of microarray data to measure alternative splicing genome-wide. Pioneering studies of both human and mouse data have produced algorithms for discerning evidence of alternative splicing and clustering genes and samples by their alternative splicing patterns. Moreover, these data indicate the presence of alternative splice forms in up to 80 per cent of human genes. Comparative genomics studies in both mammals and insects have demonstrated that alternative splicing can in some cases be predicted directly from comparisons of genome sequences, based on heightened sequence conservation and exon length. Such studies have also provided new insights into the connection between alternative splicing and a variety of evolutionary processes such as Alu-based exonisation, exon creation and loss. A number of groups have used a combination of bioinformatics, comparative genomics and experimental validation to identify new motifs for splice regulatory factors, analyse the balance of factors that regulate alternative splicing, and propose a new mechanism for regulation based on the interaction of alternative splicing and nonsense-mediated decay. Bioinformatics studies of the functional impact of alternative splicing have revealed a wide range of regulatory mechanisms, from NAGNAG sites that add a single amino acid; to short peptide segments that can play surprisingly complex roles in switching protein conformation and function (as in the Piccolo C2A domain); to events that entirely remove a specific protein interaction domain or membrane anchoring domain. Common to many bioinformatics studies is a new emphasis on graph representations of alternative splicing structures, which have many advantages for analysis.