Online analysis of development

The genomics revolution has altered the very nature of research in molecular biology, from how to find genes to how to find out what specific genes do. Given the availability of so many fully (or nearly) sequenced genomes, it is now relatively easy to track down dozens or even hundreds of genes relevant to a particular field of study. Unfortunately, up till now, the tools for determining what these genes actually do in embryos and cells have not kept pace, but the burgeoning field of bioinformatics should help correct this shortcoming and introduce the power of genomics to the study of developmental biology. In this review, some of the bioinformatics resources relevant to developmental biologists are described along with some simple approaches for applying these tools to analyzing early development.     

Nine quick tips for pathway enrichment analysis

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.     

Bioinformatics Goes to School—New Avenues for Teaching Contemporary Biology

Since 2010, the European Molecular Biology Laboratory''s (EMBL) Heidelberg laboratory and the European Bioinformatics Institute (EMBL-EBI) have jointly run bioinformatics training courses developed specifically for secondary school science teachers within Europe and EMBL member states. These courses focus on introducing bioinformatics, databases, and data-intensive biology, allowing participants to explore resources and providing classroom-ready materials to support them in sharing this new knowledge with their students.In this article, we chart our progress made in creating and running three bioinformatics training courses, including how the course resources are received by participants and how these, and bioinformatics in general, are subsequently used in the classroom. We assess the strengths and challenges of our approach, and share what we have learned through our interactions with European science teachers.     

Using provenance to manage knowledge of in silico experiments

This article offers a briefing in one of the knowledge management issues of in silico experimentation in bioinformatics. Recording of the provenance of an experiment-what was done; where, how and why, etc. is an important aspect of scientific best practice that should be extended to in silico experimentation. We will do this in the context of eScience which has been part of the move of bioinformatics towards an industrial setting. Despite the computational nature of bioinformatics, these analyses are scientific and thus necessitate their own versions of typical scientific rigour. Just as recording who, what, why, when, where and how of an experiment is central to the scientific process in laboratory science, so it should be in silico science. The generation and recording of these aspects, or provenance, of an experiment are necessary knowledge management goals if we are to introduce scientific rigour into routine bioinformatics. In Silico experimental protocols should themselves be a form of managing the knowledge of how to perform bioinformatics analyses. Several systems now exist that offer support for the generation and collection of provenance information about how a particular in silico experiment was run, what results were generated, how they were generated, etc. In reviewing provenance support, we will review one of the important knowledge management issues in bioinformatics.     

Ins and Outs of Systems Biology vis-à-vis Molecular Biology: Continuation or Clear Cut?

The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology ‘revolutionizes’ molecular biology and ‘transcends’ its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology’s use of modelling and bioinformatics, and by its scale enlargement.     

Interactome networks and human disease

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.     

The case for studying tadpole autecology,with comments on strategies to study other small,fast-moving animals in nature

Two of the most fundamental questions in tadpole biology, also applicable to most small, under-studied organisms are: (1) ‘Why are they built the way they are?’ and (2) ‘Why do they live where they do?’ Regrettably, despite significant progress in most aspects of tadpole biology, the answers to these questions are not much better now than they were in the last century. We propose that an autecological approach, that is the careful observation of individuals and how they interact with the environment, is a potential path towards a fuller understanding of tadpole ecomorphology and evolution. We also discuss why more attention should be given to studying atypical tadpoles from atypical environments, such as torrential streams, water-filled cavities of terrestrial plants and wet rock surfaces neighbouring streams. Granted, tadpoles are rare in these settings, but in those unusual habitats the physical environments can be well described and characterized. In contrast, the more common ponds where tadpoles are found are typically too structurally complex to be easily delineated. This makes it difficult to know exactly what individual tadpoles are doing and what environmental parameters they are responding to. Our overall thesis is that to understand tadpoles we must see exactly what they are doing, where they are doing it, and how they are doing it. This takes work, but we suggest it is feasible and could greatly advance our understanding of how anuran larvae have evolved. The same strategies for studying tadpoles that we encourage here can be applied to the study of many other small and fast-moving animals.     

Embryogenomics: developmental biology meets genomics

Fundamental questions in developmental biology are: what genes are expressed, where and when they are expressed, what is the level of expression and how are these programs changed by the functional and structural alteration of genes? These questions have been addressed by studying one gene at a time, but a new research field that handles many genes in parallel is emerging. The methodology is at the interface of large-scale genomics approaches and developmental biology. Genomics needs developmental biology because one of the goals of genomics – collection and analysis of all genes in an organism – cannot be completed without working on embryonic tissues in which many genes are uniquely expressed. However, developmental biology needs genomics – the high-throughput approaches of genomics generate information about genes and pathways that can give an integrated view of complex processes. This article discusses these new approaches and their applications to mammalian developmental biology.     

进化细胞生物学的提出及其任务

作者提出应创建一门源于进化生物学与细胞生物学两者的交叉学科一进化细胞生物学(细胞的进化生物学)。其根本任务在于用进化的观点考察真核细胞的一切方面,从它们的起源和演化来认识它们的现在。文中列举了其具体的研究内容,并分析了其研究方法上的特点,指出在这里需要把进化生物学的综合性分析与细胞生物学的实验研究最紧密地结合起来。文中还论述了真核细胞的细胞器的“不进化”现象,指出其根本原因在于进化焦点的转移。     

Bioinformatics meets user-centred design: a perspective

Designers have a saying that "the joy of an early release lasts but a short time. The bitterness of an unusable system lasts for years." It is indeed disappointing to discover that your data resources are not being used to their full potential. Not only have you invested your time, effort, and research grant on the project, but you may face costly redesigns if you want to improve the system later. This scenario would be less likely if the product was designed to provide users with exactly what they need, so that it is fit for purpose before its launch. We work at EMBL-European Bioinformatics Institute (EMBL-EBI), and we consult extensively with life science researchers to find out what they need from biological data resources. We have found that although users believe that the bioinformatics community is providing accurate and valuable data, they often find the interfaces to these resources tricky to use and navigate. We believe that if you can find out what your users want even before you create the first mock-up of a system, the final product will provide a better user experience. This would encourage more people to use the resource and they would have greater access to the data, which could ultimately lead to more scientific discoveries. In this paper, we explore the need for a user-centred design (UCD) strategy when designing bioinformatics resources and illustrate this with examples from our work at EMBL-EBI. Our aim is to introduce the reader to how selected UCD techniques may be successfully applied to software design for bioinformatics.     

Advancing signaling networks through proteomics

Healthful physiology can be distinguished from unhealthful physiology by focusing upon how a given signal transduction pathway is shifted as a function of disease. In order to distinguish between pathways that contribute to normal versus disease biology, it is necessary to identify components that comprise a protein module. The development of methods that target such differences is essential for the identification, development and validation of biomarkers that can improve the quality of diagnoses and treatment of disease. This review discusses the use of proteomic methods that integrate cell biology, mass spectrometry and bioinformatics, in relation to the analyses of protein signaling modules that are subject to differential phosphorylation. We examine how these methods can be used to distinguish abnormal from normal physiology.     

Advancing signaling networks through proteomics

Healthful physiology can be distinguished from unhealthful physiology by focusing upon how a given signal transduction pathway is shifted as a function of disease. In order to distinguish between pathways that contribute to normal versus disease biology, it is necessary to identify components that comprise a protein module. The development of methods that target such differences is essential for the identification, development and validation of biomarkers that can improve the quality of diagnoses and treatment of disease. This review discusses the use of proteomic methods that integrate cell biology, mass spectrometry and bioinformatics, in relation to the analyses of protein signaling modules that are subject to differential phosphorylation. We examine how these methods can be used to distinguish abnormal from normal physiology.     

How to Identify and Interpret Evolutionary Tree Diagrams

Evolutionary trees are key tools for modern biology and are commonly portrayed in textbooks to promote learning about biological evolution. However, many people have difficulty in understanding what evolutionary trees are meant to portray. In fact, some ideas that current professional biologists depict with evolutionary trees are neither clearly defined nor conveyed to students. To help biology teachers and students learn how to more deeply interpret, understand and gain knowledge from diagrams that represent ancestor–descendant relationships and evolutionary lineages, we describe the different rooted and unrooted evolutionary tree visualisations and explain how they are best read. Examples from a study of tree-shaped diagrams in the journal Science are used to illustrate how to distinguish evolutionary trees from other tree-shaped representations that are easily misunderstood as visualising evolutionary relationships. We end by making recommendations for how our findings may be implemented in teaching practice in this important area of biology education.     

Creating parts that allow for rational design: Synthetic biology and the problem of context-sensitivity

The parts-based engineering approach in synthetic biology aims to create pre-characterised biological parts that can be used for the rational design of novel functional systems. Given the context-sensitivity of biological entities, a key question synthetic biologists have to address is what properties these parts should have so that they give a predictable output even when they are used in different contexts. In the first part of this paper I will analyse some of the answers that synthetic biologists have given to this question and claim that the focus of these answers on parts and their properties does not allow us to tackle the problem of context-sensitivity. In the second part of the paper, I will argue that we might have to abandon the notions of parts and their properties in order to understand how independence in biology could be achieved. Using Robert Cummins’ account of functional analysis, I will then develop the notion of a capacity and its condition space and show how these notions can help to tackle the problem of context-sensitivity in biology.     

Meaning in Nature: Organic Manufacture?

The paper examines Marcello Barbieri’s (2007) Introduction to Biosemiotics. Highlighting debate within the biosemiotic community, it focuses on what the volume offers to those who explain human intellect in relation to what Turing called our ‘physical powers.’ In scrutinising the basis of world-modelling, parallels and contrasts are drawn with other work on embodied-embedded cognition. Models dominate biology. Is this a qualitative fact or does it point to biomechanisms? In evaluating the 18 contributions, it is suggested that the answers will shape the field. First, they will decide if biochemistry and explanatory reduction can be synergised by biosemantics. Second, they will show if our intellectual powers arise from biology. Does thinking use—not a language faculty—but what Marko? and colleagues call semiosis by the living? Resolution of such issues, it is suggested, can change how we view cognition. Above all, if the biomechanists win the day, cultural models can be regarded as extending natural meaning. On such a view, biomechanisms prompt us to act and perceive as we model our own natural models. This fits Craik’s vision: intellect gives us the alphanumerical ‘symbols’ that allow thoughts to have objective validity. For the biomechanist, this is explained—not by brains alone—but, rather, by acting under the constraints of historically extended sensoria.     

Finding new clock components: past and future

The molecular mechanism of circadian clocks has been unraveled primarily by the use of phenotype-driven (forward) genetic analysis in a number of model systems. We are now in a position to consider what constitutes a clock component, whether we can establish criteria for clock components, and whether we have found most of the primary clock components. This perspective discusses clock genes and how genetics, molecular biology, and biochemistry have been used to find clock genes in the past and how they will be used in the future.     

Thriving in Multidisciplinary Research: Advice for New Bioinformatics Students

The sciences have seen a large increase in demand for students in bioinformatics and multidisciplinary fields in general. Many new educational programs have been created to satisfy this demand, but navigating these programs requires a non-traditional outlook and emphasizes working in teams of individuals with distinct yet complementary skill sets. Written from the perspective of a current bioinformatics student, this article seeks to offer advice to prospective and current students in bioinformatics regarding what to expect in their educational program, how multidisciplinary fields differ from more traditional paths, and decisions that they will face on the road to becoming successful, productive bioinformaticists.     

A review of bioinformatics education in the UK

If the completion of the first draft of the human genome represents the coming of age of bioinformatics, then the emergence of bioinformatics as a university degree subject represents its establishment. In this paper bioinformatics as a subject for formal study is discussed, rather than as a subject for research, and a selection of the taught, mainly graduate, courses currently available in the UK are reviewed. Throughout, the author tries to draw parallels between the integration of bioinformatics into biomedical research and teaching today, and that of molecular biology, two decades ago. Others have made this analogy between these two relatively young disciplines. Although research sources are referenced, the author makes no pretence of objectivity. This article contains his opinions, and those of a number of current bioinformatics course organisers whose comments on the subject were solicited in advance specifically for this paper. The course organisers kindly advised how they planned their curricula, and described the special strengths of their programmes. Comments from present and former students of several bioinformatics degree programmes were also solicited. Except where individuals are directly quoted, any opinions expressed herein should be considered the author's. Compared with its sister piece [Marion Zatz, in previous issue of Briefings in Bioinformatics pp. 353], this paper is less about funding policy--which, in the UK, has lately (if belatedly) been more generous towards bioinformatics teaching--than it is about practice and content; the requirements of the bioinformatics research communities, the corresponding emphases of bioinformatics courses, and the general market for holders of bioinformatics degrees. Individual courses are cited throughout as examples, but the final section contains a full annotated listing with URL addresses. Based on the author's own experience of practising and teaching bioinformatics, he describes the skills he believes will be most useful to bioinformaticians in the near future and suggests ways to prepare students of bioinformatics for a fall in demand for those abilities.     

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed 'omics' technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A 'system' approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with 'system approaches' in animal sciences, providing exciting opportunities to predict and modulate animal traits.     

Proteome signatures—how are they obtained and what do they teach us?

The dawn of a new Proteomics era, just over a decade ago, allowed for large-scale protein profiling studies that have been applied in the identification of distinctive molecular cell signatures. Proteomics provides a powerful approach for identifying and studying these multiple molecular markers in a vast array of biological systems, whether focusing on basic biological research, diagnosis, therapeutics, or systems biology. This is a continuously expanding field that relies on the combination of different methodologies and current advances, both technological and analytical, which have led to an explosion of protein signatures and biomarker candidates. But how are these biological markers obtained? And, most importantly, what can we learn from them? Herein, we briefly overview the currently available approaches for obtaining relevant information at the proteome level, while noting the current and future roles of both traditional and modern proteomics. Moreover, we provide some considerations on how the development of powerful and robust bioinformatics tools will greatly benefit high-throughput proteomics. Such strategies are of the utmost importance in the rapidly emerging field of immunoproteomics, which may play a key role in the identification of antigens with diagnostic and/or therapeutic potential and in the development of new vaccines. Finally, we consider the present limitations in the discovery of new signatures and biomarkers and speculate on how such hurdles may be overcome, while also offering a prospect for the next few years in what could be one of the most significant strategies in translational medicine research.