Search

There are emerging concerns in biomedical science regarding the limited success in reproducing research data and translating them into applications. This scenario is a major problem for academic science as well as for the economy and the society at large which benefit from research findings. Excluding fraud, the reasons for this might be found in the lack of identification and application of standards, poor description and sharing of data, protocols and procedures, and underdevelopment of quality control activities. Driven by stakeholders and scientific networks, several attempts to face the reproducibility crisis have been initiated. In this chapter we discuss the actions implemented into the academic research environment, as well as the difficulties encountered due principally to the limitation of resources. Our aim is to highlight how the identification and adoption of best practice for Quality and Data Management are key issues in this battle, and to propose a roadmap for their implementation, based on the involvement of all the interested actors, such as the academic community, research agencies and government programs.

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.

The completion of the human genome sequence triggered worldwide efforts to unravel the secrets hidden in its deceptively simple code. Numerous bioinformatics projects were undertaken to hunt for genes, predict their protein products, function and post-translational modifications, analyse protein-protein interactions, etc. Many novel analytic and predictive computer programmes fully optimised for manipulating human genome sequence data have been developed, whereas considerably less effort has been invested in exploring the many thousands of other available genomes, from unicellular organisms to plants and non-human animals. Nevertheless, a detailed understanding of these organisms can have a significant impact on human health and well-being.New advances in genome sequencing technologies, bioinformatics, automation, artificial intelligence, etc., enable us to extend the reach of genomic research to all organisms. To this aim gather, develop and implement new bioinformatics solutions (usually in the form of software) is pivotal. A helpful model, often used by the bioinformatics community, is the so-called hackathon. These are events when all stakeholders beyond their disciplines work together creatively to solve a problem. During its runtime, the consortium of the EU-funded project AllBio - Broadening the Bioinformatics Infrastructure to cellular, animal and plant science - conducted many successful hackathons with researchers from different Life Science areas. Based on this experience, in the following, the authors present a step-by-step and standardised workflow explaining how to organise a bioinformatics hackathon to develop software solutions to biological problems.

On the 2 March 2020, the COST Action - Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research (CHARME) - held its final conference in BrusselsAfter four years of successful work, the members of the COST Action CHARME met in Brussels to summarise the achievements and to discuss future perspectives and challenges for standardisation in the life sciences.Following the motto "Standards make the world go round", the outcomes of the COST Action are manifold and introduced some basic concepts and definitions that support a better understanding of the challenges and requirements.The results of this COST Action's network will be subject of a White Paper addressing the needs of standardisation, including a catalogue of requirements and recommendations to be disseminated to decision-makers at all levels to enable the implementation of standards in the daily workflow of research in academia and industry.The Action, ending this month, has given the opportunity to its members to tentatively presenting a set of preliminary requirements to develop further the harmonisation of standards. We hope this will inspire other Actions for the future.

Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation., Zweitver��ffentlichungen der Universit��t Potsdam : Mathematisch-Naturwissenschaftliche Reihe; 1201

Modern, high-throughput methods for the analysis of genetic information, gene and metabolic products and their interactions offer new opportunities to gain comprehensive information on life processes. The data and knowledge generated open diverse application possibilities with enormous innovation potential. To unlock that potential skills in generating but also properly annotating the data for further data integration and analysis are needed. The data need to be made computer readable and interoperable to allow integration with existing knowledge leading to actionable biological insights. To achieve this, we need common standards and standard operating procedures as well as workflows that allow the combination of data across standards. Currently, there is a lack of experts who understand the principles and possess knowledge of the principles and relevant tools. This is a major barrier hindering the implementation of FAIR (findable, accessible, interoperable and reusable) data principles and the actual reusability of data. This is mainly due to insufficient and unequal education of the scientists and other stakeholders involved in producing and handling big data in life science that is inherently varied and complex in nature, and large in volume. Due to the interdisciplinary nature of life science research, education within this field faces numerous hurdles including institutional barriers, lack of local availability of all required expertise, as well as lack of appropriate teaching material and appropriate adaptation of curricula.

Open Science describes the ongoing transitions in the way research is performed, i.e. researchers collaborate, knowledge is shared, and science is organized. It is driven by digital technologies and by the enormous growth of data, globalization, enlargement of the scientific community and the need to address societal challenges [23]. It has now widely been recognized that making research results more accessible to all societal actors contributes to better and more efficient science, as well as to innovation in the public and private sectors [1, 17]. However, the reuse of research results can only be achieved reliably and efficiently, if these data are valorized in a specific manner. Data are to be generated, formatted and stored according to Standard Operating Procedures (SOPs) and according to sophisticated Data Management Plans [23]. Hence, to generate accurate and reproducible data sets, to allow interlaboratory comparisons as well as further and future use of research data it is mandatory to work in line with good laboratory practices and well-defined and validated methodologies. Within this article, members of the Cost Action CHARME [10] will discuss aspects of quality management and standardization in context with Open Access (OA) efforts. We will address the question: Are Standardization and Quality Management measures in life-science research crucially needed or introduce further unwanted means of regulation?.

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master’s level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master’s programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student’s ability to phrase and communicate research questions in such a manner that they can be solved by the integration of experiments and modelling, as well as to communicate and collaborate productively across different experimental and theoretical disciplines in research and development., npj Systems Biology and Applications, 2, ISSN:2056-7189

Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome.

The dynamics of a proteome can only be addressed with large-scale, high-throughput methods. To cope with the inherent complexity, techniques based on targeted quantification using proteotypic peptides are arising. This is an essential systems biology approach; however, for the exploratory discovery of unexpected markers, nontargeted detection of proteins, and protein modifications is indispensable. We present a rapid label-free shotgun proteomics approach that extracts relevant phenotype-specific peptide product ion spectra in an automated workflow without prior identification. These product ion spectra are subsequently sequenced with database search and de novo prediction algorithms. We analyzed six potato tuber cultivars grown on three plots of two geographically separated fields in Germany. For data mining about 1.5 million spectra from 107 analyses were aligned and statistically examined in approximately 1 day. Several cultivar-specific protein markers were detected. Based on de novo-sequencing a dominant protein polymorphism not detectable in the available EST-databases was assigned exclusively to a specific potato cultivar. The approach is applicable to organisms with unsequenced or incomplete genomes and to the automated extraction of relevant mass spectra that potentially cannot be identified by genome/EST-based search algorithms.

On March 21st, representatives from 26 countries met in Brussels to execute the kick-off meeting of the new COST Action (Cooperation in Science and Technology) CA15110: "Harmonising standardisation strategies to increase efficiency and competitiveness of European lifescience research (CHARME)". The participants exchanged information about the need for understanding formats and standards for biological data and computer models in systems biology research, and elected Chair, Vice Chair and working group leaders.

Pyrimidine nucleotides are of general importance for many aspects of cell function, but their role in the regulation of biosynthetic processes is still unclear. In this study, we investigate the influence of a decreased expression of UMP synthase (UMPS), a key enzyme in the pathway of de novo pyrimidine synthesis, on biosynthetic processes in growing potato (Solanum tuberosum) tubers. Transgenic plants were generated expressing UMPS in the antisense orientation under the control of the tuber-specific patatin promoter. Lines were selected with markedly decreased expression of UMPS in the tubers. Decreased expression of UMPS restricted the use of externally supplied orotate for de novo pyrimidine synthesis in tuber tissue, whereas the uridine-salvaging pathway was stimulated. This shift in the pathways of UMP synthesis was accompanied by increased levels of tuber uridine nucleotides, increased fluxes of [14C]sucrose to starch and cell wall synthesis, and increased amounts of starch and cell wall components in the tubers, whereas there were no changes in uridine nucleotide levels in leaves. Decreased expression of UMPS in tubers led to an increase in transcript levels of carbamoylphosphate synthase, uridine kinase, and uracil phosphoribosyltransferase, the latter two encoding enzymes in the pyrimidine salvage pathways. Thus, the results show that antisense inhibition of the de novo pathway of pyrimidine synthesis leads to a compensatory stimulation of the less energy-consuming salvage pathways, probably via increased expression and activity of uridine kinase and uracil phosphoribosyltransferase. This results in increased uridine nucleotide pool levels in tubers and improved biosynthetic performance.

In the present paper we investigated the effect of heterologous expression of a rat liver ketohexokinase in potato (Solanum tuberosum L.) plants with the aim of investigating the role of fructose 1-phosphate in plant metabolism. Plants were generated that contained appreciable activity of ketohexokinase but did not accumulate fructose 1-phosphate. They were, however, characterised by a severe growth retardation and abnormal leaf development. Studies of (14)CO(2) assimilation and metabolism, and of the levels of photosynthetic pigments, revealed that these lines exhibited restricted photosynthesis. Despite this fact, the levels of starch and soluble sugars remained relatively constant. Analysis of intermediates of starch and sucrose biosynthesis revealed large increases in the triose phosphate and fructose 1,6-bisphosphate pools but relatively unaltered levels of inorganic phosphate and 3-phosphoglycerate, and these lines were also characterised by an accumulation of glyceraldehyde. The transformants neither displayed consistent changes in the activities of Calvin cycle enzymes nor in enzymes of sucrose synthesis but displayed a metabolic profile partially reminiscent of that brought about by end-product limitation, but most likely caused by an inhibition of photosynthesis brought about by the accumulation of glyceraldehyde. Analysis of the metabolite contents in lamina and vein fractions of the leaf, and of the enzymes of carbohydrate oxidation indicate that the phloem-enriched veins of ketohexokinase-expressing leaves tend toward hypoxia and indicate a problem of phloem transport.

Potato plants (Solanum tuberosum L., cv. Désirée) were transformed with the polyphosphate kinase gene from Escherichia coli fused to the leader sequence of the ferredoxin oxidoreductase gene (FNR) from Spinacea oleracea under the control of the leaf specific St-LS1 promoter to introduce a novel phosphate pool in the chloroplasts of green tissues. Transgenic plants (cpPPK) in tissue culture developed necrotic lesions in older leaves and showed earlier leaf senescence while greenhouse plants showed no noticeable phenotype. Leaves of cpPPK plants contained less starch but higher concentrations of soluble sugars. The presence of polyphosphate in cpPPK leaves was demonstrated by toluidine blue staining and unambiguously verified and quantified by in vitro 31P-NMR of extracts. Polyphosphate accumulated during leaf development from 0.06 in juvenile leaves to 0.83 mg P g-1 DW in old leaves and had an average chain length of 18 residues in mature leaves. In situ 31P-NMR on small leaf pieces perfused with well-oxygenated medium showed only 0.036 mg P g-1 DW polyphosphate that was, however, greatly increased upon treatment with 50 mM ammonium sulfate at pH 7.3. This phenomenon along with a yield of 0.47 mg P g-1 DW polyphosphate from an extract of the same leaf material suggests that 93% of the polyphosphate pool is immobile. This conclusion is substantiated by the observation that no differences in polyphosphate pool sizes could be discerned between darkened and illuminated leaves, leaves treated with methylviologen or anaerobis and control leaves, treatments causing a change in the pool of ATP available for polyPi synthesis. Results are discussed in the context of the chelating properties of polyphosphates for cations and its consequences for the partitioning of photoassimilate between starch and soluble sugars.

The IT Future of Medicine (ITFoM, http://www.itfom.eu/ ) initiative will produce computational models of individuals to enable the prediction of their future health risks, progression of diseases and selection and efficacy of treatments while minimising side effects. To be able to move our health care system to treat patients as individuals rather than as members of larger, divergent groups, the ITFoM initiative, proposes to integrate molecular, physiological and anatomical data of every person in ‘virtual patient’ models. The establishment of such ‘virtual patient’ models is now possible due to the enormous progress in analytical techniques, particularly in the ‘-omics’ technology areas and in imaging, as well as in sensor technologies, but also due to the immense developments in the ICT field. As one of six Future and Emerging Technologies (FET) Flagship Pilot Projects funded by the European Commission, ITFoM with more than 150 academic and industrial partners from 34 countries, will foster the development in functional genomics and computer technologies to generate ‘virtual patient’ models to make them available for clinical application. The increase in the capacity of next generation sequencing systems will enable the high-throughput analysis of a large number of individuals generating huge amounts of genome, epigenome and transcriptome data, but making it feasible to apply deep sequencing in the clinic to characterise not only the patient's genome, but also individual samples, for example, from tumours. The genome profile will be integrated with proteome and metabolome information generated via new powerful chromatography, mass spectrometry and nuclear magnetic resonance techniques. The individualised model will not only enable the analysis of the current situation, but will allow the prediction of the response of the patient to different therapy options or intolerance for certain drugs.

Molecular medicine is undergoing fundamental changes driving the whole area towards a revolution in modern medicine. The breakthrough was generated the fast-developing technologies in molecular biology since the first draft sequence of the human genome was published. The technological advances enabled the analysis of biological samples from cells and organs to whole organisms in a depth that was not possible before. These technologies are increasingly implemented in the medical and health care system to study diseases and refine diagnostics. As a consequence, the understanding of diseases and the health status of an individual patient is now based on an enormous amount of data that can only be interpreted in the context of the body as a whole. Systems biology as a new field in the life sciences develops new approaches for data integration and interpretation. Systems medicine as a specialized aspect of systems biology combines in an interdisciplinary approach all expertise necessary to decipher the human body in all its complexity. This created new challenges in the area of information and communication technologies to provide the infrastructure and technology needed to cope with the data flood that will accompany the next generation of medicine. The new initiative 'IT Future of Medicine' aims at driving this development even further and integrates not only molecular data (especially genomic information), but also anatomical, physiological, environmental, and lifestyle data in a predictive model approach-the 'virtual patient'-that will allow the clinician or the general practitioner to predict and anticipate the optimal treatment for the individual patient. The application of the virtual patient model will allow truly personalized medicine.

Molecular medicine is undergoing fundamental changes driving the whole area towards a revolution in modern medicine. The breakthrough was generated the fast-developing technologies in molecular biology since the first draft sequence of the human genome was published. The technological advances enabled the analysis of biological samples from cells and organs to whole organisms in a depth that was not possible before. These technologies are increasingly implemented in the medical and health care system to study diseases and refine diagnostics. As a consequence, the understanding of diseases and the health status of an individual patient is now based on an enormous amount of data that can only be interpreted in the context of the body as a whole. Systems biology as a new field in the life sciences develops new approaches for data integration and interpretation. Systems medicine as a specialized aspect of systems biology combines in an interdisciplinary approach all expertise necessary to decipher the human body in all its complexity. This created new challenges in the area of information and communication technologies to provide the infrastructure and technology needed to cope with the data flood that will accompany the next generation of medicine. The new initiative 'IT Future of Medicine' aims at driving this development even further and integrates not only molecular data (especially genomic information), but also anatomical, physiological, environmental, and lifestyle data in a predictive model approach-the 'virtual patient'-that will allow the clinician or the general practitioner to predict and anticipate the optimal treatment for the individual patient. The application of the virtual patient model will allow truly personalized medicine.

Systems biology requires the availability, co-ordination and simultaneous interaction of a large number of diverse facilities and activities. These cover an entire spectrum, from mathematical modelling, through biological, biomedical and clinical experiments, to dedicated technology development. The systems biology community needs close cooperation with data-generation groups and bioinformaticians to define a strategy for producing life-science data of sufficiently high quality for model generation. For each medical, biological or biotechnological problem addressed, the optimal combination of facilities and activities is likely to be different. The complexity of biological systems, and the diversity and dynamics of their processes, means that a full analysis is far too complex to be handled by a single entity, industry or country – a variety of specialist expertise and facilities are typically necessary to achieve results suitable for modelling. Systems-level approaches for tackling the complexity of life-science data provide a profound conceptual advance compared to reductionist biological research methods of the past. Rather than focusing on individual laboratories, specialising in a limited number of research technologies, the Infrastructure for Systems Biology in Europe (ISBE) will facilitate the synergistic application of a wide range of research techniques and technologies to problems of major medical and biotechnological importance.

Three cDNAs encoding purple acid phosphatase (PAP) were cloned from potato (Solanum tuberosum L. cv. Desiree) and expression of the corresponding genes was characterised. StPAP1 encodes a low-molecular weight PAP clustering with mammalian, cyanobacterial, and other plant PAPs. It was highly expressed in stem and root and its expression did not change in response to phosphorus (P) deprivation. StIPAP2 and StPAP3 code for high-molecular weight PAPs typical for plants. Corresponding gene expression was shown to be responsive to the level of P supply, with transcripts of StPAP2 and StPAP3 being most abundant in P-deprived roots or both stem and roots, respectively. Root colonisation by arbuscular mycorrhizal fungi had no effect on the expression of any of the three PAP genes. StIPAP1 mRNA is easily detectable along the root axis, including root hairs, but is barely detectable in root tips. In contrast, both StPAP2 and StPAP3 transcripts are abundant along the root axis, but absent in root hairs, and are most abundant in the root tip. All three PAPs described contain a predicted N-terminal secretion signal and could play a role in extracellular P scavenging, P mobilisation from the rhizosphere, or cell wall regeneration

Modern life sciences are facing a rapidly increasing amount of data produced worldwide. The diversity and heterogeneity of the data calls for standardized formats for the data itself, its description and its context (the metadata), as well as for the processes during generation and processing of the data, the Standard Operating Procedures (SOPs). The newly established project “NORM-SYS - Normalization and standardization for the exchange of models and data in systems biology research” aims at harmonizing standardization processes for model and data exchange in systems biology.

Starch represents the most important carbohydrate used for food and feed purposes. With the aim of increasing starch content, we decided to modulate the adenylate pool by changing the activity of the plastidial adenylate kinase in transgenic potato plants. As a result, we observed a substantial increase in the level of adenylates and, most importantly, an increase in the level of starch to 60% above that found in wild-type plants. In addition, concentrations of several amino acids were increased by a factor of 2-4. These results are particularly striking because this genetic manipulation also results in an increased tuber yield. The modulation of the plastidial adenylate kinase activity in transgenic plants therefore represents a potentially very useful strategy for increasing formation of major storage compounds in heterotrophic tissues of higher plants.

Sequencing has seen major breakthroughs in recent years and has paved the way for developing novel life-science applications for the life sciences. Consequently, the life sciences are facing a rapidly increasing demand for data handling capacity. The workshop “ICT needs and challenges for Big Data in the Life Sciences” invited experts from different areas of the life sciences, bioinformatics and systems biology across Europe to discuss the topic. The aim was to identify and describe current and future ICT needs from sample analysis to model generation.