UK Bioinformatics Landscape

Transcription

1 UK Bioinformatics Landscape

2 Contents Executive summary 1 Rationale 2 Segmentation by application 3 Segmentation by technology 4 Market 4 UK landscape 6 SWOT analysis of UK landscape 7 Main challenges identified through conversations with end-users and service providers 8 Further observations from discussions with the community 12 Booklet Editor: Dr. Alexander Henzing, KTN Ltd. Booklet Design & Infographics: Jonathan Stevens & Hantian Zhang, Starbit Ltd. Printed on 100% recycled paper.

3 Executive summary Omics is a fast moving field, both technologically and in terms of generating high throughput data, creating a high demand for several fundamental aspects of omics and bioinformatics: data standardisation, data sharing, storing and managing data appropriately and exploring Omics data. Market research suggests a strong growth for bioinformatics over the next couple of years, due to its key role in R&D in the life sciences. In particular, data integration and management including big data and cloud computing, whole system approaches, synthetic biology, and the need for predictability of products and processes through in-silico modelling are considered drivers for growth in the industrial and medical biotechnology, pharmaceutical and health markets. Data integration and management of omics data, enabling the use of big data across thematic areas and to facilitate data sharing; In-silico modelling and algorithms to predict toxicity; enhance process understanding and control in manufacturing; and address the gaps in understanding of biological systems, applications and model organisms to guide engineering strategies; Visualisation tools, in particular interactive ones that facilitate exploratory data analysis, higher correlation between analysis and visualisation, more interaction with visualisation of larger data, and parallelisation of algoritms and visualisations; Open innovation, or pre-competitive approaches in particular to data sharing and data standardisation. These opportunities are well placed to make the most of a number of UK strengths, such as the academic expertise in the biological sciences, informatics and bioinformatics; government support for synthetic biology in both the public and private sectors; the continued emphasis on bioinformatics as a driver for systems biology and synthetic biology by the research councils; the colocation of the Wellcome Trust Sanger Institute and European Bioinformatics Institute; the synergy with the Genomics England initiative; and the large and growing end-user market both within the UK and worldwide. Conversations with end-users and bioinformaticians identified a number of challenges and opportunities: UK Bioinformatics Landscape 1

4 Rationale The overaching objective for this exercise is to have a better understanding of the UK bioinformatics capabilities, challenges and opportunities. Providing a useful overview of current UK activity as a result of the increased importance of bioinformatics as an enabler due to increased application of genomics and other omics in the agri-food, biotech, medical and pharmaceutical sectors, underpinning R&D in these areas. Secondly, the identification of industry needs and challenges which can then feed into the Innovate UK Biosciences strategy refresh, and forthcoming funding competitions. Definition / scope Bioinformatics Computer Science Statistics Biology is a data-rich area with studies at cellular and progressively more at subcellular level, generating increasingly large volumes of information. Bioinformatics is an interdisciplinary field of science which develops methods and software tools for storing, retrieving, organizing and analysing biological data. It combines computer science, statistics, mathematics and engineering to study and process biological data (Figure 1). The US National Center for Biotechnology Information recognises three sub-disciplines within bioinformatics: the development of new algorithms and statistics with which to assess relationships among members of large data sets; the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and the development and implementation of tools that enable efficient access and management of different types of information. Innovate UK includes approaches to organising, filtering and interpreting biological data, including biological system modelling, data visualisation, and user centred design in its definition of bioinformatics. Mathematics Engineering Figure 1 : Bioinformatics combines computer science, statistics, mathematics and engineering to study and process biological data. 2 UK Bioinformatics Landscape

5 Segmentation by application There are a large number of data-generating and thus potential application areas for bioinformatic tools to be applied (Figure 2): Computational biology/ mathematical biology Genomics/structural genomics/ comparative genomics/ molecular phylogenetics Pharmacogenomics/genetics/precision medicine/stratified medicine Proteomics and gene expression/ protein-protein interactions Transcriptomics/metabolomics/ metabolic pathway modelling Cheminformatics/chemical informatics/ chemometrics/computational chemistry In-silico ADME-Toxicity prediction Biomedical informatics/e-health Agri-informatics Process modelling/rational design Systems biology/systems medicine/synthetic biology Biophysics Big data/computer science/text mining/data integration/hpc Bioinformatics Tools Figure 2 : There are a large number of potential applications for bioinformatics tools. UK Bioinformatics Landscape 3

6 Segmentation by technology Software Tools & Databases Infrastructure Data analysis Molecular modelling Molecular dynamic Generalized/specialised knowledge bases Workflows/pipelines IT (Cloud, hosting, hardware & software) Market Bioinformatics is considered to be at the forefront of the biotechnology revolution. The bioinformatics market has significantly evolved worldwide due to increasing application of genomics in the industrial biotechnology, medical and pharmaceutical sectors. The rising demand for bioinformatics in genomics, proteomics, transcriptomics, cheminformatics and molecular phylogenetics has created a large commercial market. The true importance of bioinformatics to the industrial and medical biotechnology, synthetic biology, pharmaceutical and other sectors and thus its market size is difficult to quantify as it is often a component of several internal divisions rather than a separate department. However, according to a report from Research and Markets the global bioinformatics market was estimated to be worth around $3.8 billion in 2013, and is anticipated to grow at a CAGR of around 19.3% during , reaching a market size of $9.1 Billion in 2018 (Figure 3). The key factors driving growth of bioinformatics are the drug discovery and R&D sectors and the rapidly decreasing cost of DNA sequencing. Various governments, including the UK, have realized the potential of bioinformatics and are funding initiatives in both the public and private sectors. Increasingly suppliers of bioinformatics applications and solutions are capitalizing on opportunities such as cloud computing. Factors that are perceived to restrain growth in this area are the shortage of skilled bioinformaticians and a growing consolidation within the industry of services, analysis platforms, sequencing hardware and knowledge bases. The bioinformatics platform holds the largest market share and is estimated to account for nearly 50% of the market revenue (Figure 4). The services market currently holds a relatively smaller market share, however 4 UK Bioinformatics Landscape

7 $ 9.1 Billion arena. However, as competition intensifies, strategic partnerships and outsourcing will be the business models that will ensure success. $ 3.8 Billion Figure 3 : The growth of market size from this is expected to increase considerably due to outsourcing of bioinformatic platforms, services and knowledge management, by both the biotech and pharmaceutical sectors according to Transparency Market Research. Currently the North American market is the largest forecasted to be overtaken by Europe in 2018 due to fast growth particularly in the UK and Germany. Thus the number of companies providing software and services is expected to grow as well as global market share and end-user internal resources and capabilities. Genomics is likely to play an increasingly important role in R&D in the synthetic biology, biotech, medical (in particular precision medicine) and pharmaceutical (including diagnostics) sectors, as is the increase in volume of omics data in general. This makes data analysis increasingly arduous for the R&D provider sector, thus creating increasing demand for bioinformatics, in particular for data management tools. One of the key growth areas of bioinformatics includes systems biology modelling, which will be driven by large-scale integration of data and processes across the R&D continuum, fuelled by the trend to move from a reductionist approach to whole system evaluation. The need for predictability of both products and processes is likely to increase demands for algorithms for in-silico modelling. In a report by Global Industry Analysts, biocontent or knowledge bases, represent the largest segment of the bioinformatics market, whereas software is the fastest growing one. According to Frost & Sullivan: the bioinformatics market is positioned to achieve its maximum potential over the next three to seven years because biotechnology companies with bioinformatics operations, software firms in bioinformatics as well as core computer hardware, peripherals and IT companies will diversify into this area and make concerted efforts in order to realise its full value. Strong growth is projected for bioinformatics, which is the key to tackling long-standing problems in the life sciences Bioinformatics Platform Figure 4 : The Bioinformatics Platform holds the largest market share and is estimated to account for nearly 50% of the market revenue. UK Bioinformatics Landscape 5

8 The increasing availability of software, both open source and cloud based offerings, is expected to increase the need for knowledge management and bio-content. UK landscape Whilst in 2002 a DTI report suggested that the main strength of UK bioinformatics lay in agrifood applications more recently the UK government s emphasis has been on health and medicine following recommendations from the Bell report (2012) (Figure 5). This resulted in heavy investment in ELIXIR (EMBL-EBI) and Genomics England as well as recognizing the need of skills-building on the interface of biology and computer science. Genomics England has been recognised as one of 2014 s 50 Smartest Companies in MIT Technology Review s annual list of the world s most innovative technology companies. In the UK, debate around ownership and IP related to bioinformatics has been dominated by the Cambridge-Hinxton based Wellcome Trust Sanger Institute and European Bioinformatics Institute (EBI), both focusing heavily on open source and open access public domain principles. Their early-mover power and standardizing influence has shaped the market for open source software and knowledge base tools. Health Medicine Figure 5 : UK Landscape. Agrifood 6 UK Bioinformatics Landscape

9 SWOT analysis of UK landscape Strengths Weaknesses Academic excellence (biological sciences, informatics & bioinformatics) EBI/ Sanger Institute; (Knowledge) databases; Government support for bioinformatics, systems- and synthetic biology; Genomic England initiative; Large end-user market. Shortage of (quality) bioinformaticians; Informatics/maths poorly understood by biologists; End-use poorly understood by mathematicians & software engineer; Competence gap between computer science and biological sciences; In-house development of specialized software (esp. academically); Commercialisation (conversion of skills & innovation into business); Failure to capitalise on innovation to generate products. Opportunities Threats Potential paradigm shift; Omics fast moving field (both technologically & data creation); Service industry not mature; High demand; Outsourcing will define future of market; Software tools predominantly open-source (less development costs, easier to integrate); Potential to exploit academic knowledge; Education in bioinformatics to be tailored to industry needs; Growth of domestic biotech & other sectors increasing demand. Poor IP protection; US more mature; Low barrier to entry; Academically thriving (difficult to entice to join industry); Financial reward (pay, financial market); Open source nature of software (IP protection, academic competition). UK Bioinformatics Landscape 7

10 Main challenges identified through conversations with end-users and service providers Big data & data management The explosion in data generation, in particular regarding omics, is resulting in research moving from a hypothesis-driven to a data-driven approach characterized by big data. The term Big Data implies large datasets, but volume though inherently useful is not the only aspect that defines Big Data s applicability to the biosciences sector. Velocity, the speed with which new (omics) data is created, and added to existing data; variety, from the different shapes and sizes in which structured data is generated on the various omics platforms, and unstructured data in particular healthcare data (e.g PROM s, patient reported outcome measures) and text-mining from literature; and variability, whether batch-to-batch or population wide as in clinical data, play an equally important role in the integration of data into one big data holistic picture. Thus the central theme of big data is the breakdown of data silos, within companies or between thematic areas to inform future research. However, the aim is not the data integration itself, but integrating different types of data in order to identify trends, patterns and correlations to gain better insight and help with the decision making process. It is therefore imperative to understand what the data is for, its relevance and context within the organization and defining a goal. Integrating data and systems will require knowledge domain experts, data curation, data standards, semantic technologies and ontologies for structuring data within existing IT architecture and processes, as well as the analytics (algorithms) enabling us to connect structure, manage and analyse data to understand pathways at omic and other levels. Sharing information in Healthcare and Life Sciences has the potential to increase system efficiency. However, regulations on datasharing are different around the world when it comes to healthcare or genomic data, thus the legal framework must be taken into consideration. Genome-scale in-silico modelling for industrial biotechnology Advances in high-throughput biological technologies and the growing number of sequenced genomes has driven the construction of in-silico models at genome scale, providing powerful tools for the investigation of biological systems and applications. The use of in silico models is still in its early stages for delivering to industry, but some significant initial successes have been achieved. Genome-scale models can predict engineering strategies to enhance properties of interest in an organism and can provide a basis for rational engineering and synthetic biology in industrial biotechnology. An accurate genome-scale model (GEM) can help predict the system-wide effect of genetic and environmental perturbations on an organism, and hence drive metabolic engineering experiments. Since the 8 UK Bioinformatics Landscape

11 development of the first GEM in 1999 for Haemophilus influenza, systems modelling approaches have worked towards efficiently utilizing increasingly available highthroughput biological data (e.g., genomics, transcriptomics, proteomics, metabolomics) in models. A challenge in this field is enabling rapid development of predictive computational models for any sequenced organism by harnessing these high-throughput experimental technologies. The compelling need for this ability is evidenced by the gap between the number of sequenced organisms and the corresponding models. In-silico toxicity prediction In order to efficiently and effectively assess the risks of existing chemicals as well as new chemical entities, there is an increasing emphasis in the regulatory setting on the use of non- testing methods, either as a supplement to, or as a substitute for, traditional testing methods. Particular emphasis is towards alternatives to animal methods reducing the need for animal testing in pharmacology and toxicology. These methods are based on the premise that the physicochemical properties and biological activities of a chemical depend on its intrinsic nature and can be directly predicted from its molecular structure or inferred from the properties of similar compounds whose activities are known. The use of in-silico methods directed towards the evaluation of safety endpoints, as well as the deployment of chemoinformatics approaches in the analysis of genome responses after exposure to xenobiotics, has been enacted through legislation in the European Union: Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), to help to reduce, refine and replace animal testing. Methods include a range of predictive approaches, including Structure-Activity Relationships (SARs), Quantitative Structure Activity Relationships (QSARs), chemical grouping and read-across, and computerbased tools based on the use of one or more of these approaches. Non-testing UK Bioinformatics Landscape 9

12 methods also include models for predicting the absorption, distribution, metabolism and elimination (ADME) characteristics of chemicals in biological organisms. Data visualisation As a consequence of the rapid evolution of biology and the rate at which new data is being generated there is a growing need to improve the methodology and toolkit required for gaining insight and understanding from complex biological data. Significant advances have been made in automated data analysis through pipelines, however the nature and complexity of large omic datasets means that it is often not known what one is looking for. Visualisation tools, in particular interactive ones that facilitate exploratory data analysis, incorporating the latest advancements in design usability and visualisation principles from other fields of computer science, are critical to generate new observations, extract biological meaning and generate hypotheses for experimental validation from complex biological data. Without visualisation, we depend on numbers, which most often represent summaries or aggregations, statistics, or other computed results. These can hint at structure in the data but are often too precise to show related structures. Although visualisation of biological data can bring insights, these need to be treated as hypotheses that require validation analytically. In general there is no best way to go about visualising biological data, this depends on the data, the task, what it is that one is trying to do and familiarity with the tools. It is all about obtaining better understanding from the data, before learning, interpretation and drivers can occur that lead to better predictive models. There are a number of trends in biological data visualisation, tighter coupling between analysis and visualisation; more interaction with visualisations of larger data; parallelisation of many algorithms and visualisations; and pre-computation of large data sets to save time in the later discovery steps. Process modelling Process modelling is increasingly in demand in relation to biopharmaceuticals and biotechnology products manufacturing, in particular process validation, to ensure that quality, safety and efficacy are built into the product (Quality by Design). Process validation concerns the collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product. Process modelling plays a crucial role over the lifecycle of products and processes from: process design, where the manufacturing process is based on knowledge gained through development and scale-up; process qualification, evaluation to determine if the process is capable of reproducible commercial manufacturing; and continued process verification. Increasing demand for development and application of advanced statistical tools, such as multivariate analysis of complex analytical and process data sets, to support investigations, address questions from regulatory authorities and to enhance process understanding and process control for late stage and commercial biologics manufacturing processes is also playing a part in the understanding and need. 10 UK Bioinformatics Landscape

13 Open innovation / pre-competitive space Open innovation, defined as the process of innovating with others for shared risk and reward to produce mutual benefits, is finding increasing traction with the industrial and medical biotechnology sectors. Initially embraced by the pharmaceutical industry driven by declining productivity, currently a major motivation is the emergence of novel technologies and tools that underpin the entire sector. An open innovation partnership involves sharing ideas, data, tools or intellectual property (IP) with other organisations, therefore requiring mechanisms that allow the value created from the collaboration to be captured by each of the contributors. These partnerships do not necessarily have to be open access, e.g. free of charge, free of copyright and licensing restrictions or open source -free to the wider community rather than the partners- although in particular the latter has traditionally been promulgated by software developers, distributing source code freely. In particular the pre-competitive space is widely recognised as an opportunity to work with others, cross-sector, thus enabling the bridging of cultures. It is recognised however that this willrequire changing the way of thinking about IP. Data sharing is perceived as the largest potential barrier to success in precompetitive collaborations, not in the legal and ethical sense as often cited in the clinical domain although data privacy regulation plays a role, but more regarding data integration and aggregation. Data standardisation has been recognised as an area that will need to be addressed together as an industry. The more data you can integrate does not necessarily result in better insights as this inevitably requires obfuscation, and too much of this may render the entire dataset useless. Potential enablers mooted include safe havens which host public and private data which do not require data to be made public upon completion; API s and search results in series thereby not exposing datasets; precompetitive platform and tools, including enabling SME s to provide tools. Further observations from discussions with the community Omics still (perceived as) separate fields with own set of tools and experts; however tools are becoming available; Move from reductionist to whole system flagged as important. Biological systems are extremely complex with properties that cannot be explained or predicted by studying (individual) parts, reductionist approach underestimates complexity. However, holistic approaches in which everything is connected are not seen as the methodological answer, rather connecting networks and systems through new techniques, tools and software with standardised vocabulary, making effective use of big data to build realistic models of complex systems and validating this using various omic data; Data integration is often mentioned as a prerequisite. However, it is not necessarily the integration of data, which is currently often stored in silo s which is the issue, but the ability to analyse various formats of data (software tools); In-silico prediction is an area mentioned often, in relation to processes, product, validation of omic data and phenotype from genotype. The need to move UK Bioinformatics Landscape 11

14 from constrained based models to genome scale models, incorporating transcription and translation, metabolism and regulatory networks. Observation is that whilst several GEM are available for model organisms a user interface is largely missing? are often not very good at articulating their problem and what they want out of a project; Turnkey services are required, integrating people with diverse bioinformatics skills to serve variety of needs of industry; Several respondents mentioned that publicly available datasets often lack comprehensive metadata required for reanalysis or use as control or validation sets; Bio-informatics people resourcing perception : lack of talented bioinformaticians; bioinformaticians moving to financial sector (monetary pull); academic sector wanting to collaborate as research project vs industry requirement of services; UK strong in software engineers with understanding of biology, not so much in biologists who can program; Bioinformatics services: large companies outsource to US, SME s look towards academic collaborators; Service providers have difficulty in attracting new clients, retention of current clients generally very good. End-user view: difficult to know where to go for help with a particular problem. Whereas the service industry observes that clients User-friendly software required versus skills required for setting parameters and interpreting outputs; Fundamental challenge is access to data, in particular phenotypic (patient) data. Difficulty with the data-sharing permissions, legal, ethical, phenotypic data description often ill defined. Data integration and data privacy regulation is a barrier. Annotation bottleneck. Clinical statistics under par; There is often grant funding available to generate new datasets, but little provision for sharing/distribution, and no provision for data management long term. Data generation is other end point, leading to regeneration of datasets. Data warehousing is not appropriate for translational medicine/informatics; More predictability in discovery, in particular predictive behaviour of proteins (e.g. solubility, aggregation, behaviour in medium (from sequence/models)). 12 UK Bioinformatics Landscape

15 Contact Dr Alexander Henzing Knowledge Transfer Manager

16