Level 7 (at end of programme) equivalent to Masters level competence
30 months duration including EPA
£18,000 maximum funding cap
available from Apprenticeship Levy
Bioinformatics Scientist Level 7
Specialists who use computational, data analytical and data mining techniques which are applied to a range of problems in the life sciences.
Bioinformaticians are scientists – specialists who use computational, data analytical and data mining techniques which are applied to a range of problems in the life sciences, for example, in pharmaceutical companies in the process of drug discovery and development. Roles require scientists who understand life sciences, and who can work computationally with diverse and large volumes of data derived from different life science activities – and role names and descriptions often reflect this by using slightly different names for what is broadly the same computational skill-set. For example, bioinformatics, computational biology, computational toxicology, Health informatics, Medical informatics, Agri-informatics. This range of titles reflect the importance of life-science-specific knowledge coupled with the underlying (and sometimes specifically-adapted) data science, statistics and computational skills.
Broadly, bioinformatics is: Research, development, or application of computational tools and approaches for expanding the use of life science, (inc. biological, chemical or health) data, including those to acquire, store, organise, archive, analyse, or visualise such data; in such a way that aids development and application of data-analytical and theoretical methods, mathematical modelling and computational simulation techniques to the study of such biological systems.
Typical job roles: A bioinformatician is often part of a collaborative group or team of scientists, drawing together life scientists, statisticians and computational infrastructure specialists. Consequently, the bioinformatician must be able to work across these disciplinary boundaries.
- A topic aligned with the life science field, and the core experimental platform/data generatingtechnologies in the chosen field.
- How research is conducted in bioinformatics and within the broader context of interdisciplinary lifesciences.
- The technical limitations and the underlying biological and experimental assumptions that impact ondata quality.
- Details of omic-scale/big-data-driven life science making use of core platform technologies.
- The responsibilities of working in production/industry environments managing scientific data –including regulated environments (good practice, and IP/confidentiality requirements).
- Current approaches for modelling and warehousing of life science data.
- Requirements for responsible, legal or ethical access and use of biological data, including generaldata protection (GDPR) considerations, identifiable personal genomic & healthcare data, andgeographic biodiversity-related data concerns.
- Ontologies and their use.
- Retrieval and manipulation of biological data, including data mining, from public repositories.
- Techniques to integrate, interpret, analyse and visualise biological data sets.
- Bioinformatics analysis methodologies and expertise in common bioinformatics software packages,tools and algorithms – including workflow management tools.
- Common bioinformatics programming languages; algorithm design, analysis and testing.
- The use of suitable version control tools, software sustainability practices and open source softwarerepositories.
- Licensing limitations on the use of bioinformatics software and data such as open source,commercial and academic usage restrictions.
- Database design and management, including information security considerations and big-datatechnologies.
- Relevant big-data and high performance computing platforms including Linux/Unix, local andremote High Performance Computing (HPC), and cloud computing.
- Application of statistics in the contexts of bioinformatics and life science data analysis.
- Statistical and mathematical modelling methods, and key scientific and statistical analysis softwarepackages.
- General data science approaches to life science problems, such as machine learning and artificialintelligence (AI).20.
- The importance of data governance, curation, information architecture and ensuringinteroperability.
- Differences in the knowledge-base of diverse audiences, and the most appropriate means ofeffectively communicating scientific and technical information.
- Communication models and techniques which can be employed in a collaborative researchenvironment to effect change at individual, team and organisational level eg. active listening skills,teamworking, influencing and negotiation skills.
- Work with multi-disciplinary colleagues to design life-science experiments that will generate datasuitable for subsequent bioinformatics analysis
- Provide guidance to experimental scientists on data generation methodology and handling toensure the quality of data produced.
- Recognise and critically review the format, scope and limitations of different biological data.
- Define the required metadata to be collected for specific datatypes and analytical approaches.
- Design and implement appropriate data storage formats and associated database structure.
- Choose appropriate computational infrastructure and database solutions – including internal orexternal/cloud resources.
- Store and analyse data in accordance with ethical, legal and commercial standards, includingchecking who has access.
- Curate biological data using suitable metadata, ontologies and/or controlled vocabularies.
- Make use of suitable programming languages and/or workflow tools to automate data handling andcuration tasks.
- Maintain a working knowledge of a range of public data repositories for biological data.
- Prepare data for submission to appropriate public bioinformatics data repositories as required,being aware of IP and/or ethical and legal issues.
- Carry out data pre-processing and quality control (QC) to prepare datasets for bioinformaticsanalysis.
- Determine the best method for bioinformatics analysis, including the selection of statistical tests,considering the research question and limitations of the experimental design.
- Identify and define appropriate computing infrastructure requirements for the analysis of suchbiological data.
- Apply a range of current techniques, skills and tools (including programming languages) necessaryfor computational biology practice – and;
- Contribute to (where appropriate, lead) research to develop novel methodology.
- Build and test analytical pipelines, or write and test new algorithms as necessary for the analysis ofbiological data.
- Document all data processing, analysis and implementation of new methods in accordance withgood scientific practices and industry requirements for regulatory process and IP.
- Interpret the results of bioinformatics analysis in the context of the experimental design and, wherenecessary, in a broader biological context through integration with complementary (often public) data.
- Obtain data sets from private and/or public resources – considering any legal, privacy or ethicalaspects of data use.
- Carry out the analysis of biological data using appropriate programmatic methods, statistical andother quantitative and data integration approaches – and visualise results.
- Communicate and disseminate bioinformatics analysis and results to a range of audiences,including multi-disciplinary scientific colleagues, non-scientific members of management, externalcollaborators and stakeholders, grant/funding bodies and the public as required.
- Supervise and mentor colleagues and peers to develop bioinformatics knowledge relevant to theirspecific life science subject experience.
- Communicate orally and in writing, and collaborate effectively with interdisciplinary scientificcolleagues, and management functions to monitor and manage people, processes or teams.
- Manage their own time through preparation and prioritisation, time management andresponsiveness to change.
- Professional standards in the workplace in relation to: ethics and scientific integrity, legalProfessional legalcompliance and intellectual property, respect and confidentiality, and health and safety.
- The need to continuously develop their knowledge and skills in relation to scientific developmentsthat influence their work, ensuring they continue to provide relevant analyses, including emergingtechniques where appropriate.
- The ongoing need for awareness of technical advances in the broader scientific field that maypresent opportunities for personal and / or organisational development.
- The wider context (policy, economic, societal, technological, legal, cultural and environmental) inwhich scientific research operates, recognising the implications for professional practice.
- The need to be enthusiastic, self-confident, self-aware, empathic, reliable and consistent to operateeffectively in the role.
- The requirement to persevere, have integrity, be prepared to take responsibility, to challenge areasof concern, to lead, mentor and supervise.
- Individual employers will set the selection criteria, however in most cases applicants will have a background in a life sciences subject or informatics/computer science.
- Apprentices without Level 2 English and Maths will need to achieve this level prior to taking the endpoint assessment.
- For those with an education, health and care plan or a legacy statement, the apprenticeship English and Maths minimum requirement is entry Level 3 and British Sign Language qualifications are an alternative to an English qualification for whom this is their primary language.
- At the end of the apprenticeship apprentices will have achieved specific Level 7 occupational competence.
- Following successful completion of endpoint assessment the apprentice will operate as a Bioinformatics Scientist
Training provider analysis
We have a number of ATAC apprentices currently studying this programme at Cranfield.