Apprenticeship training course

Bioinformatics scientist (degree) (level 7)

There are 2 training providers who offer this course.

Information about Bioinformatics scientist (degree) (level 7)

Specialists who use computational, data analytical and data mining techniques which are applied to a range of problems in the life sciences.

• Knowledge, skills and behaviours:
  View knowledge, skills and behaviours

  Knowledge

  • A topic aligned with the life science field, and the core experimental platform/data generating technologies in the chosen field.
  • How research is conducted in bioinformatics and within the broader context of interdisciplinary life sciences.
  • The technical limitations and the underlying biological and experimental assumptions that impact on data 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 general data protection (GDPR) considerations, identifiable personal genomic & healthcare data, and geographic 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 software repositories.
  • 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-data technologies.
  • Relevant big-data and high performance computing platforms including Linux/Unix, local and remote 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 software packages.
  • General data science approaches to life science problems, such as machine learning and artificial intelligence (AI).
  • The importance of data governance, curation, information architecture and ensuring interoperability.
  • Differences in the knowledge-base of diverse audiences, and the most appropriate means of effectively communicating scientific and technical information.
  • Communication models and techniques which can be employed in a collaborative research environment to effect change at individual, team and organisational level eg. active listening skills, teamworking, influencing and negotiation skills.

  Skills

  • Work with multi-disciplinary colleagues to design life-science experiments that will generate data suitable for subsequent bioinformatics analysis.
  • Provide guidance to experimental scientists on data generation methodology and handling to ensure 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 or external/cloud resources.
  • Store and analyse data in accordance with ethical, legal and commercial standards, including checking 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 and curation 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 bioinformatics analysis.
  • 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 such biological data.
  • Apply a range of current techniques, skills and tools (including programming languages) necessary for 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 of biological data.
  • Document all data processing, analysis and implementation of new methods in accordance with good scientific practices and industry requirements for regulatory process and IP.
  • Interpret the results of bioinformatics analysis in the context of the experimental design and, where necessary, 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 ethical aspects of data use.
  • Carry out the analysis of biological data using appropriate programmatic methods, statistical and other 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, external collaborators and stakeholders, grant/funding bodies and the public as required.
  • Supervise and mentor colleagues and peers to develop bioinformatics knowledge relevant to their specific life science subject experience.
  • Communicate orally and in writing, and collaborate effectively with interdisciplinary scientific colleagues, and management functions to monitor and manage people, processes or teams.
  • Manage their own time through preparation and prioritisation, time management and responsiveness to change.

  Behaviours

  • Professional standards in the workplace in relation to: ethics and scientific integrity, legal compliance and intellectual property, respect and confidentiality, and health and safety.
  • The need to continuously develop their knowledge and skills in relation to scientific developments that influence their work, ensuring they continue to provide relevant analyses, including emerging techniques where appropriate.
  • The ongoing need for awareness of technical advances in the broader scientific field that may present opportunities for personal and / or organisational development.
  • The wider context (policy, economic, societal, technological, legal, cultural and environmental) in which scientific research operates, recognising the implications for professional practice.
  • The need to be enthusiastic, self-confident, self-aware, empathic, reliable and consistent to operate effectively in the role.
  • The requirement to persevere, have integrity, be prepared to take responsibility, to challenge areas of concern, to lead, mentor and supervise.
• Apprenticeship category (sector): Health and science
• Qualification level: 7; Equal to master’s degree
• Course duration: 30 months
• Funding: £18,000 ; Maximum government funding for ; apprenticeship training and assessment costs.
• Job titles include:
  • Bioinformatician

View more information about Bioinformatics scientist (degree) (level 7) from the Institute for Apprenticeships and Technical Education.