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Bioinformatics scientist

Bioinformatics scientist

Health and science

Level 7 - Professional Occupation

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

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Reference: OCC0649

Status: assignment_turned_inApproved occupation

Average (median) salary: £43,283 per year

SOC 2020 code: 2433 Actuaries, economists and statisticians

SOC 2020 sub unit groups:

  • 2433/04 Statistical data scientists
  • 2115/04 Epidemiologists
  • 2115/09 Public health analysts

Technical Education Products

ST0649:

Bioinformatics scientist (degree)

(Level 7)

Approved for delivery

Employers involved in creating the standard:

Unilever, GSK, Medimmune, Astrazeneca, Fujifilm Diosynth, Eagle Genomics, Covance, Envigo, UCB, Kymab, EMBL-European Bioinformatics Institute, Medical Research Council, Genomics England, Association of British Pharmaceutical Industry, Biochemical Society, Royal Society of Biology, Royal Society of Chemistry

Summary

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. 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.

Employers involved in creating the standard:

Unilever, GSK, Medimmune, Astrazeneca, Fujifilm Diosynth, Eagle Genomics, Covance, Envigo, UCB, Kymab, EMBL-European Bioinformatics Institute, Medical Research Council, Genomics England, Association of British Pharmaceutical Industry, Biochemical Society, Royal Society of Biology, Royal Society of Chemistry

Green themes

Financial and Professional Business Services

Typical job titles include:

Bioinformatician

Keywords:

Analytical Science Services
Bioinformatics
Computational
Data
Life Sciences
Science

Knowledge, skills and behaviours (KSBs)

K1: A topic aligned with the life science field, and the core experimental platform/data generating technologies in the chosen field.
K2: How research is conducted in bioinformatics and within the broader context of interdisciplinary life sciences.
K3: The technical limitations and the underlying biological and experimental assumptions that impact on data quality.
K4: Details of omic-scale/big-data-driven life science making use of core platform technologies.
K5: The responsibilities of working in production/industry environments managing scientific data – including regulated environments (good practice, and IP/confidentiality requirements).
K6: Current approaches for modelling and warehousing of life science data.
K7: 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.
K8: Ontologies and their use.
K9: Retrieval and manipulation of biological data, including data mining, from public repositories.
K10: Techniques to integrate, interpret, analyse and visualise biological data sets.
K11: Bioinformatics analysis methodologies and expertise in common bioinformatics software packages, tools and algorithms – including workflow management tools.
K12: Common bioinformatics programming languages; algorithm design, analysis and testing.
K13: The use of suitable version control tools, software sustainability practices and open source software repositories.
K14: Licensing limitations on the use of bioinformatics software and data such as open source, commercial and academic usage restrictions.
K15: Database design and management, including information security considerations and big-data technologies.
K16: Relevant big-data and high performance computing platforms including Linux/Unix, local and remote High Performance Computing (HPC), and cloud computing.
K17: Application of statistics in the contexts of bioinformatics and life science data analysis.
K18: Statistical and mathematical modelling methods, and key scientific and statistical analysis software packages.
K19: General data science approaches to life science problems, such as machine learning and artificial intelligence (AI).
K20: The importance of data governance, curation, information architecture and ensuring interoperability.
K21: Differences in the knowledge-base of diverse audiences, and the most appropriate means of effectively communicating scientific and technical information.
K22: 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.

S1: Work with multi-disciplinary colleagues to design life-science experiments that will generate data suitable for subsequent bioinformatics analysis.
S2: Provide guidance to experimental scientists on data generation methodology and handling to ensure the quality of data produced.
S3: Recognise and critically review the format, scope and limitations of different biological data.
S4: Define the required metadata to be collected for specific datatypes and analytical approaches.
S5: Design and implement appropriate data storage formats and associated database structure.
S6: Choose appropriate computational infrastructure and database solutions - including internal or external/cloud resources.
S7: Store and analyse data in accordance with ethical, legal and commercial standards, including checking who has access.
S8: Curate biological data using suitable metadata, ontologies and/or controlled vocabularies.
S9: Make use of suitable programming languages and/or workflow tools to automate data handling and curation tasks.
S10: Maintain a working knowledge of a range of public data repositories for biological data.
S11: Prepare data for submission to appropriate public bioinformatics data repositories as required, being aware of IP and/or ethical and legal issues.
S12: Carry out data pre-processing and quality control (QC) to prepare datasets for bioinformatics analysis.
S13: Determine the best method for bioinformatics analysis, including the selection of statistical tests, considering the research question and limitations of the experimental design.
S14: Identify and define appropriate computing infrastructure requirements for the analysis of such biological data.
S15: Apply a range of current techniques, skills and tools (including programming languages) necessary for computational biology practice – and;
S16: Contribute to (where appropriate, lead) research to develop novel methodology.
S17: Build and test analytical pipelines, or write and test new algorithms as necessary for the analysis of biological data.
S18: Document all data processing, analysis and implementation of new methods in accordance with good scientific practices and industry requirements for regulatory process and IP.
S19: 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.
S20: Obtain data sets from private and/or public resources – considering any legal, privacy or ethical aspects of data use.
S21: Carry out the analysis of biological data using appropriate programmatic methods, statistical and other quantitative and data integration approaches – and visualise results.
S22: 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.
S23: Supervise and mentor colleagues and peers to develop bioinformatics knowledge relevant to their specific life science subject experience.
S24: Communicate orally and in writing, and collaborate effectively with interdisciplinary scientific colleagues, and management functions to monitor and manage people, processes or teams.
S25: Manage their own time through preparation and prioritisation, time management and responsiveness to change.

B1: Professional standards in the workplace in relation to: ethics and scientific integrity, legal compliance and intellectual property, respect and confidentiality, and health and safety.
B2: 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.
B3: The ongoing need for awareness of technical advances in the broader scientific field that may present opportunities for personal and / or organisational development.
B4: The wider context (policy, economic, societal, technological, legal, cultural and environmental) in which scientific research operates, recognising the implications for professional practice.
B5: The need to be enthusiastic, self-confident, self-aware, empathic, reliable and consistent to operate effectively in the role.
B6: The requirement to persevere, have integrity, be prepared to take responsibility, to challenge areas of concern, to lead, mentor and supervise.

Occupational Progression

This occupational progression map shows technical occupations that have transferable knowledge and skills.

In this map, the focused occupation is highlighted in yellow. The arrows indicate where transferable knowledge and skills exist between two occupations. This map shows some of the strongest progression links between the focused occupation and other occupations.

It is anticipated that individuals would be required to undertake further learning or training to progress to and from occupations. To find out more about an occupation featured in the progression map, including the learning options available, click the occupation.

Progression decisions have been reached by comparing the knowledge and skills statements between occupational standards, combined with individualised learner movement data.

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Level 7

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