Main Responsibilities and Required Skills for a Computational Biologist

data scientist working on a computer

A Computational Biologist is a professional who combines expertise in biology, computer science, and mathematics to tackle complex biological problems using computational methods. They play a crucial role in analyzing vast amounts of biological data, developing models and algorithms, and extracting meaningful insights to advance our understanding of living systems. In this blog post, we describe the primary responsibilities and the most in-demand hard and soft skills for Computational Biologists.

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Main Responsibilities of a Computational Biologist

The following list describes the typical responsibilities of a Computational Biologist:

Analyze

  • Analyze behavioral data as well as genomic data from citizen science projects.

  • Analyze biological data in a statistically rigorous manner.

  • Analyze high-throughput data from pre-clinical studies supporting development of novel IO therapies.

  • Analyze large-scale genomic, proteomic, and metabolomic datasets.

Apply

  • Apply computational techniques for drug discovery and personalized medicine.

  • Apply machine learning techniques to predict biological outcomes.

  • Apply principles of sound statistical design.

  • Apply principles of sound statistical design and develop novel methods when appropriate.

Assist

Assist in the development of new computational algorithms and methodologies.

Build

Build machine learning models.

Coach

Coach junior team members, particularly in software development.

Collaborate with

  • Collaborate with experimental biologists to design and optimize experiments.

  • Collaborate with interdisciplinary teams on research projects.

  • Collaborate with software engineers to develop user-friendly bioinformatics tools.

  • Collaborate with the MGH and Broad Institute single-cell analysis community.

  • Collaborate with translational oncologists on analysis of early stage clinical trials molecular data.

Communicate

  • Communicate research findings through presentations and scientific publications.

  • Communicate the findings to rest of the organization to inform future experiments.

Conduct

  • Conduct data visualization and interpretation to communicate results effectively.

  • Conduct literature reviews to gather relevant information for research projects.

  • Conduct quality control and data quality assessment on biological datasets.

Contribute to

  • Contribute effectively to a shared codebase that can be leveraged broadly for multiple applications.

  • Contribute innovative and novel scientific analyses to cross line platform projects.

  • Contribute to experimental design of high-throughput viral libraries.

  • Contribute to generation of protocols, publications, and intellectual property.

  • Contribute to grant writing and fundraising efforts for research projects.

  • Contribute to the development of data packages supporting decision making.

  • Contribute to the development of pipelines and systems for data analysis, integration and .

  • Contribute towards publication of scientific methods and findings.

Create

  • Create and maintain databases of biological information.

  • Create appropriate documentation.

  • Create scientifically rigorous visualizations, communications, and presentations of results.

Design

  • Design and develop computational models to simulate biological processes.

  • Design and execute computational experiments to test hypotheses.

  • Design and implement methods for single cell genomics analysis of biological data.

  • Design and implement workflows using advanced bioinformatics and biostatistics.

  • Design and maintain lab databases containing phenotypic and sample data as well as metadata.

Develop

  • Develop algorithms for the analysis and interpretation of biological data.

  • Develop and apply computational tools for data sharing and visualization.

  • Develop and apply cutting-edge functional genomics tools and relevant enabling technologies.

  • Develop and test new survey instruments.

  • Develop bioinformatics pipelines for analyzing next-generation sequencing data.

  • Develop computational optimization approaches.

  • Develop new bioinformatics tools such as targeted sequencing panels and imputation panels.

  • Develop new tools in R or python under the guidance of Genentech Human Genetics scientists.

  • Develop software tools and pipelines for processing and analyzing biological data.

  • Develop tools for data sharing and visualization.

Devise

  • Devise new algorithms and approaches to data analysis.

  • Devise new algorithms and approaches to single-cell data analysis.

Document

Document analytical results in regulated systems and technical reports.

Enable

Enable adoption of new methods and tools.

Ensure

Ensure compliance with data privacy and security regulations.

Evaluate

  • Evaluate alternative analytical methods relevant to the needs of Genentech Human Genetics scientists.

  • Evaluate and recommend new emerging single-cell genomics analytical approaches.

Execute

Execute data analysis projects consistent with specified timelines and milestones.

Explore

Explore opportunities to re-position existing drugs for novel indications.

Follow

Follow proper scientific methodology in development of advanced integrative mathematical models.

Gather

Gather information from, and present results to a broad range of non-computational staff.

Identify

Identify patterns and correlations in biological data to uncover underlying biological mechanisms.

Implement

  • Implement methods and pipelines at scale to deliver statistical analyses.

  • Implement statistical methods for analyzing biological experiments.

  • Implement working analysis pipelines that run existing / published tools.

Integrate

Integrate analyses of multiple data types to uncover novel insights.

Investigate

Investigate interesting and sometimes puzzling data.

Lead

  • Lead and contribute to single-cell genomics analysis of biological data.

  • Lead computational efforts to design and optimize mRNA molecules for vaccines and other therapeutics.

Machine

Machine learning techniques and their application to solving real-world biological problems.

Maintain

  • Maintain and manage computational resources and infrastructure.

  • Maintain common core bioinformatics libraries.

Manage

Manage the Working Dog Project, coordinating the acquisition of DNA and behavioral samples.

Mentor

Mentor and train junior computational biologists and researchers.

Monitor

Monitor and evaluate analytical aspects of new and emerging computational immune oncology techniques.

Optimize

Optimize algorithms and code for efficient data processing.

Oversee

Oversee the data and code management for the lab, including backups.

Participate in

  • Participate in project teams with GSK scientists and external collaborators.

  • Participate in the development and validation of new computational methods.

Perform

  • Perform literature searches, testing and validation of new data analysis software.

  • Perform network analysis and pathway enrichment studies.

  • Perform statistical analysis and hypothesis testing to validate computational models.

  • Perform statistical and population analysis of large cohorts of biological data.

Prepare

  • Prepare data visualization for publication or oral presentations.

  • Prepare written reports and presentations for internal use and publication.

Present

Present results of analysis or software testing internally and externally.

Process

  • Process, analyze and interpret high dimensional data from diverse OMICS platforms.

  • Process NGS alignment, transcript quantitation, variant calling, other data types as required.

Provide

  • Provide data visualization templates for interface and documentation.

  • Provide technical guidance and support to other researchers and scientists.

  • Provide timely analysis and interpretation of experiments from preclinical models.

Publish

Publish research findings in high quality scientific journals.

Share

Share and communicate all aspects of data analysis, tools, and methods developed.

Stay updated with

Stay updated with the latest advancements in computational biology and bioinformatics.

Study

Study publications related to the tools to gain deeper understanding of how they work.

Support

  • Support efforts on the experimental design, analysis, and interpretation of experiments.

  • Support pre-clinical bioinformatics needs.

Test

Test and validate new data analysis software.

Track

Track record of innovative and impactful research including peer-reviewed publications.

Troubleshoot

Troubleshoot computational issues and implement solutions.

Use

Use and develop automated methods to support large-scale bioinformatics analysis.

Utilize

Utilize experimental results to refine mRNA design and computational approaches.

Work

Work on CRISPR-Cas based approaches to target discovery and validation.

Most In-demand Hard Skills

The following list describes the most required technical skills of a Computational Biologist:

  1. Proficiency in programming languages such as Python, R, and Java.

  2. Strong knowledge of statistics and probability theory.

  3. Expertise in bioinformatics tools and databases.

  4. Experience with machine learning algorithms and data mining techniques.

  5. Familiarity with high-performance computing and parallel processing.

  6. Competence in data visualization libraries and tools.

  7. Proficiency in next-generation sequencing data analysis.

  8. Knowledge of molecular dynamics simulations and modeling.

  9. Understanding of genomics, proteomics, and systems biology.

  10. Experience with cloud computing platforms and distributed computing.

Most In-demand Soft Skills

The following list describes the most required soft skills of a Computational Biologist:

  1. Strong analytical and problem-solving skills.

  2. Excellent communication and presentation skills.

  3. Ability to work effectively in interdisciplinary teams.

  4. Attention to detail and accuracy in data analysis.

  5. Critical thinking and creativity in approaching biological problems.

  6. Time management and ability to handle multiple projects.

  7. Adaptability and flexibility in a fast-paced research environment.

  8. Collaboration and willingness to learn from others.

  9. Strong work ethic and commitment to scientific rigor.

  10. Ability to explain complex concepts to non-technical stakeholders.

Conclusion

The role of a Computational Biologist is multifaceted and requires a combination of technical expertise in biology, computer science, and mathematics, along with strong analytical and communication skills. By leveraging computational methods and tools, Computational Biologists contribute significantly to the advancement of biological research and the development of innovative solutions for understanding and tackling complex biological challenges. They play a pivotal role in deciphering biological data, designing computational models, and collaborating with experimental biologists to gain insights into the intricate workings of living systems.

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