What Does A Computational Biologist Do?

WHAT DOES A COMPUTATIONAL BIOLOGIST DO?

Updated: Dec 24, 2024 - The Computational Biologist utilizes computational models and machine learning algorithms to analyze multi-omics datasets, predicting complex traits and enhancing genome product development across crops. This role involves collaborating with interdisciplinary teams to integrate environmental, phenotypic, and molecular data, driving data-centric solutions for genome-edited breeding programs. The biologist also contributes to the continuous improvement of computational pipelines, ensuring alignment and strategic planning through comprehensive documentation and communication of research findings.

A Review of Professional Skills and Functions for Computational Biologist

1. Computational Biologist Duties

Antigen/Antibody Structural Analysis: Structural analysis of antigen/antibody complexes for HIV-1 and other pathogens
Antibody Resistance Analysis: Antibody resistance analysis for pathogens with high sequence diversity
Structure-Based Antibody Design: Structure-based design to improve antibody potency and breadth for HIV-1 and other pathogens
Structure-Based Immunogen Design: Structure-based design of immunogens for HIV-1 and other pathogens
Statistical Analysis of Preclinical Studies: Statistical analysis of preclinical immunization studies for HIV-1 and other pathogens
Support Structural Biology Missions: Assist the missions of Structural Biology and Structural Bioinformatics Core Sections
Protein Expression and Characterization: Express, purify, and characterize (e.g., binding kinetics and thermostability) protein designs from the Structural Bioinformatics Core Section
Scientific Manuscript Support: Assist with creating figures, writing, and proofreading scientific manuscripts
Collaborative Targeting Strategy Development: Work collaboratively with project leads to develop targeting strategies and perform in silico predictions
Database and Tool Development: Build databases, workflows, and visualization tools
Data Integration and Machine Learning: Integrate data sets from multiple sources and use machine learning to advance the prediction algorithm

2. Computational Biologist Details

Data-Based Strategy Development: Develop data-based strategies, write new algorithms or utilize existing ones, and deploy computational tools for the analysis of large cancer and sequencing data sets.
Proposal Preparation and Project Management: Help with proposal preparation, deliver monthly results to multiple investigators, capable of handling several projects simultaneously.
Publishing Novel Findings: Publish novel findings in high-impact factor journals in collaboration with wet lab members.
Data Visualization Exploration: Explore novel data visualization tools, with an emphasis on integrating diverse data types.
Algorithm Implementation for Distribution: Implement algorithms as software for distribution to the global cancer research community.
Analysis Summary and Reporting: Provide a synopsis of analyses with presentations and written reports.
Analysis Pipeline Development: Define and develop analysis pipelines to analyze single-cell sequencing, somatic mutations, TCRs, and predictive biomarkers.
Statistical Methods Application: Apply rigorous statistical methods to drive actionable insights from large-scale datasets.
Statistical Model Development and Experimentation: Conceive, implement and test statistical models, work with wet-lab researchers to translate these models into testable experiments, and analyze the data produced from these experiments.
Data-Rich Experiment Analysis: Lead the analysis of data-rich experiments including those involving RNA-seq, CRISPR and shRNA pooled screens, gain-of-function ORF screens, proteomic data sets, and many others.
Bioinformatics and Methodology Development: Devote 80% to bioinformatics needs for the lab and mentoring associates while 20% to methodology development, support CCLE project needs and data reproducibility of various ongoing projects.

3. Computational Biologist Responsibilities

Data Management Protocol Development: Support Government PIs in planning and developing best practice data management and analysis protocols for proposed and ongoing projects.
Computational Strategy Consultation: Consult with research scientists to determine computational strategies as well as the selection and use of project-specific bioinformatics tools.
Software Development and Customization: Develop new or customize existing software applications to meet specific project needs.
Bioinformatics Tool Application: Apply bioinformatics tools or applications in areas such as proteomics, transcriptomics, metabolomics, and clinical bioinformatics.
Computational Approach Development: Create novel computational approaches and analytical tools as required by research objectives.
Large Molecular Dataset Analysis: Analyze large molecular datasets, such as raw microarray data, genomic sequence data, and proteomics data for mining trends to support specific project purposes.
Data Management Guidance: Attend and participate in meetings with programmatic staff to guide data management needs.
Data Presentation: Prepare and present data and research findings to internal and external collaborators as well as stakeholders.
Work Prioritization: Prioritize work in a fast-paced environment to limit downtime and keep operations moving.
Safety Compliance: Ensure that safety is a priority in everything you do.
Professionalism and Core Values: Maintain professionalism and live CCA’s core values at all times.

4. Computational Biologist Job Summary

Bioinformatics Analysis: Conduct bioinformatics analysis primarily with gene expression data but also with other types of genomic and clinical data.
Data Visualization and Summary: Produce high-quality summary and visualization of the findings and interpretations of the results.
Scientific Research Integration: Digest the latest scientific research papers and cancer biology knowledge for import into the database of transcriptomes and locked gene expression signatures.
Genomic Signature Development: Develop genomic signatures using machine learning approaches.
Data Analysis for Process Improvement: Support data analysis that contributes to the process improvement of Decipher products.
Manuscript Drafting: Contribute to manuscript drafting for peer-reviewed journals.
Data Preparation and Analysis Support: Support and assist colleagues and external collaborators in data preparation and analysis.
Database Maintenance and Improvement: Understand, maintain, and improve various databases and schema.
Data Processing Pipeline Enhancement: Contribute to the maintenance and improvement of the data processing pipelines, automating common data analysis routines into software packages or applications.
AI Technology Application: Contribute to the research and development of applying the latest developments of AI technologies to the Decipher data.
Informatics Pipeline Design: Design, implement, and refine informatics pipelines that analyze mass spectrometry raw and processed results from immune-precipitation experiments.

5. Computational Biologist Accountabilities

NGS Assay Competency: Perform and maintain competency in complex Next Generation Sequencing assays/tasks according to established operational procedures under supervision.
Automation Workflow Familiarity: Familiarity with automation workflows on robotic NGS equipment.
Lab-Based Activity Execution: Carry out all lab-based activities - sample preparation, library construction, data generation, and upstream data processing to ensure a high level of quality and reliability of generated results.
NGS Library Preparation Optimization: Effectively optimize and troubleshoot NGS library preparation.
Nucleic Acid Analysis Application: Apply Bioanalyzer and other nucleic acid analysis techniques to library construction quality control.
NGS Technology Expertise: Serve as a subject matter expert for NGS technology.
Experimental Design Guidance: Guide experimental design, method or platform selection, and data management to address specific needs.
Scientific Education and Training: Educate other scientists, provide hands-on training on tech center equipment, and deliver internal presentations when applicable.
NGS Platform and Innovation Awareness: Stay current and at the cutting edge of various NGS platforms and technical innovations.
Documentation and Reporting: Draft experimental plans, validation reports, work instructions, and SOPs.
Scientific Contribution: Contribute experience and creativity to the scientific community and endeavors.

6. Computational Biologist Functions

Scientific Consulting: Scientific consulting and professional service activities for customer projects, working with customers’ cross-functional partners, including scientific and IT partners.
Customer Need Identification: Identify customer needs and map them to the capabilities of the software.
Technical Troubleshooting: Troubleshoot scientific and technical support issues and capture user feedback.
Internal Communication and Coordination: Communicate customer needs and coordinate activities internally with the scientific consulting and professional services team, business account managers, and product managers.
Sales Support and Product Expertise: Support the sales team with product expertise, scientific presentations, and software demonstrations.
Product Vision Presentation: Present the product vision of Genedata Selector to prospects and customers.
Product Training: Conduct internet-based and on-site product training sessions with customers.
Business Opportunity Identification: Identify new business opportunities with business account managers.
Service Project Definition: Define appropriate service projects with customers to fulfill unmet needs.
Informatics Algorithm Design: Design, implement, and refine informatics algorithms and pipelines that quantify and annotate antibody repertoire sequence information from NGS sequencing of donor samples.
Phage Display Pipeline Design: Design, implement, and refine informatics pipelines to identify phage display enrichers from NGS sequencing data of phage display experiments.

7. Computational Biologist Job Description

Bioinformatics Analysis: Perform bioinformatics analysis in support of scientific research using technical knowledge of software and its implementation in a high-performance computing infrastructure.
Bioinformatic Data Analysis Execution: Perform or direct others to execute various bioinformatic data analyses related to specialized research methodologies and results.
NGS Data Analysis: Perform next-generation sequence (NGS) data analysis (bulk RNA-seq, ChiP-seq, scRNA-seq, ATAC-seq, HiC-seq) and integrate with data present in publicly available databases.
Research Project Management: Manage assigned research projects with minimal input from the principal investigator.
Software Development and Modification: Develop and/or modify software to support new and ongoing research projects.
Research Reporting and Manuscript Preparation: Prepare research reports for presentation and assist with the preparation of manuscripts for publication.
Computational Analysis of NGS Datasets: Conduct computational analysis on available next-gen sequencing datasets (bulk and single-cell) from pre-clinical model systems, CART manufacturing, and clinical trials to understand the mechanism of action of cell therapies, identify biomarkers, and elucidate resistance mechanisms.
Algorithmic Approach Development: Develop innovative algorithmic approaches to provide robust and testable hypotheses from analyzed experimental data.
State-of-the-Art Methodology Support: Stay up-to-date on state-of-the-art methods and techniques in computational biology and provide support on an as-needed basis to cross-disciplinary project teams.
Data Integration Method Development: Develop and implement methods for integrating internal and public datasets from diverse platforms such as RNA-seq (bulk and single-cell), epigenetic profiling, FACS, and high-dimensional imaging.
Analysis Tool and Methodology Development: Partner with others in the group to develop new analysis tools and methodology.

8. Computational Biologist Overview

Bioinformatics Vision Contribution: Contribute to the ongoing bioinformatics vision along with the strategic direction of the entire Data team.
Bioinformatics Pipeline Direction: Provide direction for bioinformatics pipeline and applications for marker development.
Targeted Resequencing and NGS Application: Work with Molecular Geneticists on applications of targeted resequencing, microarray and WGS/WES, and transcriptomics NGS platforms, including detection of rare variants, gene fusions, deletions, other structural variants, quantitation of differential gene expression, and genotype imputation.
Functional Variation Detection: Detect functional and/or causal variation in genes associated with Traits of Interest.
Pipeline Development: Use, extend, and where applicable, develop new pipelines for expression and genotyping data analysis.
Post-Bioinformatics Marker Assay Implementation: Support Molecular Genetics and Breeding in the implementation of post-bioinformatics marker assay implementation.
Data Analysis and Visualization: Work closely with Breeding, Genomics, and Chemistry to analyze experimental data and report results through data visualization.
Software Development for Data Mining: Identify and/or develop software for data mining of large datasets, including genomic sequence information, plant phenotype, and chemical profiling data.
Experimental Design Collaboration: Collaborate with R and D Teams on experimental design to ensure compatible data structure and adequate statistical power in all studies.
Process Documentation and SOP Development: Develop and draft documentation and SOPs for new processes.
Cross-Functional Collaboration: Interface with several cross-functional groups within the company and through collaborators.

9. Computational Biologist Tasks

Bioinformatics Leadership in Oncolytic Virus Studies: Lead bioinformatics studies in oncolytic virus treatments for the cancer field.
Molecular Profiling Experiment Design: Work in concert with IO clinical and pre-clinical teams to design molecular profiling experiments, draft data analysis plans, and provide critical analytics support.
Presentation and Documentation: Prepare clear, concise, and easy-to-understand presentations and documentation for collaborators, senior management, and government agencies.
Publication and Presentation of Findings: Publish and present novel findings in peer-reviewed journals and conferences.
NGS Data Processing Tool Development: Design, develop, and implement tools for processing and analyzing high-throughput next-generation sequencing data, including single-cell sequencing, WGS, ExomeSeq, RNASeq, copy number, methylation data, etc.
Molecular Profiling Strategy Development: Contribute to team efforts of developing Molecular Profiling strategies and benchmarking best practices.
Computational Needs Identification: Collaborate with project teams to identify key computational needs.
Study Design and Protocol Development: Contribute to study design and protocol development to enable novel experimentation.
Analysis Planning and Execution: Plan and execute analyses built to suit a range of established protocols as well as newly developed workflows.
Result Visualization and Presentation: Visualize, summarize, and present results to stakeholders.
Outcome Synthesis: Work with the team to synthesize findings into actionable outcomes.

10. Computational Biologist Roles

Collaborative Therapeutic Research: Collaborate with a diverse team of experimentalists, computational scientists, and clinicians to address key therapeutic questions in a data-centric manner.
Genomic and Transcriptomic Analysis: Analyze internal and external datasets to determine the genomic and transcriptomic properties of effective T-cell therapeutics.
Cross-Disciplinary Collaboration: Engage and collaborate with a team of Scientists from multiple biological disciplines, Plant Breeders, Bioinformaticians, Computational Biologists, and Software Engineers to integrate environmental, phenotypic, and molecular datasets, and build data-driven solutions supporting the genome product development pipeline across crops.
Computational Model Development: Develop computational models to integrate environmental, phenomics, genomics, transcriptomics, proteomics, and other types of biological ('omics') information to build a predictive platform for complex traits.
Machine Learning Implementation: Implement machine learning and/or other non-linear or linear algorithms to enable the performance prediction of novel genetic diversity derived from genome editing technologies, using a combination of environmental and multiple ‘omics’ datasets.
Data Pipeline and Application Development: Work with Products, Science, and Digital teams to develop, prototype, and implement data pipelines and front-end applications that increase the efficiency of genome-edited breeding programs across crops, by deploying predictive models.
Germplasm Characterization: Contribute to ongoing germplasm characterization efforts, including both naturally occurring as well as novel genetic diversity derived from genome-editing technologies.
Training Set Development for Genomic Selection: Contribute to the development of highly accurate training sets for genomic selection of complex traits, including the design, data generation, and analysis of field and molecular experiments.
Computational Pipeline and Algorithm Improvement: Develop, improve, or expand in-house computational pipelines, algorithms, models, and services used in crop product development.
Research Documentation and Communication: Constantly document and communicate results of research on data mining, analysis, and modeling approaches to ensure strategic planning and alignment within and across teams.

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