Bioinformatics Scientists

Develop new software applications or customize existing applications to meet specific scientific project needs.

AI: Fully automatable - By 2025 AI code generation and customization tools can produce and adapt software applications to meet many specific scientific project needs with minimal human coding effort.

Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes.

AI: Fully automatable - AI systems can run end-to-end pipelines to preprocess, analyze, and interpret large molecular datasets at scale for research (and often for clinical workflows with appropriate validation and oversight).

Develop data models and databases.

AI: Fully automatable - Given requirements, AI can design data models, generate schema and database code, and implement databases for many bioinformatics use cases with minimal human intervention.

Compile data for use in activities, such as gene expression profiling, genome annotation, or structural bioinformatics.

AI: Fully automatable - AI can aggregate, clean, standardize, and compile diverse biological datasets into formats ready for gene expression profiling, genome annotation, or structural analyses.

Manipulate publicly accessible, commercial, or proprietary genomic, proteomic, or post-genomic databases.

AI: Fully automatable - AI tools can programmatically query, transform, and integrate public, commercial, and proprietary omics databases, subject to access controls and governance provided by humans.

Provide statistical and computational tools for biologically based activities, such as genetic analysis, measurement of gene expression, or gene function determination.

AI: Fully automatable - AI can develop and deliver statistical and computational tools (packages, pipelines, and models) for genetic analysis, gene-expression measurement, and gene-function inference.

Create or modify web-based bioinformatics tools.

AI: Fully automatable - AI can create, modify, and deploy web-based bioinformatics tools, producing front-end, back-end, and integration code along with documentation in many scenarios.

Instruct others in the selection and use of bioinformatics tools.

AI: Fully automatable - AI can produce targeted tutorials, comparative tool analyses, interactive walkthroughs, and troubleshooting guidance sufficient to instruct many users in selecting and using bioinformatics tools.

Test new and updated bioinformatics tools and software.

AI: Fully automatable - AI can fully automate the bulk of software testing—generating test cases, running unit/integration/regression/performance tests, and flagging issues—covering most routine testing needs.

Prepare summary statistics of information regarding human genomes.

AI: Fully automatable - AI can programmatically compute and prepare summary statistics for human genomic datasets given access to the data and appropriate privacy controls.

Communicate research results through conference presentations, scientific publications, or project reports.

AI: Partial - AI can generate manuscripts, slides, and reports and assist with submission, but delivering talks, defending results live, and managing author responsibilities still need humans.

Create novel computational approaches and analytical tools as required by research goals.

AI: Partial - AI can propose and prototype new computational approaches and analytic tools, but truly novel methods require human domain insight, validation, and scientific judgment.

Consult with researchers to analyze problems, recommend technology-based solutions, or determine computational strategies.

AI: Partial - AI can analyze problems and propose computational strategies from data and literature but cannot fully replicate the nuanced domain judgment and interactive stakeholder negotiation of a human consultant.

Keep abreast of new biochemistries, instrumentation, or software by reading scientific literature and attending professional conferences.

AI: Partial - AI can continuously scan, summarize, and highlight relevant literature and virtual conference content, but it cannot fully reproduce in-person networking, tacit knowledge exchange, and human expert judgment from attendance.

Design and apply bioinformatics algorithms including unsupervised and supervised machine learning, dynamic programming, or graphic algorithms.

AI: Partial - AI can design and apply standard ML and algorithmic approaches and automate many experimental comparisons, but truly novel algorithm design and deep theoretical innovation still require human expertise.

Improve user interfaces to bioinformatics software and databases.

AI: Partial - AI can generate UI designs, prototypes, accessibility recommendations, and implementation code, but human-centered research, stakeholder validation, and nuanced usability judgment remain necessary.

Confer with departments, such as marketing, business development, or operations, to coordinate product development or improvement.

AI: Partial - AI can draft communications, synthesize requirements, and propose coordination plans across departments, but it cannot fully replace human negotiation, relationship-building, and organizational decision-making.

Recommend new systems and processes to improve operations.

AI: Partial - AI can analyze operational data and recommend new systems or process improvements, yet tailoring, prioritizing, and implementing those recommendations requires human context and leadership judgment.

Collaborate with software developers in the development and modification of commercial bioinformatics software.

AI: Partial - AI can contribute code, suggest architectures, and automate development tasks, but effective collaboration with software developers still requires human coordination, domain negotiation, and integration into team workflows.

Direct the work of technicians and information technology staff applying bioinformatics tools or applications in areas such as proteomics, transcriptomics, metabolomics, or clinical bioinformatics.

AI: Not automatable - Directing the work of technicians and IT staff entails leadership, personnel management, and organizational accountability that AI cannot assume.