WHAT DOES A BIOINFORMATICIAN DO?

The Bioinformatician conducts extensive reviews of genetics-related literature and data to understand the significance of genes and their variants. They play a pivotal role in classifying gene-disease associations and developing software tools that enhance the curation of genomic data. Additionally, this professional facilitates collaborative research projects, supports clinical working groups, and contributes to scholarly publications in the field of genomics.

A Review of Professional Skills and Functions for Bioinformatician

1. Bioinformatician Duties

  • Data Science and Engineering Expertise: Apply data science and engineering expertise to formally specify common data models mapping scientific data with a focus on the types of questions researchers ask.
  • Collaboration with Scientists: Collaborate with scientists to distill scientific questions and data into the computable entities of a data model.
  • Software Engineering Collaboration: Work side-by-side with software engineers to maximize the cross-compatibility, searchability, and interoperability of datasets.
  • International Standards Collaboration: Collaborate with international standards groups to strive for interoperability with data across the globe.
  • Data Science and Bioinformatics: Apply and grow data science, bioinformatics, and domain experience to solve data management and access challenges.
  • Learning New Domains: Learn new domains through collaborations with a wider community of computational biologists and biomedical researchers.
  • Independent Work Structure: Juggle multiple deadlines and structure work independently.
  • Statistical Analysis Support: Support statistical analysis of in vivo and in vitro studies and lead data science functions of NGS.
  • Literature Consultation: Consult and search the scientific literature regularly.
  • Communication Skills: Communicate computational methods and results to non-computational researchers in associated groups as well as with the department at large.

2. Bioinformatician Details

  • Collaboration: Collaborate with molecular biology, protein screening, biochemistry teams, and others on a project-to-project basis.
  • Multiomic Analysis Pipeline: Create a robust and reproducible multiomic analysis pipeline covering NGS, RNA, protein expression, and proteomics data.
  • Data Interpretation: Interpret and analyze data with the latest statistical techniques and disseminate the results to other scientific teams.
  • Stakeholder Support: Work with stakeholders to support data infrastructure needs while assisting with data-related technical issues.
  • Data Generation Support: Accelerate data generation by supporting protein and genomic screening team’s efforts.
  • Quality Improvement: Improve the quality and consistency of routine genomic and biochemical analysis.
  • Sample Preparation: Prepare samples to generate high-quality, accurate, and dependable results.
  • R&D Contribution: Contribute towards generating key data and insights that will drive R&D strategy.
  • Decision-Making: Drive decision-making for the tools and techniques our data platform should adopt to achieve goals.
  • Project Forecasting: Forecast project finalization and communicate service project status updates to sales and customers.

3. Bioinformatician Responsibilities

  • Computational Analysis: Utilizing a variety of computational and/or machine learning approaches for analyzing large-scale NGS and other datasets.
  • Target Identification: Target identification & validation for drug discovery and development programs.
  • Tool Development: Developing & optimizing computational tools, pipelines, models, and approaches.
  • Project Management: Project management, including setting timelines and milestones for areas such as generating and analyzing genomic data.
  • Data Interpretation: Working with molecular biologists to help interpret data and optimize experimental approaches.
  • Team Leadership: Lead the bioinformatics team within QIAGEN Genomic Services, which provides high-quality, customized sample-to-result services for clients: from nucleic acid isolation to qPCR, NGS, and bioinformatics.
  • Data Analysis: Analysis of biological data with QIAGEN’s own software solutions and other bioinformatics and statistical methods.
  • Pipeline Development: Development, validation, documentation, and deployment of end-to-end bioinformatics analysis pipelines.
  • Collaboration: Collaboration with QIAGEN Genomic Services lab scientists and product management.
  • Customer Support: Assure pre- and post-sales customer support for experimental setup and NGS data analysis results.

4. Bioinformatician Job Summary

  • Project Management: Manage timelines and deliverables on projects and organize and document analysis strategies.
  • Tool Development: Contribute to maintaining and developing DNA synthesis design tools.
  • Pipeline Maintenance: Aid in maintaining and developing Variant Detection and De Novo NGS Pipelines.
  • Python Scripting: Develop Python scripts for R&D automation and analysis and serve as an example to Codex DNA’s culture.
  • Test-Driven Development: Use Test Driven Development to maintain and develop Bioinformatic APIs.
  • Literature Review: Keep up to date with scientific literature in the areas of strategic interest to Codex DNA.
  • Data Analysis: Analyze multiple data sets generated internally, as well as publicly available data sets, in an integrated fashion.
  • Computational Modeling: Apply computational methods to model the kinetics, molecular recognition, and structure of nucleic acids.
  • Signal Data Analysis: Analyze and incorporate signal data from next-generation sequencing assays, including ChIP, CLIP, structure probing, etc., to understand the biochemical properties of DNA, RNA, and oligonucleotides.
  • Collaboration: Collaborate with computational and experimental scientists in a multidisciplinary team environment to accomplish research goals.

5. Bioinformatician Accountabilities

  • Improve operational efficiencies by automating common tasks with scripts (e.g., Powershell, Batch file)
  • Identify ways to automate data comparisons, data analysis, and identification of patterns, then create resultant reports
  • Access internal and external databases (e.g., NIH, dbSNP) and ingest relevant data into own environment
  • Write standard operating procedures (SOPs) for bioinformatics activities
  • Support customers by providing more in-depth explanations of data results
  • Extract data for white papers or posters
  • Collaboration with the IT department will be key to maintaining a robust infrastructure compliant with regulatory standards.
  • Developing and maintaining computational pipelines that leverage software tools to solve clinical and biological problems.
  • Systematically exploring high-dimensional data to identify issues of practical or theoretical importance using statistical and computational approaches.
  • Exploring patterns in clinical and multi-omic biological data using bioinformatic and machine learning algorithms, including dimensionality reduction and clustering.

6. Bioinformatician Functions

  • Literature and Data Review: Researches the significance of variants and genes through literature and data review.
  • Classification: Classifies variant- and gene-disease associations.
  • Research Facilitation: Facilitates research projects that require curation of genomic knowledge.
  • Knowledge Sharing: Supports the sharing of curated knowledge with the community.
  • Software Development Collaboration: Collaborates in the development of software tools for supporting variant and gene curation.
  • Clinical Support: Aids Clinical Domain WGs in supporting gene and variant curation programs.
  • Research Publication Support: Supports the writing of ClinGen research publications.
  • Data Interpretation: Interprets data analysis of high throughput genomics, proteomics, and genetic data.
  • Resource Planning: Plans own resources to implement or modify existing web-based bioinformatics tools.
  • Report Writing: Writes reports on exploitation of DNA sequences and identification of areas of collaboration.

7. Senior Bioinformatician Job Description

  • Descriptor Frameworks: Design and implement novel molecular descriptor frameworks with the aim of extrapolating bioactivities between synthetic compounds and natural products.
  • Benchmarking: Implement a strategy to benchmark the effectiveness of these descriptor frameworks.
  • Team Collaboration: Collaborate with a team of cheminformatics and end-users to ensure fit-for-purpose models.
  • Expertise Collaboration: Collaborate with both scientific & tech teams to provide chem-/bioinformatics expertise.
  • Data Management: Data collection and standardization, database structure, workflow development, algorithmic design, data analysis, and visualization.
  • Data Analysis: Coordinate and perform data analysis efforts on different projects.
  • Communication: Communicate results to diverse audiences, including project teams, collaborators, and customers.
  • Technological Advancement: Stay abreast with new technological advancements and analyze them for potential inclusion in platform.
  • Pipeline Development: Develop new bioinformatics pipelines and improve current algorithms.
  • Tool Development: Plan and develop bioinformatic tools and pipelines for NGS diagnostics.

8. Bioinformatician Overview

  • Pipeline Development: Develop bioinformatics pipelines to enable genomic data storage, curation, and reporting.
  • Imputation Pipeline: Implement imputation pipeline for genomic information.
  • Evaluation Methods: Evaluate alternative genomic evaluation methods for genomic breeding value estimation and implement workflow.
  • Quality Control: Implement quality control measures for genomic evaluation.
  • Collaborative Projects: Contribute toward ongoing collaborative projects between CVF and RI.
  • Training and Development: Undertake appropriate training and development associated with the KTP Associate program.
  • Program Management: Overall day-to-day management of the KTP Associate program.
  • Problem Resolution: Resolve most problems without assistance from the line manager.
  • Workload Organization: Organize own workload according to priority and adapt.
  • Autonomous Decision-Making: Make operational and research decisions autonomously.

9. Bioinformatician Details and Accountabilities

  • Software Development: Builds software components according to provided specifications, standards, and procedures that are stand-alone in nature or part of a larger system.
  • Experimental Design: Creates experimental designs, protocols, engineering designs, plans, and procedures with minimal supervisory input.
  • Protocol Execution: Executes defined protocols, procedures, and data analysis plans.
  • Project Leadership: Plans and leads moderately complex development projects involving multiple components and driven by cross-functional initiatives and requirements.
  • Mentorship: Coaches/mentors other Associates in a broad scope of technical and product areas.
  • Knowledge Sharing: Regularly shares and applies knowledge gained from outside sources of information (scientific literature, patents, regulatory body websites, industry best practice) including integration of competitive information into projects.
  • Industry Awareness: Stays current on industry/technology trends.
  • Documentation: Creates, reviews, and approves experimental designs, protocols, engineering designs, plans, procedures, product/process documents, and reports to achieve business objectives.
  • Troubleshooting: Troubleshoots and performs extensive data analysis within unit of work.

10. Bioinformatician Duties

  • Leadership: Leads by example in following all policies, procedures, and proactively identifies and remedies problems.
  • Technical and Strategic Discussions: Leads/facilitates technical and strategic discussions and decision-making across functions.
  • Requirements Definition: Authors and drives requirements definition at project/cross-functional level.
  • Specification Review: Reviews and provides approval of product specifications.
  • Risk Mitigation: Anticipates and plans for mitigations for product, process, project, and schedule risks.
  • Process Improvement: Drives process improvement ideation, implementation, and sustainment within function.
  • Improvement Initiatives: Involved in driving broader scale improvement initiatives.
  • Decision Accountability: Accountable for decisions including architecture, resource, schedule, and cost estimates within a unit of work.
  • Sound Judgment: Uses sound judgment and thinks through impacts of situations, options, and broader impact as well.
  • Risk Management: Identifies, assesses risk, and proposes correction/corrective action to resolve systemic issues.
Relevant Information
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