WHAT DOES A BIOINFORMATICS SCIENTIST DO?

The Bioinformatics Scientist analyzes complex single-cell RNA sequencing (scRNA-seq) data from various tissues under multiple experimental conditions to aid in mechanism of action and biomarker studies. This responsibility involves conducting rigorous and thorough analyses of both in-house and public bioinformatics data, developing relevant bioanalytical tools, and creating detailed analytical reports with high-quality visualizations. Additionally, the scientist also collaborates closely with experimental scientists and contributes to building and optimizing production-scale pipelines for diverse sequencing data analysis, including porting existing pipelines to Amazon Web Services.

A Review of Professional Skills and Functions for Bioinformatics Scientist

1. Bioinformatics Scientist Duties

  • Data Analysis: Analyse, summarize, and interpret NGS data sets using existing and customized approaches.
  • Technology Development: Contribute to technology development by providing insights into computational solutions and identifying suitable workflows.
  • Experiment Interpretation: Interpret the outcome of proof-of-concept experiments and help to improve the design jointly with the tech-dev team.
  • Code Development: Develop production-ready, regulatory-compliant code in partnership with the software development team.
  • Pipeline Optimization: Support the rollout, optimization, and improvement of scalability of Inivata's analysis pipelines.
  • Methodology Development: Contribute to the development of novel methodologies and algorithms for the analysis of ctDNA.
  • Support Services: Provide support to clinical, business development, and other departments for novel and existing products.
  • Innovative Projects: Support innovative projects in RNA modulation, oncology, target identification and validation, biomarker discovery, and disease signature.
  • Pipeline Improvement: Develop and improve internal methods and pipelines, and play a key role in the management of Linux servers.
  • Communication: Communicate findings and present results internally to biological associates and externally to clients and partners.

2. Bioinformatics Scientist Details

  • Data Analysis: Perform analysis of large-scale genomic and clinical datasets from internal and external sources.
  • Data Curation: Import, curate, and catalog public datasets to support integrative analyses of data towards target discovery.
  • Data Management: Support target discovery by enriching, managing, and analyzing data.
  • Literature Review: Conduct literature searches on novel bioinformatics analysis methodology and algorithms and introduce them in-house.
  • Dataset Evaluation: Evaluate single-cell and bulk genomic datasets through established workflows.
  • Communication: Liaise with computational and experimental scientists and present results to teams.
  • Collaboration: Collaborate with the computational team to develop custom scripts and pipelines.
  • 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.
  • Team Collaboration: Collaborate with computational and experimental scientists in a multidisciplinary team environment to accomplish research goals.

3. Bioinformatics Scientist Responsibilities

  • Quality Assessment: Develop/implement quality assessment methods, statistical analysis, and data visualization for multi-omics datasets.
  • Pipeline Development: Build the bioinformatics and data analysis pipeline/visualization infrastructure, data storage, and analytical capabilities.
  • Algorithm Design: Design and apply bioinformatics/statistical algorithms, unsupervised and supervised methods, univariate and multivariate regression analyses, etc.
  • Resource Provision: Serve as a resource for the most current data visualization, bioinformatics, and statistical methods.
  • Biomarker Support: Support biomarker discovery and development efforts to elucidate drug MoA and disease biology to bring meaningful therapies to patients.
  • Computational Methods: Develop computational methods for integrative analysis and visualization of large multi-omic datasets.
  • Interdisciplinary Collaboration: Collaborate with interdisciplinary teams to drive data analysis, integration, and application across various ‘omics platforms.
  • Testing: Develop pipelines and test algorithms to optimize the analysis of NGS data produced from samples collected by the smart sticker platform.
  • Feature Identification: Search large complex datasets to identify predictive features and combine these in algorithms to predict disease status.
  • Biomarker Programs: Participate in disease-focused translational biomarker programs and clinical biomarker assessment.

4. Bioinformatics Scientist Job Summary

  • Technology Development: Contribute to the new technology development within a multidisciplinary team of scientists focused on single-cell research
  • Molecular Tagging: Develop molecular tagging and barcoding designs to uniquely identify thousands of samples
  • Designing: Design oligos and primers to implement these designs in RT, amplification, and barcoding reactions with whole transcriptomes
  • Data Analysis: Analyzing and troubleshooting NGS data, hidden biases, and read structures
  • Supporting: Support experimental optimization and development of advanced molecular tagging and provide constructive feedback to the bench scientists
  • NGS Tool: Develop new and customized NGS single-cell analysis tools for internal R&D use and as part of customer-facing production software
  • Communication: Proactively communicating across functions, especially with cell and molecular biologists
  • Sequence Interpretation: Interpret sequence results, propose additional experiments, and dynamically adjust designs to support the fast-paced development work
  • Software Development: Contribute to the development of customer-facing software, algorithms, visualization
  • Scripting: Employ a variety of scripting languages and tools that bridge across disparate systems

5. Bioinformatics Scientist Accountabilities

  • Bioinformatics: Develop bioinformatic tools and pipelines from scratch utilizing NGS data.
  • Pipeline Management: Manage pre-existing bioinformatics pipelines, making them suited to human genome applications.
  • Environment Maintenance: Maintain and develop the computational and storage environment, made up of databases and servers.
  • Supporting: Support the company’s central NGS repository.
  • Cloud Platform: Develop and build from scratch the cloud-based bioinformatics platform.
  • Big Data Sharing: Develop a sharing mechanism for big data.
  • RNA Biology Knowledge: Stay abreast of the latest RNA biology and transcriptomics datasets.
  • Analysis Documentation: Organize and document analysis strategies.
  • Communication: Communicate and collaborate with both internal and external stakeholders.
  • Organizational Support: Work across the organization to offer bioinformatics and/or biostatistics support.

6. Bioinformatics Scientist Functions

  • Collaboration: Collaborate within interdisciplinary teams focused on mechanistic understanding of immune reconstitution, immune competence, and immune tolerance.
  • Development: Contribute to the development of new methods and data analytics.
  • Analysis: Identify and characterize cell-cell interactions and immune regulatory networks involved in the initiation and maintenance of peripheral tolerance.
  • Support: Support external collaborations, ensure data transfer and data integrity, and review study reports for mechanistic biomarker discovery.
  • Authorship: Author, review, and approve SOPs, study protocols, reports, and manuscripts.
  • Expertise: Serve as an SME on cross-functional program teams.
  • Collaboration: Collaborate with Talaris Process Development, Analytical Development, Manufacturing, Quality and Regulatory functions, and functional leads.
  • Alignment: Assist in maintaining alignment of goals and navigating project obstacles.
  • Execution: Design and execute single-cell RNA sequencing, pathway analysis and multi-omics software platforms, spatial gene expression technology, and T/B cell receptor repertoire analysis.

7. Bioinformatics Scientist Job Description

  • Data Generation: Help generate single-cell data from immune-oncology model systems using the 10x Genomics Chromium System
  • Bioinformatics Implementation: Help implement state-of-the-art bioinformatics pipelines to analyze single-cell data
  • Data Analysis: Analyze and interpret single-cell data and communicate biological insights to bioinformatics and wet lab research colleagues in Berlin
  • Team Collaboration: Be part of an international and interdisciplinary research team closely interacting with colleagues from the entire R&D organization
  • Scientific Visibility: Strengthen the visibility of research and scientific excellence through publishing and actively engaging with the scientific community
  • Collaboration: Collaborate with biologists and software engineers on the development of data models and analytical workflows to support the variety of data types
  • Technology Learning: Learn about, understand, evaluate, and incorporate new cloud tools and technologies
  • Prototyping: Rapidly translate concepts to prototypes for user interfaces
  • Codebase Understanding: Dig into and understand an existing codebase, and learn how to interact with a variety of other tools, data resources, APIs, etc.
  • Workflow Support: Work with workflow languages and batch computing tools to support analysis workflows
  • Data Transfer: Work with HTAN data contributors to enable and facilitate data transfer and annotation

8. Bioinformatics Scientist Overview

  • Data Analysis: Analyze complex in-house scRNA-seq data with multiple experimental conditions in a range of tissues to contribute to mechanism of action and biomarker studies.
  • Bioinformatics: Interrogate in-house and public bioinformatics data such as functional genomics and imaging data.
  • Analytical Rigor: Do rigorous, thorough analyses following best practices and using state-of-the-art approaches.
  • Method Development: Responsible for developing the relevant bioanalytical methods/tools.
  • Reporting: Compose detailed analytical reports, concise and clear key summaries, and quality visualizations.
  • Collaboration: Collaborate with scientists who work at the bench and interact closely with others doing bioinformatics analyses.
  • Pipeline Construction: Build production-scale pipelines for the analysis of diverse sequencing data.
  • Cloud Computing: Port existing pipelines into AWS, incorporating containers and workflow systems.
  • Pipeline Development: Contribute towards the development of analysis pipelines for newly established assays.
  • Interdisciplinary Collaboration: Collaborate closely with experimental biologists and chemists in the design, analysis, and communication of scientific results.

9. Bioinformatics Scientist Details and Accountabilities

  • Data Analysis: Analyze sequencing data using established workflows either already in place or state-of-the-art workflows developed by others.
  • Algorithm Development: Develop and improve next-generation sequencing data analysis algorithms and pipelines and/or statistical methods.
  • Experiment Analysis: Analyze results of RNA-seq or single-cell RNA-seq experiments.
  • Statistical Method: Develop and apply innovative statistical methods and data integration approaches for transcriptomics when off-the-shelf methods are not adequate.
  • Supporting: Assist, collaborate, and consult with internal/external researchers on the analysis of transcriptomic data.
  • Presentation: Interpret and present analysis results to coworkers and collaborators.
  • Scientific Publication: Publish developed methods and scientific findings in scientific journals and give presentations at conferences.
  • Team Collaboration: Collaborate with other members of the computational biology team as well as external collaborators and customers.
  • Expert Development: Progressively become the subject matter expert on all genomics analyses involving RNA within the Computational Biology group.
  • Continual Learning: Stay current on innovations in genomics technologies and analyses.
  • Grant Participation: Help identify relevant funding opportunities and interests and participate in grant writing.

10. Bioinformatics Scientist Additional Details

  • Collaboration: Collaborate with academic and industrial partners to understand and shape the novel data generation platform and support clinical validation of predictive models.
  • Signal Interpretation: Signal recovery and interpretation from rich single-cell multi-omic datasets.
  • Strategy Development: Develop and execute a data architecture strategy following a Data Management Plan.
  • Data Modeling: Define and maintain a data model and data dictionaries describing the consortium datasets and the relationships.
  • Data Storage Strategy: Plan and implement a redundant data storage, backup and recovery strategy.
  • Data Integration: Aggregate, integrate and summarize consortium data to complement data modeling activities.
  • Exploratory Analysis: Engage in exploratory analysis of the data generated by the platform to ensure data quality and understand data trends.
  • Experiment Documentation: Record all experiments in an accurate, timely and presented manner, and use this to prepare data summaries and reports.
  • Documentation Production: Produce thorough but concise written documentation, produce standard operating procedures, and contribute to intellectual property submissions.
  • Communication: Effectively communicate analyses and research findings to a technical and non-technical audience.
  • Research Publication: Contribute to manuscripts for peer-reviewed publications.
Relevant Information
What Does A Bioinformatics Analyst Do?