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Computational Spatial Transcriptomics Jobs in Tennessee

Computational Spatial Transcriptomics information

What are some typical challenges faced when working in computational spatial transcriptomics, and how can new team members prepare for them?

Professionals in computational spatial transcriptomics often encounter challenges related to handling and analyzing large, complex datasets that combine spatial and gene expression information. Integrating data from different technologies and ensuring data quality can be demanding, requiring strong programming skills and familiarity with bioinformatics pipelines. New team members can prepare by strengthening their skills in statistical analysis, programming languages like Python or R, and staying updated on the latest spatial transcriptomics techniques. Collaborating closely with experimental biologists and data scientists is also key to overcoming these challenges and driving successful research outcomes.

What is the difference between Computational Spatial Transcriptomics vs Computational Biologist?

AspectComputational Spatial TranscriptomicsComputational Biologist
Required CredentialsAdvanced degrees in bioinformatics, computational biology, or related fields; experience with spatial data analysisTypically a PhD or Master's in biology, bioinformatics, or related disciplines; strong programming skills
Work EnvironmentResearch labs, biotech companies, academic institutions focusing on spatial genomicsResearch institutions, biotech firms, academia working on biological data analysis
Industry UsageSpecialized in spatial transcriptomics techniques and data interpretationBroad biological data analysis across various fields

Computational Spatial Transcriptomics focuses on analyzing spatial gene expression data within tissues, requiring specialized skills in spatial data processing. In contrast, Computational Biologists work on a wider range of biological data types. While both roles involve bioinformatics expertise, the former emphasizes spatial data analysis techniques specific to transcriptomics.

What is computational spatial transcriptomics?

Computational spatial transcriptomics is a field that combines advanced computational methods with spatial transcriptomics, a technique that measures gene expression within the physical context of tissue samples. It involves processing and analyzing large datasets to map where specific genes are active within tissues, helping researchers understand how cells interact and function in their native environments. This approach is crucial for studies in developmental biology, cancer research, and neuroscience, as it provides insights into cellular organization and tissue architecture. Computational tools help extract meaningful patterns from complex data, enabling discoveries that were previously impossible with traditional methods.

What are the key skills and qualifications needed to thrive as a Computational Spatial Transcriptomics Scientist, and why are they important?

To excel in Computational Spatial Transcriptomics, you need a strong background in bioinformatics, genomics, and statistical data analysis, typically supported by advanced degrees in computational biology or related fields. Familiarity with programming languages (such as R and Python), spatial transcriptomics platforms (like 10x Genomics Visium), and high-throughput sequencing data analysis tools is essential. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting complex datasets and collaborating with multidisciplinary teams. These competencies ensure accurate data interpretation, innovative research, and successful integration of spatial transcriptomics insights into biological and clinical applications.
What are popular job titles related to Computational Spatial Transcriptomics jobs in Tennessee? For Computational Spatial Transcriptomics jobs in Tennessee, the most frequently searched job titles are:
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Bioinformatics Analyst, Senior

Bioinformatics Analyst, Senior

Vanderbilt University

Nashville, TN • On-site

Full-time

Posted 26 days ago


Vanderbilt University rating

7.8

Company rating: 7.8 out of 10

Based on 38 frontline employees who took The Breakroom Quiz

193rd of 539 rated colleges and universities


Job description

Job Description
The Senior Bioinformatics Analyst is part of the Creative Data Solutions (CDS) Shared Resource at Vanderbilt University and serves as a senior-level contributor responsible for leading bioinformatics analyses of genomic data sets, developing and implementing advanced transcriptomic statistical models, and directing research studies. This position requires that the ideal candidate will have extensive experience in analyzing large-scale genomic and transcriptomic data, including next-generation sequencing data, and deep expertise in statistical analysis methods involved with such data. The candidate should demonstrate leadership capabilities, mentor junior team members, and possess exceptional communication and problem-solving skills. Reporting directly to the Director of CDS, the Senior Bioinformatics Analyst will lead collaborations with investigators throughout the Vanderbilt community on multiple complex projects simultaneously.
About the Work Unit:
Creative Data Solutions (CDS), a Vanderbilt University Shared Resource, provides informatics and bioinformatics services to investigators in the fields of academic and clinical research, with an emphasis on life sciences. CDS blends modern data science approaches with robust software and database engineering to provide practical solutions to problems and challenges faced by research investigators in a timely and cost-effective fashion.
Key Functions and Expected Performance:
  • Acts as a specialist in the analysis and interpretation of large-scale genomic and transcriptomic data sets using advanced bioinformatics tools and methods. Modalities include both bulk and single-cell RNA-Seq, DNA-Seq, chromatin binding analysis (CUT&RUN, ChIP-Seq, ATAC-Seq, etc.), spatial transcriptomics (10X Visium/HD, Nanopore GeoMX), and ad hoc bioinformatics.
  • Designs and develop complex sophisticated statistical models.
  • Leads the design and execution of research studies involving genomic and transcriptomic data.
  • Directs groups to conduct collaborative efforts with team members to interpret and present research findings.
  • Maintains expertise in the latest developments in bioinformatics, genomics, and transcriptomics.
  • Architects and maintain robust pipelines for processing and analyzing large-scale genomic data sets.
  • Supervises and coordinates with bioinformatics and laboratory personnel to ensure the quality and accuracy of genomic data.
  • Leads the interpretation and visualization of genomic data and prepares comprehensive reports and presentations for research studies and publications.
  • Drives the development of novel analytical methods and techniques for genomic data analysis.
  • Provides strategic bioinformatics leadership for clinical studies and trials involving genomic data.
  • Leads the development and implementation of data management and sharing policies for genomic data.
  • Mentors and trains team members and collaborators on advanced bioinformatics tools and methods.
  • Represents the organization at scientific conferences, workshops, and training programs to maintain cutting-edge knowledge in bioinformatics, genomics, and transcriptomics.
  • Coordinates externally with partners and stakeholders, such as academic and industry researchers, regulatory agencies, and patient advocacy groups, on research projects and initiatives.
  • Handles project timelines, resource allocation, and deliverable quality across multiple concurrent projects, while auditing processes to ensure compliance and efficiency.

Supervisory Relationships:
  • This position does not have supervisory responsibilities; this position reports administratively and functionally to the Director of CDS.

Education and Certifications:
  • Ph.D. degree in bioinformatics, computational biology, genetics, statistics, or a related field is required with at least 8 or more years of experience

Experience and Skills:
  • Post-doctoral experience or skill is preferred.
  • Experience with BioVU would be an added benefit.
  • Experience with long read RNA-Seq would be a strong advantage.
  • Background or experience in bioinformatics and a strong understanding of statistics is required.
  • Proficiency in programming languages R and Python
  • Knowledge of statistical analysis methods for genomic data, including GWAS, rare variant analysis, and eQTL analysis.
  • Familiarity with common bioinformatics tools and databases, such as Ensembl, UCSC Genome Browser, and dbGaP.
  • Familiarity with HPC and cloud computing infrastructure.
  • Excellent communication and problem-solving skills.
  • Ability to work independently and as part of a team

About Us
At Vanderbilt University , our work - regardless of title or role - is in service to an important and noble mission in which every member of our community serves in advancing knowledge and transforming lives on a daily basis. Located in Nashville, Tennessee, on a 330+ acre campus and arboretum dating back to 1873, Vanderbilt is proud to have been named as one of "America's Best Large Employers" as well as a top employer in Tennessee and the Nashville metropolitan area by Forbes for several years running. We welcome those who are interested in learning and growing professionally with an employer that strives to create, foster and sustain opportunities as an employer of choice.
We understand you have a choice when choosing where to work and pursue a career. We understand you are unique and have a story. We want to hear it. We encourage you to apply today so that you might become a part of our story.
Vanderbilt University is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran, or any other characteristic protected by law.

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