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Postdoc position at University of California, San Diego

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  • Postdoc position at University of California, San Diego

    Our lab is looking for a postdoc with bioinformatics experience to analyze WES and RNAseq results. Below is an overview of what is posted for job description.

    Interested candidates should contact [email protected].


    The successful candidate's work will be multi-faceted, largely tasked with assisting in the analysis of a diverse collection of biological data. Other responsibilities will include providing ad-hoc technical support, tools building for specific projects and system maintenance. The primary tasks revolve around development and maintenance of a variant analysis pipeline that processes second generation sequence data, integrates internal and external data, and filters bases on an array of metrics. This pipeline is currently relies on a MySQL database so database management and SQL expertise are required to broaden the schema of this database and create views into the data. Additional work includes creating new visual summaries of the data including establishing a genomic browser, automatically generated paper quality figures and performing statistics based analysis. Projects may require some web-based tool development and some minor IT assistance maybe requested.
    A. Maintain, develop and test algorithms developed in the project.

    B. Use Python, PERL, C++, etc to further develop the software in the project.

    C. Annotate and test software, and make public release.

    D. Regularly maintain software pipelines.

    E. Write supporting tools using Perl or Python or similar scripts

    Maintain and optimize MySQL relational database to manage annotated sequence results.

    B. Develop solutions for storage and retrieval of large datasets consisting of thousands of patients with 20,000-30,000 of existing and newly discovered variants per patient.

    C. Develop queries and views into a mySQL database that stores all of our patient variant information. These queries will be used to perform comparisons between groups of patients to identify potentially interesting variants for downstream functional validation.

    D. Develop tools to integrate experimental data with standard datasets such as dbGaP and the human genome browser to enable downstream analysis.