Bioinformatics Systems Analyst 1 - Sequencing Technology - 75271
Organization: GN - Genomics
The DOE Joint Genome Institute (JGI) in Walnut Creek, CA (a division of the Lawrence Berkeley National Lab) has an exciting opportunity available for a motivated Bioinformatics Systems Analyst. As part of the Sequencing Technology group, will assist with the development of computational tools to perform sequence analysis for research and development projects surrounding genome, transcriptome and chromatin characterization. Duties will include evaluating the nature of the biological challenges, design of molecular biological approaches and short read sequencing data, as well as performing analysis of DNA sequence data produced by next generation sequencing machines and various library types. Will be involved in the development and implementation of basic analysis pipelines, and providing technical and analytical interpretation of data. Will also be responsible for improving existing data analysis tools and implement new tools for general use. The position will provide assistance with defining the biological significance of the data, will work with various JGI groups on data analysis projects and facilitate transfer of knowledge of technologies and applications to JGI staff. May also provide custom analysis support to external collaborators. Position reports to the Sequencing Technology Group analysis work lead.
Specific Job Duties
Sequence Analysis and Interpretation
• Perform quality analysis of sequence data and assist others in interpretation of analyzed data.
• Extract new biological insights and phenomenon from the analyzed results.
• Proficiently utilize assembled genomes, various alignment tools, visualization tools, and other genome interrogation and analysis software.
• Provide feedback on data quality to wet-lab scientists and sequencing team members.
• Provide clear and concise professional reports on data analysis and results.
• Present technical and analytical status in weekly meetings.
Bioinformatics Tool Adaptation and Improvement
• Assist with the adaptation and improvement of bioinformatics tools to process sequencing data generated by various R&D library types and projects.
• Assist with the design and development of new tools for the new R&D projects.
• Refine and improve existing analytical tools.
• Design and build new tools to analyze sequences of various research projects.
• Troubleshoot basic system and data analysis problems and provide feedback on areas of improvement for existing tools.
• Provide technical support to detect issues and improve library protocols.
• Provide high quality documentation for newly developed software and approaches.
Key Success Factors
• Typically requires a B.S. in Bioinformatics, Computer Science, Biology or a related field and some related experience, or an equivalent combination of education and experience. Master’s Degree preferred.
• Experience in genome bioinformatics and the collection, recording and analysis of experimental data.
• Familiarity with DNA sequencing, short read alignment programs and data analysis.
• Knowledge and experience with standard bioinformatics methods and tools, including sequencing databases.
• Familiarity with various molecular biology procedures, including DNA sequencing, PCR and cloning.
• Ability to process large volumes of data, whole-genome data analysis preferred.
• Proficiency with programming languages including Perl, C/C++, Java or others in a Unix environment as well as web-based interface.
• Experience with SQL, MySQL, Oracle or other RDBMs.
• Demonstrated analytical skills sufficient to troubleshoot system and data analysis problems of limited scope and make recommendations.
• Strong organizational and record-keeping skills.
• Effective oral and written communication and interpersonal skills.
• Ability to follow laboratory safety guidelines.
• Knowledge and experience with laboratory DNA sequencing technologies and genetics, or related field or equivalent experience preferred.
• Experience with database structure and management.
• Familiarity with statistical tools and interpretation of data.
• Familiarity with machine learning, pattern recognition, or modeling to scientific problems.
• Experience in a multi-disciplinary scientific or production environment.