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The Research Bioinformatician I performs general bioinformatics analysis and software support for projects involving in omics. The RBI manages omic data including loading and querying data from database system(s) and public repositories, and transforms and merges multilevel omic data into user-friendly formats.
The RBI assists in development, testing, and maintenance of modular software pipelines, provides daily bioinformatics data analysis, preliminary interpretation of the data, and reports of the results for possible publications.
Primary Duties and Responsibilities
Provides general bioinformatics analysis support for omic projects and mainly responsible for data analysis and software compilations including next generation sequence alignment, polymorphism identification, expression analysis, and visualization tools and browsers
Summarizes data analyses results in the form suitable as the basis for the first draft of written reports, and makes preliminary interpretations of the data.
Assists in development, testing, and maintenance of modular software pipelines for genome sequencing, assembly, annotation, metagenomic analysis, and genotyping using high-throughput sequencing platforms including 454, Illumina, and Pacific Biosciences.
Manages data including loading and querying data from database systems, downloading omic data from public repositories, and transforming data to the necessary formats.
Helps to identify, evaluate, and incorporate relevant algorithms and software pipelines by reviewing pertinent literatures in bioinformatics and computational biology.
Education:
Work Experience:
Three years in a research environment.
Preferred skills: Background and work knowledge in algorithms, scientific computing, and machine learning or statistics, Familiar with C/C++, Java, Perl, python, and the Unix (Linux) environment, Experiences in manipulating, analyzing, and annotating very large genomic (e.g. NGS) data sets, both in exploratory and pipelined fashions, will be a plus.
Experiences in manipulating, analyzing, and annotating very large genomic (e.g. NGS) data sets, both in exploratory and pipelined fashions, will be a plus
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