We focus on high throughput quantitative proteomics and disease-induced biomarkers studies at newly-founded Advanced Clinical BioSystems Research Institute (ACBRI) at Cedars-Sinai Medical Center Smidt Heart Institute. We perform experiments in cutting edge mass spectrometry applications in biomarker discoveries and validations. Our projects are on the development of and application of targeted protein quantification techniques for studying specific cellular pathways at the molecular level and for verification of protein candidate biomarkers for diagnosis, prognosis, and therapeutics from cells, tissues, and biofluids for heart diseases.

Under supervision of the Bioinformatics Lead, the Research Bioinformatician I (RBI) provides complex bioinformatics data analysis & software development support for projects involving omics data. W ill manage omic data including loading and querying data from database system and public repositories, and transform and merge multilevel omic data into user-friendly formats, assist in development, test, and maintenance of modular software pipelines, provide daily bioinformatics data analysis, preliminary interpretation of the data, and reports of the results for possible publications.

Essential job 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.