Summary

The Bioinformatics Analyst I will analyze and integrate multi-modal molecular signatures in normal human and disease populations using bioinformatics and computational techniques and concepts. The job will include both deployment and development of state-of-the art analysis pipelines. Analysis will further incorporate advanced data science techniques such as deep learning and classification to identify potential disease drivers or biomarkers, repurposable drugs, and novel clinical associations of molecular profiles. 
 

Job Duties

• Assist the RPPA staff design antibody array, help with image analysis, assess quality control, process and normalize the RPPA data, generate reports for the RPPA core customers. The analyst will further analyze antibody proteomics datasets using both parametric and non-parametric approaches. 
• Map bulk RNA-seq data and quantify gene expression using cluster computing. The analyst will detect differentially expressed genes using multiple R analysis packages, run pathway enrichment, and generate visualizations including heatmaps. 
• Map and quantify single cell RNASeq data using cluster computing. Cell subpopulations will be identified and characterized, and gene signatures and enriched pathways will be generated for individual cell types. Visualizations of cell types, gene markers, enriched pathways will be generated.
• Process DNA methylation and histone modifications ChIP-Seq data, both in bulk tissue and in single-cell experiments. The analysis will determine differential epigenomic features, associate them with nearby genes, then assess enriched pathways. 
• Normalize and assess the quality of MS Metabolomics and Lipidomics data. The analyst will analyze MS Metabolomics data using both parametric and non-parametric methods and generate both metabolic signatures and visualizations including heatmaps and boxplot for different experimental groups. 
• Integrate datasets from multiple omics platforms generated in the BCM cores or downloaded from public repositories such as NCBI GEO, Metabolomics Workbench, or Proteomics Workbench. The analysis will meet users and explain analysis and integration strategies employed, and assist also with preparation of quality figures for manuscripts and grant submissions. Analysis will further incorporate advanced data science techniques such as deep learning and classification to identify potential disease drivers or biomarkers, repurposable drugs, and novel clinical associations of molecular profiles.
• Review existing multi-modal omics signatures, publicly available datasets, propose and execute integrative analysis. 
• Interpret results from similarity searches and integration of investigator and publicly available datasets. 

Minimum Qualifications

• Bachelor's degree in Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related field.
• No experience required.

Preferred Qualifications

• Master of Science or PhD are preferred. 
• Python/R programming, bioinformatics analysis of RNA-Seq, ChIP-Seq, pathway analysis, Unix/Linux, PBS or SLURM cluster based computing.
• R/Shiny / JavaScript/Django web development, database management, Machine Learning.

Other

The successful candidate will receive training in analyses of multi-omics data and be expected to participate in multiple projects involving design, implementation and integration of large-scale datasets and data processing pipelines. Successful candidates will be expected to pass a basic programming test in R or Python. Due to the nature of the position, successful candidates must have excellent written and verbal English skills, strong communication and interpersonal skills, and be capable of working with large collaborative teams.