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. The Bioinformatics Analyst will also have the opportunity to observe the process and procedures of day-to-day job duties through training from the Bioinformatics team.Job Duties
- The Bioinformatics group will provide training and compute resources for the analysis, and regular guidance on the projects performed.
- 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.
- Analyze antibody proteomics datasets using both parametric and non-parametric approaches.
- Map bulk RNA-seq data and quantify gene expression using cluster computing and 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.
- 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.
- Bachelor's degree in Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related field.
- No experience required.
- A Bachelor of Science in Computer Science/Engineering, Bioinformatics, Mathematics and a minimum of two years experience are required.
- 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
Baylor College of Medicine requires employees to be fully vaccinated -subject to approved exemptions-against vaccine-preventable diseases including, but not limited to, COVID-19 and influenza.
Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.