Bioinformatics Analyst IV, ITEB, CGR

Job

Employee Type: exempt full-time

Division:
Clinical Research Program

Facility:
Rockville: 9609 Med Ctr Dr

Location:

9609 Medical Center Dr, Rockville, MD 20850 USA

The Frederick National Laboratory is operated by Leidos Biomedical Research, Inc. The lab addresses some of the most urgent and intractable problems in the biomedical sciences in cancer and AIDS, drug development and first-in-human clinical trials, applications of nanotechnology in medicine, and rapid response to emerging threats of infectious diseases.

Accountability, Compassion, Collaboration, Dedication, Integrity and Versatility; it's the FNL way.

We are seeking a skilled and motivated Bioinformatics Analyst to join the Cancer Genomics Research Laboratory (CGR), located at the National Cancer Institute (NCI) Shady Grove campus in Rockville, MD. CGR is operated by Leidos Biomedical Research, Inc., and collaborates with the NCI’s Division of Cancer Epidemiology and Genetics (DCEG) - the world’s leading cancer epidemiology research group.

Our scientific team leverages cutting-edge technologies to investigate genetic, epigenetic, transcriptomic, proteomic, and molecular factors that drive cancer susceptibility and outcomes. We are deeply committed to the mission of discovering the causes of cancer and advancing new prevention strategies through our contributions to DCEG’s pioneering research.

Our team of CGR bioinformaticians supports DCEG’s multidisciplinary family- and population-based studies by working closely with epidemiologists, biostatisticians, and basic research scientists in DCEG’s intramural research program. We provide end-to-end bioinformatics support for genome-wide association studies (GWAS), methylation profiling, targeted, whole-exome, whole-transcriptome and whole-genome sequencing along with viral and metagenomic studies from both short- and long-read sequencing platforms.

Our work spans germline and somatic variant detection, structural and copy number variation, microsatellite analysis, mutational signature profiling, gene and isoform expression, base modification analysis, viral and bacterial genomics, and more. Additionally, we advance cancer research by integrating latest technologies such as single-cell and spatial transcriptomics, multiomics and proteomics, in collaboration with the Functional and Molecular and Digital Pathology Laboratory groups within CGR.

We extensively analyze large population databases such as All of Us, UK Biobank, gnom

AD and 1000 genomes to inform and validate GWAS signals, study the association between genetic variation and gene expression, protein levels, and metabolites and to develop polygenic risk scores across multiple populations.

Our bioinformatics team develops and implements sophisticated, cloud-enabled pipelines and data analysis methodologies, blending traditional bioinformatics and statistical approaches with cutting-edge techniques like machine learning, deep learning, and generative AI. We prioritize reproducibility through containerization, workflow management tools, thorough benchmarking, and detailed workflow documentation. Our infrastructure and data management team works closely with researchers and bioinformaticians to maintain and optimize a high-performance computing (HPC) cluster, provision cloud environments, and curate and share large datasets.

The successful candidate will provide dedicated analytical support to the Integrative Tumor Epidemiology Branch (ITEB) and contribute to cancer research through their expertise in DNA repair, lung cancer, epidemiology, and cancer genetics to advance the Sherlock-Lung Study, a large-scale initiative investigating the genomic, transcriptomic, methylation, and microbiome landscapes of lung cancer in never smokers to uncover mutational processes, molecular changes, and tumor evolution.

• Formulate hypotheses for large-scale cancer studies and test them by analyzing single nucleotide variants (SNVs), indels, structural variants (SVs), copy number alterations (SCNAs), clonal and subclonal drivers, and mutation signatures to characterize intra-tumor heterogeneity, with a particular…