Bioinformatics Analyst III

Program Description:
The Cancer Genomics Research Laboratory (CGR) investigates the contribution of germline and somatic genetic variation to cancer susceptibility and outcomes in support of the National Cancer Institute (NCI)'s Division of Cancer Epidemiology and Genetics (DCEG). Working in concert with epidemiologists, biostatisticians and basic research scientists in DCEG's intramural research program, CGR provides the capacity to conduct genome-wide discovery studies and targeted regional approaches to identify the heritable determinants of various forms of cancer. This includes the design and analysis of high throughput studies using various types of “-omics” technologies such as array- and sequence-based genome-wide association studies, studies of tumor characteristics using integrated genomic data analysis, and molecular epidemiologic studies based on novel metabolomic and microbiomic assays.

Job Description/Basic Function:
The Cancer Genomics Research (CGR) laboratory in Gaithersburg, MD, is a fast-paced, high-throughput organization dedicated to the support of molecular, genetic and epidemiologic studies for investigators at the National Cancer Institute's Division of Cancer Epidemiology & Genetics (DCEG). The Division includes over 70 principal investigators in epidemiology, genetics, and biostatistics who conduct multidisciplinary family- and population-based research to discover the genetic and environmental determinants of cancer, and new approaches to cancer prevention. We are seeking a highly motivated bioinformatics analyst to join the bioinformatics team at the CGR and provide analytical support to DCEG. Working with DCEG investigators, external collaborators, CGR management and staff, the successful incumbent will provide bioinformatics support to the extensive DCEG analytical efforts, specifically:

• Designing, developing and maintaining complex bioinformatics pipelines to support the analysis of high-throughput data
• Analysis and interpretation of high-through genomic data generated by microarray and next-generation sequencing platforms
• GWAS analytical tasks including data QC, imputation, population structure analysis, association analyses
• Accessing, extracting and preparing data for analysis, including combining data run on multiple platforms as well as externally generated data in support of meta-analyses
• Harmonizing and maintaining diverse data with associated metadata
• Organizing results into clear presentations (including QQ-plots, Manhattan plots) and concise summaries of work, in formats useful for scientific interpretation
• Maintaining experience with state-of-the-art bioinformatics tools and data repositories

Basic Qualifications:

To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below:

• Possession of a Bachelors degree in biomedical science/bioinformatics/math/computer related field from an accredited college or university according to CHEA. (Additional qualifying experience may be substituted for the required education). Foreign degrees must be evaluated for U.S. equivalency
• A minimum of five (5) years of related analytical or bioinformatics software development experience
• Hands-on experience in processing and analyzing, genotyping, sequencing, expression and other data utilizing R/Bioconductor (including quality and biological assessment) is essential
• Familiarity with diverse analytical tools (e.g. PLINK, GATK, GLU, IMPUTE2+snptest, BEAGLE, UCSC Genome Browser, etc.)
• Programming experience with Shell, Python, Perl, C/C++, and/or JAVA
• Team oriented with excellent written and verbal communication skills, with demonstrated ability to self-educate in current bioinformatics techniques and resources
• Must be able to obtain and maintain a security clearance

Preferred Qualifications:

Candidates with these desired skills will be given preferential consideration:

• Master's degree or Ph.D. in bioinformatics, biostatistics, computer science, computational biology and other related disciplines
• High throughput pipeline development experience
• Strong knowledge of genome-wide association analysis and interpretation, and applied computational research on large multivariate datasets
• Hands-on experience with one or more high throughput analysis pipelines such as ExomeSeq, RNASeq, ChIPSeq, scRNASeq, microarray analysis, etc.
• Familiarity with publicly available data sources (such as dbGaP, TCGA, ENCODE, 1000 Genomes, TARGET, GTEX) and diverse genomic annotations
• Experience managing large datasets and computational tasks, experience working in a Linux environment (especially a compute cluster environment)
• Experience in the field of molecular and population genetics

Expected Competencies:

• Careful attention to detail. Must be analytical, flexible, innovative, and self-motivated.
• Ability to organize and execute multiple projects in parallel, work in collaborative environment and contribute to team projects
• Ability to carry out a wide range of high throughput data analyses, and troubleshoot with minimal supervision
• Ability to develop bioinformatics pipeline to handle large genomics datasets
• Excellent written and oral communication-ability to communicate clearly and effectively with staff and other groups within CGR and DCEG, and able to interpret data and write scientific reports as well as other written communications