The Genomics Section of the Systems Technologies Core (STC) is led by Dr. Andy Baltzegar who directs the Genomic Science Laboratory (GSL) at NC State. Dr. Baltzegar is the Genomics Navigator for CHHE and all requests for consultation, support and services must go through the STC portal https://labs.ncsu.edu/link/SelectLab/STC for CHHE Project Requests , watch this short video for addtional information.
All CHHE Full Members are eligible for the CHHE’s new STC Voucher Program. Under this program, CHHE provides 50% of the costs up to $15K/Full member for $30K STC services.
Powerful high-throughput sequencing technology available through the Genomic Sciences Laboratory (GSL) supports CHHE researchers to conduct complete transcriptome analysis whereby both transcriptome development and abundance are examined within a single experiment. These same technologies and resources will also allow for genome-wide studies of DNA/protein interactions (ChIPseq), gene mutation analysis, SNP analysis, DNA methylation changes, RNA-seq and micro RNA analysis, eQTL analysis, Genotyping by Sequencing (GBS), and Genome Wide Association Studies (GWAS).
The Aims of the Genomic Section are:
Specific Aim 1: Provide expertise, guidance and consultation on experimental design for innovative -omics technologies involving genomics, epigenomics, and transcriptomics
Dr. Baltzegar consults with CHHE members and their laboratory teams and provides insight on the appropriate experimental design and best practices in order to achieve their project goals. Project meetings with Dr. Baltzegar and his GSL team enable detailed discussions on the appropriate sequencing level required to meet specific aims, the level of biological replication needed for robust statistical analysis, and sample collection and storage procedures to creation quality DNA or cDNA libraries. The GSL closely interacts with CHHE’s Environmental Data Science Group (EDSG – see Aim 3) at all project stages (conception to completion), to provide an efficient sample collection to data analysis pipeline that maximizes research goals while minimizing costs.
Specific Aim 2: Perform targeted and global -OMICS analyses with state-of-the-art instrumentation and resources via a highly trained and qualified team.
The GSL provides NC State and the CHHE member community with access to high-throughput DNA sequencing and genotyping equipment to perform functional genomic assays and mapping. The GSL provides the tools and expertise to perform all supporting services, including: nucleic acid extractions, construction of genomic libraries and sequencing, and raw data quality control (QC) analysis before it is provided to the customer’s analysis team. Powerful high-throughput sequencing technologies available in the GSL will support CHHE researchers to conduct genome-wide studies of DNA/protein interactions (ChIPseq), gene mutation analysis, SNP analysis, DNA methylation changes, RNAseq and micro-RNA analysis, eQTL analysis, Genotyping by Sequencing (GBS) Genome Wide Association Studies (GWAS), comparative Whole Genome Sequencing, and Target Amplicon sequencing approaches. The GSL provides preliminary quality checks of sequencing data and links to the EDSG/BRC for storage and analysis of the enormous amounts of data produced by high-throughput sequencers.
Specific Aim 3: Perform and provide a seamless link to CHHE’s Environmental Data Science Group for training on analyses, advanced pathway mapping, data visualization and data storage.
Bioinformatics support is provided by the CHHE’s Environmental Data Science Group (EDSG) which is a section of the Integrative Health Science Facility Core. The EDSG brings together five bioinformaticians/biostatisticians. The EDSG will conduct informatics analyses of large data sets from in silico, in vitro, in vivo, and population studies to address important problems in EHS. Bioinformatic support is provided cost-free to CHHE Full members (see Bioinformatic Support section of IHSFC).
Ingenuity Pathway Analysis (IPA)
An IPA software license is purchased yearly by CHHE for access by all members and cores. IPA has broad adoption by the life science research community and is cited in thousands of articles for the analysis, integration, and interpretation of data derived from -omics experiments, such as RNA-seq, small RNA-seq, microarrays including miRNA and SNP, metabolomics, and proteomics. Mummichug in MetaboAnalyast and KEGG pathway analysis in Compound Discoverer are also used.
Visualization is vital to the analysis of large datasets and multi-omic integration. In partnership with the EDSG (see Data Visualization section of IHSFC) members are directed to personnel and software to generate appropriate data graphics.