The mission The Bioinformatics Core facilitates, amplifies, and accelerates biological and medical research and discovery through the application of the latest bioinformatics methods and technologies. This mission is achieved by delivering high quality and comprehensive support for experimental design, analysis and visualization in a timely fashion. The core is responsive to research scientists needs and effectively evolves with advances in the field. The core services include but are not limited to the following: Statistical analysis including basic statistical analysis, advanced statistical analysis (e.g., linear and generalized mixed model analysis, longitudinal modeling), and custom statistical methods (e.g., tailored to specific research projects) Omics Analysis including: Transcriptomics (Microarray and RNA-seq), Genomics (Genome, exome and targeted DNA-seq), Epigenomics (Methyl-seq, ChIP-seq, etc.), Proteomics, and Metabolomics Gene/Target/Disease Analysis: Functional annotation at variant, gene, and geneset level, Interaction analysis, and Pathway enrichment analysis Ad-hoc consultation: Advise on experimental designs, data management and analysis Computing resource development and maintenance, including Bioinformatics Software Development, Systems Toolkits Development, customized biological databases and Web Services development Training: Personalized training to match users specific requirements and group training and workshops Scientific impact We routinely meet with PIs and/or researchers to discuss the types of analyses and support that our statistical and bioinformatics expertise can provide to help them design experiments. We help with grant proposals to incorporate statistical and computational biology methods or techniques. We also prepare letters of support for such grant proposals. The Core statistical and bioinformatics consulting and collaboration covers a broad range of study design, statistical analyses and data science issues including experimental design for preclinical investigations, population studies, and Big Data analytics, and statistical analysis of next generation sequence data and omics data. The core is also actively involved in statistical and bioinformatics methods research and development and collaborates with PIs, Postdocs, and Staff in writing and implementing competitive project proposals involving methodological and software development. In fiscal year 2017, the core actively participated in 3 CHIRP grant proposals, provided letters of support for 4 postdoctoral K award applicants and as mentioned above 4consulted and collaborated with 31 PIs, and staff for their experimental design and analyses. The core has also contributed to 5 manuscripts and more than 15 posters and abstracts for conference presentation. Cores research and method development The Bioinformatics core in collaboration with laboratories at the MHLBI is heavily involved in research and new methods developments. Summarized below are few examples. > lncRNA detection and functional annotation: With advances in Next Generation Sequencing (NGS), the accessibility of affordable high-throughput sequencing is now generating a wealth of novel, unannotated transcripts, especially long noncoding RNAs (lncRNAs) that are derived from genomic regions that are antisense, intronic, intergenic, and overlapping protein-coding loci. Parsing and characterizing the functions of noncoding RNAs and lncRNAs in particular, is one of the great challenges of modern genome biology. In collaboration with Dr.
H aimi ng Cao, we devised computational methods for the identification and functional characterization of lncRNAs from genomic and transcriptomic data. We also developed bioinformatics pipelines to identify lncRNAs in a dataset and then, employed machine learning approaches for functional characterization of the fragments. > Proteogenomics: In recent years, with advances in NGS technologies such as RNA- Seq and mass spectrometry-based proteomics, the new era of Proteogenomics research has emerged. In collaboration with several PIs and our Proteomics Core at the NHLBI, we developed customized protein sequence databases using genomic and transcriptomic information to help identify novel peptides that are not present in reference protein sequence databases from mass spectrometry-based proteomic data. We also developed pipeline and computational strategies for building and using customized protein sequence databases. The proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. > Software development for cell free DNA: Donor-derived cell-free DNA (dd-cfDNA), is an emerging biomarker of acute cellular rejection in organ transplant recipients. dd- cfDNA is derived from the transplanted organ and detectable in blood and urine of transplant recipients. In collaboration with Dr. Valentines laboratory, we developed methods for quantification of cell-free DNA (cfDNA) in circulating blood derived from a transplanted organ as a powerful approach for monitoring post-transplant injury and outcome. Our pipeline quantifies donor-derived cfDNA (dd-cfDNA) by discriminant analysis of single-nucleotide polymorphisms (SNPs) between donor and recipient DNA molecules that are distributed across the genome. Plasma levels of dd-cfDNA were assessed for utility in diagnosing rejection and evaluating treatment response in transplant recipients in a longitudinal observational trial. Training and education We train users on data analysis and help with the manuscript preparation. We meet with investigators who are starting new projects to discuss the best approaches and help with the experimental design. Currently several post-bacs and post-docs frequently visit and consult with core staff on a regular basis to advance their study.
| Agbor-Enoh, Sean; Jackson, Annette M; Tunc, Ilker et al. (2018) Late manifestation of alloantibody-associated injury and clinical pulmonary antibody-mediated rejection: Evidence from cell-free DNA analysis. J Heart Lung Transplant 37:925-932 |
| Dillon, Laura W; Hayati, Sheida; Roloff, Gregory W et al. (2018) Targeted RNA-sequencing for the quantification of measurable residual disease in acute myeloid leukemia. Haematologica : |
| Agbor-Enoh, Sean; Chan, Joshua L; Singh, Avneesh et al. (2018) Circulating cell-free DNA as a biomarker of tissue injury: Assessment in a cardiac xenotransplantation model. J Heart Lung Transplant 37:967-975 |
