NHGRI/DIR Bioinformatics and Scientific Programming Core

National Human Genome Research Institute

Investigators

Abstract

The Bioinformatics and Scientific Programming Core actively supports the research being performed by NHGRI/DIR investigators by providing expertise and assistance in scientific programming and computational analysis. The Core facilitates access to specialized software and hardware, develops generalized software solutions that can address a variety of questions in genomic research, develops database solutions for the efficient archiving and retrieval of experimental and clinical data, disseminates new software and database solutions to the genome community at-large, collaborates with DIR researchers on computationally intensive projects, and provides educational opportunities in bioinformatics to trainees. Support for projects includes not only data analysis but also related efforts focused on data collection through the public DIR research web site, located at https://research.nhgri.nih.gov. Additional information can be found on the Cores web site, at https://dir.nhgri.nih.gov/nhgri_cores/BSPC. Projects performed during the reporting period include: • Identifying rare haplotypes, comprised of common frequency variants, that may explain missing heritability in rare recessive diseases • Variant calling aimed at mapping sex determination loci and producing fine-scale mapping data for the genomic region controlling histocompatibility in Hydractinia • Implementing a gene prediction pipeline and genome data portal for two Hydractinia genomes and the Capitella teleta genome • Generating a proteome-scale dataset resource of computationally predicted protein structures for Mnemiopsis leidyi • Bioinformatic support for the Reverse Phenotyping Core, including implementation and maintenance of a gnomAD-style variant browser, for establishing a shared genomic ascertainment cohort of at least 18,000 individuals whose genomes or exomes have been sequenced and are recallable for secondary phenotyping studies. • Synthesizing variant data from six cohorts, adding 9200 genomes and 2300 exomes to the existing 4700 genomes and 2000 exomes already shared in the RPC Genomic Data Browser • Assessing allele-specific expression differences in four laboratory zebrafish strains and identify eQTLs • Analyzing whole exome sequencing data to identify rare and low frequency variants that contribute to Sjögren’s disease and related clinical outcomes • Assessing the feasibility of using DNA methylation data as a biomarker of disease heterogeneity in systemic lupus erythematosus (SLE) • Analyzing RNA-seq data to assess transcriptomic alterations in a mouse model for Hermansky-Pudlak Syndrome (HPS), also assessing the efficacy various gene therapy modalities provide for gene complementation and disease treatment • Identifying DNA methylation marks in parents caring for children with inherited metabolic disorders • Performing platelet transcriptome analysis in patients with familial platelet disorder with associated myeloid malignancies • Determining differences in the genome-wide DNA methylation landscape between CBFB-MYH11 knock-in mice that serve as a model for leukemia and wildtype controls • Analyzing EM-seq, ChIC-seq, and RNA-seq data to assess changes in genome-wide DNA methylation, RUNX1 binding, and gene expression in iPSCs derived from patients with RUNX1 mutations • Performing single-cell RNA-seq analyses in peripheral blood from patients with mitochondrial disease • Analyzing ATAC-seq, ChIP-seq and RNA-seq data from from effector and memory T-cells in wild-type and pyruvate dehydrogenase deficient T-cells to examine the chromatin and transcriptional landscape • Analyzing sequencing data to assess key parameters of the B-cell receptor repertoire, including clonal diversity, VDJ segment usage, and potential selection biases • Variant calling and downstream analysis of whole-genome sequencing data from five obese and five phenotypically normal mice from the same line, in order to flag the mutation that led to obese mice after brother-sister mating • Continuing maintenance of a customized database and web interface for storing and computing on genomic data from dogs • Design and implementation of surveys that assess the health of pet dogs whose DNA samples have been submitted for use in scientific studies, and • RNA-seq analyses of post-mortem brain tissue to compare neuronal gene expression in youths with a history of ADHD against matched controls in order to establish a neuronal transcriptome and determine the genes and neural gene networks that influence the development of ADHD. Finally, recognizing the importance of having a degree of facility with computational approaches, the Core has offered a number of courses that cover various areas of the bioinformatic landscape during the reporting period.