NICHD Bioinformatics and Scientific Programming Core

Eunice Kennedy Shriver National Institute Of Child Health & Human Development

Investigators

Linked publications, trials & patents

Abstract

NICHD's Bioinformatics and Scientific Programming Core (BSPC) consists of a central core of staff who coordinate with embedded bioinformaticians working directly in laboratories. This results in centralized infrastructure that is reusable across many projects while also providing focused and custom local support. Analyses performed by BSPC make extensive use of NIH's Biowulf high-performance computing cluster. Projects continued from previous reporting periods include: identification of 3' ends of bacterial transcripts; integration of single-cell ATAC-seq and single-cell RNA-seq to identify poised enhancers related to neuronal development; detection of random monoallelic expression using single-cell RNA-seq in zebrafish brain; development of track hubs to visualize genomic data across many different experiment types; differential methylation in Cushings disease patients; variant calling in patients in several rare diseases; insertion of HIV into the human genome and its relationship with LEDGF chromatin binding protein; differential expression, translational efficiency changes, and nucleosome occupancy in a wide variety of mutant yeast strains using RNA-seq, Ribo-seq, and ChIP-seq; bulk and single-cell RNA-seq analysis of differentially expressed genes during Xenopus tropicalis development; regulation of development by thyroid hormone receptor in X. tropicalis using RNA-seq and ChIP-seq; identification of genome-wide RNA-RNA interactomes in E. coli using RNA-seq and RIL-seq; extended developmental time course transcriptomic analysis using scRNA-seq in zebrafish; scRNA-seq analysis to characterize GABAergic neurons; analysis of multiome (scRNA-seq + scATAC-seq) data from mouse brain; evaluation of a fluorescent assay to find transposon integration events genome-wide; an integrative analysis of 5 and 3 ends of bacterial transcripts in multiple species and conditions; CUT&RUN analysis in several model systems; CRISPRi screens in various neuronal systems; reimplementing and extending software for the TRIP assay; large-scale aggregation of many experiments looking at perturbations of basket nucleoporins; analysis and tool development for various ribosomal profiling assays; mass spec of basket nucleoporins to help dissect their structure; transcriptomic validation of a novel microfluidic device for culturing axons; development of a Tn-seq analysis workflow and application of it to multiple bacterial species; various scRNA-seq and snRNA-seq analyses in a wide range of systems; spatially-resolved scRNA-seq in developing mouse palate; single-cell sequencing of Borrelia cells; somatic variant calling in micropapillary thyroid cancer patients and integration with matched bulk RNA-seq data; germline variant calling to disentangle a complex phenotype in a long-running mouse strain; germline variant calling to confirm and extend known variants related to gigantism; and metabolomics in various celltypes. New projects this reporting period included RNA-seq after optogenetic manipulation of developmental pathways; assessing mutations in a collection of bacterial strains to identify mutational hotspots; using a multiomics approach to understand embryonic tooth development in mouse; scRNA-seq of human ovaries to identify novel cell types; characterizing R-loops in a mouse model using DRIPc-seq; identifying protein interactors in multiple model systems and mutants; correlation of neuron imaging data with scRNA-seq; developing a generalized tool for improved depletion of ribosomal RNA from bacterial RNA-seq samples; and additional single-cell and bulk RNA-seq analyses in a range of systems and with a range of experimental designs. BSPC continues to develop and maintain lcdb-wf, a system of workflows and pipelines to process high-throughput sequencing data, run extensive quality control, and perform differential ChIP-seq or RNA-seq analyses and which runs on NIHs Biowulf high-performance computing cluster. We develop custom web applications using R Shiny that allow our collaborators to explore and compare their data in powerful ways without requiring bioinformatics expertise. In particular, this year we continued to improve our bulk RNA-seq application based on feedback from users and are now refactoring it for public use; developed a new single-cell interactive web application; and continued development of a new interactive application for teaching the principles of the DESeq2 algorithm for differential expression. We train users in NICHD and other ICs to use these tools and others on their own data. We have also continued to contribute to the Bioconda project, a system used by bioinformaticians worldwide to easily install biology-related software tools.