Bioinformatics and Biostatistics Core

Yale University, New Haven CT

Investigators

Linked publications & trials

Abstract

The Biostatistics and Bioinformatics Core (BBC) supports statistical, bioinformatic and computational needs of the Discovery and Targeted Proteomics Cores, as well as Center Investigators, their postdoctoral associates and students, and Pilot Grant awardees. The BBC has four inter-related Specific Aims: 1) Biostatistics; 2) Bioinformatics; 3) High Performance Computing; 4) Training and Education. In Aim 1, we will provide statistical guidance on experimental design and data analysis, including sample quality assessment, and exploratory analysis for a wide range of types of proteomics data sets; explore the application of deep learning methods in the analysis and interpretation of proteomics data.; implement methods integrative analysis of transcriptomics and proteomics data; evaluate and implement different methods for the analysis of proximity labeling data; and continue to develop an online tool for proteomics data preprocessing, including data normalization, batch effect correction, and missing data imputation. In Aim 2, we will provide advanced bioinformatics software and approaches to assist Center investigators and Pilot Grant awardees in fully interpreting their comparative protein and protein post-translational modification profiling data; continue to develop an integrative framework that connects cell-type-specific RNA abundances with analogous protein abundances, to infer cell-type-specific transcriptional parameters from single-cell RNA-seq data; and build on our comprehensive generation of uniformly processed human brain datasets to address allele-specific gene expression (bulk and single-cell RNA-seq), chromatin binding/modification (ChIP-seq and DNA methylation) and protein abundances (LC-MS/MS). In Aim 3, we will provide continued support for large-scale peptide sequence alignment and support novel pipelines to integrate genomic, transcriptomic, and proteomic datasets; work closely with the bioinformatics and biostatistics teams to help benchmark, scale, optimize, and speed up computing tasks involving large- scale data analyses and database queries; and explore alternatives to traditional high performance computing environments such as container systems and private cloud computing. In Aim 4, we will provide training and education in biostatistics, bioinformatics, database and high performance computing through interaction and collaboration with the Center investigators, including working closely with the Yale Medical Library Bioinformatics Support Program and other Yale organizations.