Applied Bioinformatics Laboratory

New York University School Of Medicine, New York NY

Investigators

Linked publications & trials

Abstract

The overarching goal of the Applied Bioinformatics Laboratories Shared Resource (ABL) is to provide robust, reproducible, timely, and cost-efficient analysis of biomedical data, including, but not limited to, multi-omics, imaging, and clinical data. ABL provides start-to-finish standardization of the analysis of sequencing datasets, rigorous data quality assessment, integration, and visualization using a variety of established state-of-the-art computational workflows, and statistical expertise in collaboration with the Biostatistics Shared Resource. ABL members also establish novel computational pipelines to ensure that new types of data can be analyzed using cutting-edge techniques. Data generated by ABL are shared with PCC members via a customized web interface and are accessible via the High-Performance Computing (HPC) cluster. ABL is essential to the research of the 4 Research Programs, as data analysis is central to most modern cancer research. During the funding period, we significantly increased our clientele, co-authored 71 peer-reviewed manuscripts (21 with impact factor >20) and were key contributors to 43 grants with PCC members in cancer genetics, epigenetics, and machine learning. These manuscripts represent approximately half of all PCC publications that used genomics data generated internally. ABL is led by Aristotelis Tsirigos, Ph.D., Professor of Pathology and Medicine. He has >18 years of experience in genomics and machine learning and has co-authored >130 studies in peer-reviewed journals, including high-impact studies in cancer genomics and epigenomics, high-throughput single-cell transcriptomic analyses, and cancer diagnostics using machine learning. ABL, like other PCC Shared Resources, is overseen by the Division for Advanced Research Technologies (DART), has an Advisory Board composed of PCC members and other NYU faculty, and is subject to yearly surveys to ensure its alignment with the present and future needs of PCC members. Since its founding in 2015, ABL has grown from a small team of 2 bioinformaticians to a vibrant group of 22 experienced data scientists (BS, MS, PhD, and faculty level) that provides essential services to PCC investigators at all stages of the research cycle (see Figure). Services include analysis of standard genomic, epigenomic, and transcriptomic datasets __ bulk and single cell __ spatial technologies including multi-channel imaging, and machine learning. Cost recovery is through chargebacks and salary support, which helps offset the costs of operations. A key part of ABL service is education, which is achieved through workshops, seminars, and internships.