# Data Discovery: Computational Methods for Searching Short-Read Sequencing Experiments - Administrative Supplement > **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2021 · $8,236 ## Abstract PROJECT SUMMARY / ABSTRACT This proposal aims to solve the sequencing experiment discovery problem. The data from hundreds of thou- sands of short-read sequencing experiments are now publicly available, and private collections of sequencing experiments are also growing rapidly. These experiments include hundreds of thousands of whole genome sequencing experiments, and tens of thousands of RNA-seq, metagenomic, and tumor sequencing samples. However, these experiments are vastly underused, with few analyses making use of more than a handful of ex- periments at a time and most analyses ignoring this collection of raw data entirely. One crucial reason for this is that merely finding the appropriate experiments is a significant barrier to their use in downstream analyses. This is due to the lack of a computational platform that can search for relevant short-read sequencing data sets by the sequences they contain. It is not currently possible to find all the metagenomic experiments in which the genes that form a particular pathway are present or to find all experiments in which a novel lncRNA is observed. The experiment discovery problem is that of finding — on a global scale — those experiments that are relevant to an isoform, variant, or species under study. By building on our existing work in large-scale sequence search, we propose to develop a new distributed platform to index and search hundreds of thousands of raw short-read se- quencing data sets to enable researchers to quickly find experiments that contain their query sequences. We will apply this system to searching RNA-seq, metagenomic, and cancer tumor samples. The research questions we will solve include how to improve the computational scaling, increase the types of biologically meaningful queries that can be answered, and increase our ability to find relevant experiments in situations where muta- tions are common. We will produce a high-quality open-source implementation of the developed computational methods. The project will significantly expand the usefulness of large repositories of raw sequencing reads and enabled new approaches for large-scale reanalysis and reuse of short-read experiments. The system will unlock a rich source of biological information for gene function prediction, for understanding microbial communities, and for connecting genetic variation with disease progression. ## Key facts - **NIH application ID:** 10393953 - **Project number:** 3R01GM122935-04S1 - **Recipient organization:** CARNEGIE-MELLON UNIVERSITY - **Principal Investigator:** Carleton Lee Kingsford - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $8,236 - **Award type:** 3 - **Project period:** 2017-05-01 → 2022-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10393953 ## Citation > US National Institutes of Health, RePORTER application 10393953, Data Discovery: Computational Methods for Searching Short-Read Sequencing Experiments - Administrative Supplement (3R01GM122935-04S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10393953. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*