The trackRank technology is a ranking algorithm suite that can identify and rank genomics datasets relative to their significance to a given query gene or genomic location.
- Dynamic genome browser that will search among thousands of tracks in its database and automatically identify the most relevant.
- This method will rank all datasets by their characteristics at the query gene or region.
- Weighted search results ensure that the most important results are placed on top.
Advances in high-throughput technologies have resulted in the growth of massive genomics datasets. The global market for bioinformatics is expected to grow from $16.1 billion in 2020 to $24.1 billion by 2025 with a CAGR of 8.4% from 2020-to 2025 (BCC Research BIO051F). Currently, there is no effective data mining tool available to analyze and visualize these large data resources. There is a need for a tool that can properly analyze massive genomic data sets and have the power to rank the given data by their characteristics at the query gene or region.
Advancements in high-throughput technologies have resulted in the augmented growth of massive genomics data. Due to its large number, it is effectively impossible to analyze and visualize datasets individually, and currently, no effective data mining tool exists. This project proposes a new method based on analyzing a broad collection of genome-wide profiling datasets. The proposed method will rank all the datasets by their characteristics at the query gene or region when given a query of related genomic features. The method proposes the implementation of the ranking algorithm suit technology trackRank to rank and identify genomic datasets as they relate to their significance to a given genomic location or query gene. This is to be achieved by converting every data type and locus into a “signal measure” that will be comparable across a large variety of data types. Additionally using the signal researchers will develop appropriate ranking algorithms that can be utilized to a diverse collection of genome-wide profiling datasets. Said algorithms are intended to be implemented into the software mining tool OmiSeqs. The long-term priority of this project is to develop informatics, computational apparatus, and statistics that can be used to further the knowledge of and generate testable hypotheses from existing genomics data.
Early-stage. Website publicly available since 5/15/2013.
TTO Home Page: https://emoryott.technologypublisher.com
Name: Hyeon (Sean) Kim
Title: Licensing Associate