Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.02.14.528550v1?rss=1
Authors: Harwood, T. V., Treen, D., Wang, M., de Jong, W., Northen, T., Bowen, B. P.
Abstract:
Summary Metabolomics has a long history of using cosine similarity to match experimental tandem mass spectra to databases for compound identification. Here we introduce the Blur-and-Link (BLINK) approach for scoring cosine similarity. BLINK calculates substantially equivalent cosine similarity scores ( greater than 99% identification agreement) over 1000 times faster than commonly used loop-based implementations by bypassing fragment alignment and simultaneously scoring all pairs of spectra using sparse matrix operations. This performance improvement can enable calculations to be performed that would typically be limited by time and available computational resources. Availability and Implementation BLINK is implemented in Python3 and is published under a modified open source license. Code and license are available on Github: https://github.com/biorack/blink
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