SIMBSIG: similarity search and clustering for biobank-scale data

In many modern bioinformatics applications, such as statistical genetics, or single-cell analysis, one frequently encounters datasets which are orders of magnitude too large for conventional in-memory analysis. To tackle this challenge, we introduce SIMBSIG (SIMmilarity Batched Search Integrated GPU...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2023-01, Vol.39 (1)
Main Authors: Adamer, Michael F, Roellin, Eljas, Bourguignon, Lucie, Borgwardt, Karsten
Format: Article
Language:English
Subjects:
Biological Specimen Banks
Cluster Analysis
Computational Biology
Documentation
Software
Online Access:Get full text
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Summary:In many modern bioinformatics applications, such as statistical genetics, or single-cell analysis, one frequently encounters datasets which are orders of magnitude too large for conventional in-memory analysis. To tackle this challenge, we introduce SIMBSIG (SIMmilarity Batched Search Integrated GPU), a highly scalable Python package which provides a scikit-learn-like interface for out-of-core, GPU-enabled similarity searches, principal component analysis and clustering. Due to the PyTorch backend, it is highly modular and particularly tailored to many data types with a particular focus on biobank data analysis. SIMBSIG is freely available from PyPI and its source code and documentation can be found on GitHub (https://github.com/BorgwardtLab/simbsig) under a BSD-3 license.
ISSN:1367-4811