SIMBSIG: similarity search and clustering for biobank-scale data

Michael F Adamer; Eljas Roellin; Lucie Bourguignon; Karsten Borgwardt

doi:10.1093/bioinformatics/btac829

SIMBSIG: similarity search and clustering for biobank-scale data

Bioinformatics. 2023 Jan 1;39(1):btac829. doi: 10.1093/bioinformatics/btac829.

Authors

Michael F Adamer^{1

2}, Eljas Roellin¹, Lucie Bourguignon³, Karsten Borgwardt^{1

2}

Affiliations

¹ Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
² Swiss Institute for Bioinformatics (SIB), 1015 Lausanne, Switzerland.
³ Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland.

Abstract

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.

Availability and implementation: 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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biological Specimen Banks*
Cluster Analysis
Computational Biology
Documentation
Software*

Grants and funding

PZ00P3_186101/SNSF_/Swiss National Science Foundation/Switzerland