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Updated: Aug 24, 2026

Novel Sequence Discovery by Subtractive Genomics
Published on: January 25, 2019
seqme: a Python library for evaluating biological sequence design from generative models
Rasmus Møller-Larsen1,2, Adam Izdebski1,2, Jan Olszewski3
1Institute of AI for Health, Helmholtz Munich, Neuherberg 85764, Germany.
Summary:
Recent advances in computational methods for designing biological sequences have sparked the development of metrics to evaluate these methods performance in terms of the fidelity of the designed sequences to a target distribution and their attainment of desired properties. However, a software library implementing these metrics was lacking. In this work we introduce seqme, a modular and highly extendable open-source Python library, containing model-agnostic metrics for evaluating computational methods for biological sequence design. seqme considers three groups of metrics: sequence-based, embedding-based, and property-based, and is applicable to a wide range of biological sequences: small molecules, DNA, ncRNA, mRNA, peptides and proteins. The library offers a number of embedding and property models for biological sequences, as well as diagnostics and visualization functions to inspect the results. seqme can be used to evaluate both one-shot generation and iterative optimization. We show the utility of seqme by performing an antimicrobial peptide benchmark and acquiring mRNA data.
Availability And Implementation:
seqme is released at https://github.com/szczurek-lab/seqme under the BSD 3-Clause license.
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