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Updated: Sep 20, 2025

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

Accelerating in silico saturation mutagenesis using compressed sensing.

Jacob Schreiber¹, Surag Nair², Akshay Balsubramani²

¹Department of Genetics, Stanford University, Stanford, CA 94305, USA.

Bioinformatics (Oxford, England)

|June 9, 2022

Summary

This summary is machine-generated.

We developed Yuzu, a faster method for in silico saturation mutagenesis (ISM) in computational genomics. Yuzu uses compressed sensing to significantly speed up feature attribution analysis on biological sequences.

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Area of Science:

Computational Genomics
Bioinformatics
Machine Learning

Background:

In silico saturation mutagenesis (ISM) is a key computational genomics technique for understanding biological sequences.
Traditional ISM is computationally intensive due to its reliance on numerous model forward passes, limiting its scalability.

Purpose of the Study:

To accelerate the in silico saturation mutagenesis (ISM) method for computational genomics.
To reduce the computational cost of feature attribution analysis on biological sequences.

Main Methods:

We introduce Yuzu, a novel modification of ISM that employs compressed sensing principles.
Yuzu requires only a constant number of forward passes, independent of sequence length, for models with limited receptive fields like convolutions.

Main Results:

Yuzu significantly reduces computation time in convolutional operations, by several orders of magnitude.
The Yuzu method accelerates ISM on common genomic architectures by over an order of magnitude.
Speedups increase with convolutional complexity and sequence length, highlighting Yuzu's effectiveness in practical applications.

Conclusions:

Yuzu offers a substantial computational speedup for in silico saturation mutagenesis.
This advancement makes feature attribution analysis on large biological sequences more efficient and accessible.
The Yuzu tool is publicly available for use in computational genomics research.