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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning.

Irene M Kaplow^1,2, Alyssa J Lawler^2,3, Daniel E Schäffer¹

¹Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.

Science (New York, N.Y.)

|April 27, 2023

Summary

This summary is machine-generated.

Scientists developed a new tool, TACIT, to link gene enhancers to species phenotypes. This helps understand how gene regulation drives evolutionary differences, like brain size variations.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

Genomics
Evolutionary Biology
Bioinformatics

Background:

Phenotypic diversity between species is not solely explained by protein-coding genes.
Gene regulatory elements, like enhancers, play a crucial role in gene expression and phenotypic evolution.
Identifying enhancer-phenotype associations is difficult due to tissue-specific activity and varying sequence conservation.

Purpose of the Study:

To develop a computational toolkit for associating candidate enhancers with species-specific phenotypes.
To leverage tissue-specific machine learning models for predicting enhancer function.
To investigate the role of enhancers in neurological phenotypes and brain size evolution.

Main Methods:

Development of the Tissue-Aware Conservation Inference Toolkit (TACIT).
Training machine learning models on tissue-specific data to predict enhancer activity.
Applying TACIT to associate motor cortex and parvalbvalbumin-positive interneuron enhancers with neurological phenotypes in species.

Main Results:

Identification of dozens of significant enhancer-phenotype associations.
Discovery of brain size-associated enhancers linked to genes involved in microcephaly and macrocephaly.
Demonstration of TACIT's capability in uncovering regulatory elements driving convergent evolution.

Conclusions:

TACIT provides a robust framework for linking enhancers to phenotypes across species.
The toolkit facilitates the study of gene regulation in the evolution of complex traits.
This approach can be applied to diverse phenotypes and species with aligned genomes.