Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A complete diploid human genome benchmark for personalized genomics.

bioRxiv : the preprint server for biology·2025

Same author

Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain.

Genome research·2025

Same author

Genome-wide association analyses identify single nucleotide polymorphisms associated with in vitro embryo cleavage and blastocyst rates in Holstein bulls.

Journal of dairy science·2025

Same author

Biomarker-based high-throughput sperm phenotyping: Andrology in the age of precision medicine and agriculture.

Animal reproduction science·2024

Same author

The Ruminant Telomere-to-Telomere (RT2T) Consortium.

Nature genetics·2024

Same author

Uncovering the architecture of selection in two <i>Bos taurus</i> cattle breeds.

Evolutionary applications·2024

Search research articles

Related Experiment Video

Updated: Jun 28, 2025

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Published on: October 13, 2019

Overcoming Limitations to Deep Learning in Domesticated Animals with TrioTrain.

Jenna Kalleberg¹, Jacob Rissman¹, Robert D Schnabel^1,2

¹University of Missouri, Division of Animal Sciences, Columbia, MO, 65201 USA.

Biorxiv : the Preprint Server for Biology

|April 25, 2024

Summary

Developing a multi-species DeepVariant (DV) model improves variant calling accuracy in non-human genomes. This new approach, TrioTrain, reduces Mendelian Inheritance Error (MIE) rates, overcoming limitations of human-genome-trained models for broader genomic applications.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

Related Experiment Videos

Last Updated: Jun 28, 2025

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Published on: October 13, 2019

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

Area of Science:

Genomics and Bioinformatics
Comparative Genomics
Machine Learning in Biology

Background:

Variant calling tools often assume human genome parameters, limiting their effectiveness in diverse species.
DeepVariant (DV) shows promise for variant calling but its performance on non-human genomes is not fully understood.
The development of universal algorithms necessitates evaluating their impact across different species.

Purpose of the Study:

To assess the limitations of human-genome-trained DeepVariant models when applied to non-human genomes, specifically bovine.
To introduce TrioTrain, a novel multi-species DeepVariant model for improved variant calling in diverse species.
To automate the extension of DV for species lacking Genome In A Bottle (GIAB) resources and mitigate cluster-based barriers.

Main Methods:

Developed TrioTrain, a multi-species DeepVariant training approach using trio data from cattle, yak, and bison.
Implemented region shuffling for SLURM-based clusters and removed Mendelian discordant variants to offset imperfect truth labels.
Trained 30 model iterations across five phases, evaluating performance on human trios (HG002) and bovine F1-hybrid genomes.

Main Results:

The multi-species DV model achieved a mean SNP F1 score >0.990 on GIAB human trios.
The phase 4 bovine model identified more variants with a lower Mendelian Inheritance Error (MIE) rate than DeepTrio in HG002.
Substantial reduction in inheritance errors for bovine F1-hybrid genomes, with a mean MIE rate of 0.03 percent.

Conclusions:

Multi-species, trio-based training produces a robust variant calling model, demonstrating the limitations of exclusively human-trained models.
TrioTrain effectively extends DeepVariant for species without GIAB resources, enhancing its applicability in comparative genomics.
The developed model significantly improves variant calling accuracy and reduces inheritance errors in non-human genomes.