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

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Multi-species Conserved Sequences

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...

You might also read

Related Articles

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

Sort by

Same author

Pre-training genomic language model with variants for better modeling functional genomics.

NPJ artificial intelligence·2026

Same author

EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.

Nature communications·2026

Same author

Spatial GWAS Atlas: a knowledgebase for decoding the genetic architecture of complex traits in spatial resolution.

Nucleic acids research·2025

Same author

Pre-training Genomic Language Model with Variants for Better Modeling Functional Genomics.

bioRxiv : the preprint server for biology·2025

Same author

Protein-truncating variants in UQCRC1 are associated with Parkinson's disease: evidence from half-million people.

NPJ Parkinson's disease·2025

Same author

Unveiling Multi-Scale Architectural Features in Single-Cell Hi-C Data Using scCAFE.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 12, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Deep Neural Networks for Epistatic Sequence Analysis.

¹City University of Hong Kong, Kowloon Tong, Hong Kong. jieconlin3-c@my.cityu.edu.hk.

Methods in Molecular Biology (Clifton, N.J.)

|March 18, 2021

Summary

This summary is machine-generated.

This study introduces pysster, a TensorFlow package for deep neural network development on sequence data like DNA and RNA. It simplifies building, training, and interpreting models, aiding in understanding complex biological sequences.

Keywords:

Deep learning Epistatic sequence analysis Model Interpretation RNA A-to-I editing

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Related Experiment Videos

Last Updated: Nov 12, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Deep neural networks (DNNs) are powerful tools for analyzing biological sequence data.
Interpreting DNN predictions on complex sequences remains a challenge.

Purpose of the Study:

To present a step-by-step protocol for using pysster, a TensorFlow-based package.
To demonstrate the development, training, and evaluation of DNNs for sequence data.
To showcase model interpretability features for uncovering DNN predictions.

Main Methods:

Utilized pysster to build and train deep neural networks on DNA, RNA, and secondary structure sequences.
Applied pysster to classify RNA A-to-I editing regions.
Visualized model predictions to interpret DNN behavior.
Evaluated pysster on an artificial sequence dataset to demonstrate generalizability.

Main Results:

Successfully developed a protocol for using pysster for DNN construction on various sequence types.
Demonstrated effective classification of RNA A-to-I editing regions.
Showcased the utility of visualization tools for interpreting DNN predictions.
Confirmed the generalizability of pysster across different sequence datasets.

Conclusions:

Pysster offers a comprehensive and user-friendly platform for deep learning on biological sequences.
The package facilitates model development, training, evaluation, and interpretation.
Pysster is a valuable tool for researchers in bioinformatics and computational biology seeking to leverage DNNs.