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

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

You might also read

Related Articles

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

Sort by

Same author

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methods·2026

Same author

A complete human pancreatic cancer genome.

bioRxiv : the preprint server for biology·2026

Same author

Accelerated long-read variant calling with Clair3 for whole-genome sequencing.

Bioinformatics (Oxford, England)·2026

Same author

Gungnir codec enabling high error-tolerance and low-redundancy DNA storage through substantial computing power.

Nature communications·2026

Same author

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

Nature communications·2026

Same author

DECODE: deep learning-based common deconvolution framework for various omics data.

Nature methods·2026

Same journal

Topological skeleton analysis for network-based shape representation in biology and beyond.

iScience·2026

Same journal

Condition-specific neural signatures of reactivation during post-retrieval rest: An EEG study.

iScience·2026

Same journal

Multi-chaotic signal identification employing a causal cross-correlation neural network.

iScience·2026

Same journal

Repeated insertions at positions 261-280 in KPC-2 highlight a ceftazidime-avibactam resistance hotspot.

iScience·2026

Same journal

ROS inhibits microtubule dynamics and cell growth heterogeneity during Arabidopsis sepal morphogenesis.

iScience·2026

Same journal

Type 1 diabetes alters early macrophage-<i>Mycobacterium tuberculosis</i> transcriptional coordination during infection.

iScience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 21, 2025

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Published on: March 11, 2020

CONNET: Accurate Genome Consensus in Assembling Nanopore Sequencing Data via Deep Learning.

Yifan Zhang¹, Chi-Man Liu¹, Henry C M Leung¹

¹Department of Computer Science, The University of Hong Kong, Hong Kong, China.

|May 19, 2020

Summary

This summary is machine-generated.

CONNET, a new deep learning tool, improves genome assembly accuracy and speed by analyzing long-read sequencing data. It enhances consensus accuracy and enables phased diploid genome assembly, reducing errors in genomic data.

Keywords:

Bioinformatics Genomics Sequence Analysis

More Related Videos

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Related Experiment Videos

Last Updated: Dec 21, 2025

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Published on: March 11, 2020

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-molecule sequencing offers longer reads for improved genome assembly contiguity.
High error rates in long reads challenge the generation of high-quality genome assemblies.
Existing consensus tools, while efficient, can be computationally intensive.

Purpose of the Study:

To develop a novel deep learning-based consensus tool for improving genome assembly quality.
To investigate the significance of spatial relationships in alignment pileups for high-quality consensus.
To enable phased diploid genome consensus in addition to haploid consensus.

Main Methods:

Developed CONNET, a deep learning model for genome sequence consensus.
Utilized partial-order alignment principles and analyzed spatial relationships in alignment pileups.
Tested CONNET on E. coli (90×) and human (37×) datasets for performance evaluation.

Main Results:

CONNET demonstrated superior accuracy and speed compared to existing consensus tools.
Achieved high-quality consensus results on both bacterial and human genome datasets.
Successfully generated phased diploid genome consensus, reducing haploid consensus errors by 12% in human data.

Conclusions:

CONNET represents a significant advancement in genome assembly consensus tools.
The deep learning approach effectively addresses challenges posed by long-read sequencing errors.
Phased diploid consensus capability further enhances the utility of CONNET for complex genomes.