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.

You might also read

Related Articles

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

Sort by

Same author

Scavenger-controlled activation of lignin in microwave-assisted deep eutectic solvents for antioxidant nanoparticles.

Bioresource technology·2026

Same author

pH Driving the Self-Assembly of Hydrolyzed Edible Dock Protein and Myricetin.

Foods (Basel, Switzerland)·2026

Same author

Exosomes derived from M2-polarized tumor-associated macrophages contribute to immune escape in cervical cancer.

Biochemical and biophysical research communications·2026

Same author

Growth of a legend: a decadal comparison at Lost City hydrothermal field.

Science bulletin·2026

Same author

Synergistic gellan gum/flaxseed gum composite enhances freeze-thaw stability of soy isolate protein emulsion gels.

Food chemistry·2026

Same author

Lattice Oxygen-Mediated Defect and Strain Regulation of SnO<sub>2</sub> via Water-Soluble Tb<sub>2</sub>O<sub>3</sub> for High-Performance Perovskite Solar Cells.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same journal

Model-based quantification of protein-protein interaction aberrations for exploring dysregulated signalling pathways through pathway maps and gene expression levels.

BMC bioinformatics·2026

Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026

Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 22, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

trieFinder: an efficient program for annotating Digital Gene Expression (DGE) tags.

Gabriel Renaud, Matthew C LaFave, Jin Liang

¹Translational and Functional Genomics Branch, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892-8004, USA. burgess@mail.nih.gov.

BMC Bioinformatics

|October 15, 2014

Summary

This summary is machine-generated.

trieFinder is a new bioinformatics tool that efficiently maps and annotates Digital Gene Expression (DGE) tags. This cost-effective method simplifies transcript quantification and novel transcript detection.

More Related Videos

A Nonsequencing Approach for the Rapid Detection of RNA Editing

A Nonsequencing Approach for the Rapid Detection of RNA Editing

Published on: April 21, 2022

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Related Experiment Videos

Last Updated: Apr 22, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

A Nonsequencing Approach for the Rapid Detection of RNA Editing

A Nonsequencing Approach for the Rapid Detection of RNA Editing

Published on: April 21, 2022

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Transcriptional profiling assesses cellular activity.
RNA sequencing (RNA-Seq) is costly and generates excessive data for basic transcript quantification.
Digital Gene Expression (DGE) offers a cost-effective alternative for transcript counting.

Purpose of the Study:

Introduce trieFinder, a program for rapid mapping, parsing, and annotation of DGE tags.
Provide a cost-effective solution for transcript quantification using DGE data.
Facilitate novel transcript detection.

Main Methods:

Developed the trieFinder algorithm for DGE tag mapping.
Utilized a two-step process involving database creation and tag comparison.
Implemented a prefix tree (trie) data structure for efficient, linear-time searches.
Handled DGE tags with mismatches using recursive calls.

Main Results:

trieFinder maps DGE tags against cDNA and genomic sequence databases.
The algorithm demonstrates favorable alignment speed compared to Bowtie.
Successfully created a tag database from RefSeq, UniGene, and genomic DNA sequences.

Conclusions:

trieFinder provides rapid, simultaneous annotation of DGE tags from multiple sources.
Simplifies transcript quantification and novel transcript detection.
Delivers parsed data, eliminating post-processing of alignment results.