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.

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.

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 microarray-based...

Sanger Sequencing

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...

RACE - Rapid Amplification of cDNA Ends

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since the...

You might also read

Related Articles

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

Sort by

Same author

The genome sequence of the Purple Treble-bar, <i>Aplocera praeformata</i> (Hübner, 1826) (Lepidoptera: Geometridae).

Wellcome open research·2026

Same author

The genome sequence of the Spotted Sulphur, <i>Emmelia trabealis</i> (Scopoli, 1763) (Lepidoptera: Noctuidae).

Wellcome open research·2026

Same author

The genome sequence of the Clouded Buff, <i>Diacrisia sannio</i> (Linnaeus, 1758) (Lepidoptera: Erebidae).

Wellcome open research·2026

Same author

The genome sequence of <i>Heligmosomum mixtum</i> Schulz, 1929 (Rhabditida: Heligmosomidae).

Wellcome open research·2026

Same author

The genome sequence of the Southern White Admiral, <i>Limenitis reducta</i> (Staudinger, 1901) (Lepidoptera: Nymphalidae).

Wellcome open research·2026

Same author

The genome sequence of the Woodland Grayling, <i>Hipparchia fagi</i> (Scopoli, 1763) (Lepidoptera: Nymphalidae).

Wellcome open research·2026

Same journal

pGWAS-Portal: a comprehensive online platform for integrative post-genome-wide association study analysis.

BMC genomics·2026

Same journal

Physiological and transcriptomic analyses of Rosa persica in response to drought stress and functional validation of the transcription factor RpERF113-like.

BMC genomics·2026

Same journal

Integrated analysis of chromatin accessibility and transcriptome profiles in granulosa cells of sheep with different FecB genotypes.

BMC genomics·2026

Same journal

Correction: TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.

BMC genomics·2026

Same journal

Chromosomal scale genome assembly of medicinal plant Sophora tonkinensis.

BMC genomics·2026

Same journal

Variant-specific RNA testing resolves variants of uncertain significance in exome testing.

BMC genomics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Comparing de novo assemblers for 454 transcriptome data.

Sujai Kumar¹, Mark L Blaxter

¹Institute of Evolutionary Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK.

|October 19, 2010

Summary

This summary is machine-generated.

Comparing transcriptome assemblers, Newbler 2.5 yielded longer contigs, while merging assemblies improved alignment and consistency. Combining multiple assembler outputs is recommended for robust transcriptome analysis.

More Related Videos

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Related Experiment Videos

Last Updated: Jun 8, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Bioinformatics
Genomics
Transcriptomics

Background:

Roche 454 pyrosequencing is crucial for non-model organism transcriptome data.
Accurate assembly of short reads is vital for transcript sequence estimation.
Current transcriptome assembly practices may not use optimal software.

Purpose of the Study:

Systematically compare five assemblers for 454 pyrosequencing reads.
Establish best practices for transcriptome assembly.
Evaluate assembler performance using a parasitic nematode dataset.

Main Methods:

Comparative analysis of five assemblers: CAP3, MIRA, Newbler, SeqMan, and CLC.
Assessment of contig length, alignment to reference sequences, and novel sequence generation.
Exploration of merging strategies for combined assembler outputs.

Main Results:

Newbler 2.5 produced longer contigs and better alignments but SeqMan better recapitulated known transcripts.
SeqMan generated more novel sequences but also redundant contigs.
Merging assemblies enhanced alignment to references and improved contig consistency.

Conclusions:

No single assembler was optimal across all criteria.
Newbler 2.5 showed strong performance, but other assemblers were comparable.
Combining assemblies from multiple programs offers a more credible and robust transcriptome assembly.