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

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...

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.

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

You might also read

Related Articles

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

Sort by

Same author

Treatment Effect Reanalysis of the Randomized Individual Screening Trial of Innovative Glioblastoma Therapy in Newly Diagnosed Glioblastoma With External Control Data.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026

Same author

Immune Spatial Organization Predicts Distant Metastasis Risk in Aggressive Localized Prostate Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Same author

Classifying germline and somatic structural variants in tumor-only contexts using GaTSV.

STAR protocols·2026

Same author

MRE11 proximal polyadenylation site-mediated looping impacts transcription and genomic stability.

Molecular cell·2026

Same author

Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics.

bioRxiv : the preprint server for biology·2026

Same author

A Cre-mediated copy number variant compromises the reliability of a <i>LoxP-STOP-LoxP</i>-<i>PLAG1</i> driven brain tumor model.

Neuro-oncology advances·2026

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 9, 2026

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

SeqLib: a C ++ API for rapid BAM manipulation, sequence alignment and sequence assembly.

Jeremiah Wala^1,2,3, Rameen Beroukhim^1,2,3

¹The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.

Bioinformatics (Oxford, England)

|December 25, 2016

Summary

This summary is machine-generated.

SeqLib offers a C++ API and command-line tool for efficient sequence analysis, including alignment and assembly. It demonstrates lower resource usage compared to other tools, aiding in genomic data processing.

More Related Videos

3' End Sequencing Library Preparation with A-seq2

3' End Sequencing Library Preparation with A-seq2

Published on: October 10, 2017

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

Published on: February 23, 2021

Related Experiment Videos

Last Updated: Mar 9, 2026

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

3' End Sequencing Library Preparation with A-seq2

3' End Sequencing Library Preparation with A-seq2

Published on: October 10, 2017

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

Published on: February 23, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput sequencing generates vast amounts of data requiring efficient analysis tools.
Existing C++ sequence analysis libraries can be resource-intensive.
Integrated solutions for sequence alignment, assembly, and data manipulation are needed.

Purpose of the Study:

To introduce SeqLib, a C++ API and command-line tool for rapid and user-friendly sequence analysis.
To provide an efficient interface for BAM/SAM/CRAM file manipulation, sequence alignment, and assembly.
To benchmark SeqLib against existing tools for CPU and memory requirements.

Main Methods:

SeqLib integrates C libraries: HTSlib for BAM access, BWA-MEM and BLAT for alignment, and Fermi for error correction and assembly.
Benchmarking was performed to compare SeqLib's performance against leading C++ sequence analysis APIs.
Demonstration of minimal SeqLib code for read extraction, error correction, assembly, and alignment from CRAM files.

Main Results:

SeqLib exhibits lower CPU and memory requirements compared to leading C++ sequence analysis APIs.
Minimal SeqLib code effectively handles extraction, error correction, assembly, and alignment of sequencing reads.
SeqLib provides additional features like chromosome-aware interval queries and read plotting.

Conclusions:

SeqLib offers an efficient and user-friendly solution for diverse sequence analysis tasks.
Its reduced resource requirements make it a valuable tool for genomic data processing.
SeqLib supports integrated workflows for error correction, micro-assembly, and alignment.