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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...

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.

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Related Experiment Video

Updated: Nov 27, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing

Sandeep Kasaragod¹, Varshasnata Mohanty¹, Ankur Tyagi¹

¹Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India.

|December 7, 2020

Summary

This summary is machine-generated.

Identifying cancer-driving coding variants requires integrating genomic and proteomic data. CusVarDB is a new tool that analyzes next-generation sequencing data to find expressed variant peptides, aiding cancer research and immunotherapy.

Keywords:

NGS-pipeline Next-generation sequencing Variant protein database

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Last Updated: Nov 27, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Area of Science:

Genomics and Proteomics
Cancer Biology
Immunotherapy

Background:

Cancer genome sequencing identifies coding variants crucial for proliferation and immune activation via neo-antigens.
Predicting protein-level expression of coding variants from genomic data alone is challenging.
Integrating proteomic data is essential for identifying expressed variant peptides, but current tools are lacking.

Purpose of the Study:

To develop a user-friendly tool that integrates genomic and proteomic data analysis for identifying expressed coding variants.
To overcome the limitations of existing bioinformatics pipelines for variant peptide identification.

Main Methods:

Development of CusVarDB, a graphical user interface (GUI)-based tool.
Integration of a variant calling pipeline to generate sample-specific variant protein databases from next-generation sequencing (NGS) data.
Validation using triple-negative breast cancer cell line datasets.

Main Results:

CusVarDB successfully generated variant protein databases from NGS datasets.
The tool identified a significant number of variant peptides in triple-negative breast cancer cell lines: 423 (BT474), 408 (MDMAB157), 386 (MFM223), and 361 (HCC38).

Conclusions:

CusVarDB provides a valuable solution for integrating genomic and proteomic data to identify expressed variant peptides.
This tool can facilitate cancer research by enabling the discovery of potential neo-antigens for immunotherapy.