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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.

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Related Experiment Video

Updated: May 27, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Calling amplified haplotypes in next generation tumor sequence data.

Ninad Dewal¹, Yang Hu, Matthew L Freedman

¹Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA.

Genome Research

|November 18, 2011

Summary

This summary is machine-generated.

We developed Haplotype Amplification in Tumor Sequences (HATS), a novel method to identify preferred alleles during tumor evolution. HATS accurately infers amplified alleles, improving cancer genomics insights.

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Last Updated: May 27, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Area of Science:

Genomics
Cancer Biology
Bioinformatics

Background:

Cancer cells gain selective advantages through genetic alterations during tumor progression.
Understanding these genetic changes, including copy number alterations and germline variants, is crucial for deciphering tumorigenesis.
Somatic amplifications are key drivers of tumor evolution, and identifying preferred alleles offers insights into cancer biology.

Purpose of the Study:

To develop a novel method for inferring amplified alleles and haplotypes in cancer.
To improve the accuracy of identifying selected alleles within tumor amplicons, especially in challenging data scenarios.
To provide deeper insights into the genetic basis of tumor-acquired traits and cancer evolution.

Main Methods:

Developed Haplotype Amplification in Tumor Sequences (HATS), a hidden Markov model-based method.
Analyzed tumor and matched normal next-generation sequencing data, incorporating phasing training data.
Evaluated HATS performance using simulated and real cancer amplicon data with varying copy numbers and coverage.

Main Results:

HATS accurately infers amplified alleles and haplotypes in regions of copy number gain.
The method demonstrates superior performance compared to naive approaches, particularly at low to intermediate sequencing coverage.
HATS effectively handles challenges such as stromal contamination and allelic bias in sequencing data.

Conclusions:

HATS provides a more accurate approach for identifying selected alleles in tumor amplicons.
This method enhances the analysis of genetic alterations driving tumor evolution.
Accurate inference of amplified alleles using HATS can significantly advance cancer genomics research.