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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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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...

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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: Dec 10, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

Assessing the Impact of Data Preprocessing on Analyzing Next Generation Sequencing Data.

Binsheng He¹, Rongrong Zhu², Huandong Yang³

¹Academician Workstation, Changsha Medical University, Changsha, China.

Frontiers in Bioengineering and Biotechnology

|August 28, 2020

Summary

Data preprocessing for next-generation sequencing (NGS) impacts tumor mutation detection and human leukocyte antigen (HLA) typing accuracy. Optimizing these steps is crucial for reliable downstream analysis.

Keywords:

HLA typing cancer data preprocessing mutation the next generation sequencing

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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:

Bioinformatics
Genomics
Cancer Research

Background:

Data quality control and preprocessing are critical initial steps in analyzing next-generation sequencing (NGS) data.
Ensuring high-quality data is essential for accurate downstream bioinformatics analysis, particularly in tumor sequencing.
Various preprocessing tools exist, but their impact on specific analyses like mutation detection and HLA typing requires careful evaluation.

Purpose of the Study:

To investigate the influence of different data preprocessing methods on the outcomes of downstream analyses in tumor sequencing.
To compare the effects of common preprocessing tools (Cutadapt, FastP, Trimmomatic) against raw sequencing data.
To highlight the importance of selecting appropriate preprocessing strategies for accurate mutation detection and human leukocyte antigen (HLA) typing.

Main Methods:

Comparison of data analysis results from raw sequencing data.
Evaluation of preprocessing using Cutadapt, FastP, and Trimmomatic.
Assessment of impacts on mutation detection frequency and human leukocyte antigen (HLA) typing accuracy.

Main Results:

Preprocessing steps led to fluctuations and differences in mutation detection frequencies.
Directly using preprocessed data for human leukocyte antigen (HLA) typing resulted in erroneous outcomes.
The choice of preprocessing tool significantly affects the reliability of downstream analysis results.

Conclusions:

Data preprocessing significantly impacts the accuracy of downstream analyses in tumor NGS data.
Current preprocessing methods may introduce biases affecting mutation detection and HLA typing.
Further development and optimization of data preprocessing methods are needed to enhance the precision of bioinformatics analyses.