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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...

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Updated: Jun 13, 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

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline.

Yash Patel^1,2,3, Chenghao Zhu^1,2, Takafumi N Yamaguchi^1,2,3

¹Department of Human Genetics, University of California, Los Angeles, USA.

Biorxiv : the Preprint Server for Biology

|September 16, 2024

Summary

This summary is machine-generated.

Metapipeline-DNA is a new Nextflow pipeline that automates comprehensive DNA sequencing data analysis. It processes raw reads to feature detection and visualization, enhancing genomic research scalability.

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Published on: May 19, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

DNA sequencing technology continues to advance in price, quality, and throughput.
Algorithmic innovations enable the inference of diverse genomic features from sequencing data.
Analysis of nuclear, mitochondrial, germline, and somatic genomes is increasingly sophisticated.

Purpose of the Study:

To develop an automated and extensible meta-pipeline for comprehensive DNA sequencing data analysis.
To streamline the process from raw sequencing reads to feature detection and visualization.
To facilitate high-scale and integrated analysis of various genomic characteristics.

Main Methods:

Created metapipeline-DNA, an extensible Nextflow meta-pipeline.
Analyzes targeted and whole-genome sequencing data.
Covers pre-processing, feature detection, quality control, and data visualization.
Incorporates robust software engineering practices, including automated failure recovery and testing.
Cloud-compatible and highly configurable for subsetting and optimization.

Main Results:

Metapipeline-DNA automates the analysis of a wide range of genomic features.
The pipeline supports both targeted and whole-genome sequencing data.
It ensures robust analysis through automated recovery, testing, and verification.
Facilitates comprehensive and high-scale DNA sequencing data analysis.

Conclusions:

Metapipeline-DNA provides a unified, automated solution for complex DNA sequencing data analysis.
Its extensible and configurable nature supports diverse research needs.
The pipeline enhances the efficiency and scalability of genomic research.