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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: May 12, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Metagenomic Data Preprocessing and Quality Control.

Bo Li¹, Jiying Xu², Tongyi Zhao²

¹Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, China. libo01@gdut.edu.cn.

Methods in Molecular Biology (Clifton, N.J.)

|May 10, 2026

Summary

This summary is machine-generated.

Preprocessing sequencing reads is crucial for accurate metagenomic analysis. This workflow ensures cleaner data by filtering, removing host DNA, and evaluating reads, minimizing bias for reliable results.

Keywords:

Host removal Metagenomic data Quality control Read trimming

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Last Updated: May 12, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Published on: November 21, 2018

Area of Science:

Bioinformatics
Genomics
Microbiology

Background:

Metagenomic analysis requires high-quality sequencing data.
Raw sequencing reads often contain contaminants and errors.
Inconsistent preprocessing can lead to unreliable downstream results.

Purpose of the Study:

To present a standardized four-step workflow for metagenomic read preprocessing.
To improve the reliability of metagenomic analysis through data cleaning.
To minimize contamination and technical bias in sequencing data.

Main Methods:

Raw sequencing data assessment.
Adapter and quality filtering of reads.
Host DNA removal techniques.
Final evaluation of cleaned reads.

Main Results:

A concise and reproducible preprocessing workflow.
Reduced levels of contamination and technical bias.
Improved data quality for downstream analysis.

Conclusions:

Standardized preprocessing is essential for accurate metagenomic analysis.
This workflow enhances the reliability of assembly and downstream analyses.
Implementing this method ensures cleaner, more trustworthy metagenomic data.