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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.

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 microarray-based...

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Related Experiment Video

Updated: Jul 11, 2026

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

An automated, high-throughput sequence read classification pipeline for preliminary genome characterization.

Philippe Chouvarine¹, Surya Saha, Daniel G Peterson

¹Mississippi Genome Exploration Laboratory, Mississippi State University, Mississippi State, MS 39762, USA.

Analytical Biochemistry

|September 18, 2007

Summary

We developed an automated Sequence Read Classification Pipeline (SRCP) for high-throughput genome structure analysis using shotgun sequence reads. This tool rapidly characterizes genome content, aiding subsequent annotation for any eukaryotic genome.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Complete genome sequences are not always available, necessitating methods for genome structure analysis from partial data.
Survey sequencing of genomic DNA provides valuable insights into genome structure when full sequences are absent.
High-throughput characterization of genome structure from shotgun sequence reads is crucial for genomic studies.

Purpose of the Study:

To develop an automated Sequence Read Classification Pipeline (SRCP) for efficient genome structure characterization.
To enable high-throughput analysis of genome structure using shotgun sequence reads.
To provide a tool for estimating gene/repeat enrichment in reduced-representation sequencing.

Main Methods:

Development of an automated Sequence Read Classification Pipeline (SRCP).
Integration of novel and standard sequence analysis algorithms within a decision tree framework.
Classification of sequence reads into functional/descriptive categories.
Priming the SRCP with genomic sequence data for enrichment estimation.

Main Results:

The SRCP successfully classifies shotgun sequence reads into 'best fit' functional/descriptive categories.
The pipeline allows for estimation of gene and repeat enrichment in reduced-representation sequencing techniques.
Initial testing on Sorghum bicolor yielded results comparable in quality to manual classification.
The SRCP provides a rapid, high-quality overview of genome sequence content.

Conclusions:

The automated Sequence Read Classification Pipeline (SRCP) is a valuable tool for rapid genome structure overview.
SRCP facilitates subsequent genome annotation by providing preliminary sequence content characterization.
The SRCP is adaptable for the analysis of any eukaryotic genome, offering broad applicability.