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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...

Sanger Sequencing

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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: Jun 19, 2026

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Sense from sequence reads: methods for alignment and assembly.

Paul Flicek¹, Ewan Birney

¹European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. flicek@ebi.ac.uk

|October 22, 2009

Summary

This summary is machine-generated.

Understanding next-generation sequencing (NGS) data starts with alignment or assembly. This review covers current and future algorithms for processing large sequencing outputs.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) technologies generate vast amounts of data.
Accurate data processing is crucial for successful genomic experiments.
Alignment and assembly are foundational steps in analyzing sequencing data.

Purpose of the Study:

To review current algorithmic approaches for next-generation sequencing data alignment and assembly.
To discuss future directions in the development of these essential bioinformatics tools.
To provide examples of commonly used alignment and assembly tools.

Main Methods:

Literature review of existing algorithms and tools.
Discussion of algorithmic strategies for sequence alignment and assembly.
Comparative analysis of different approaches.

Main Results:

Several algorithms exist for aligning and assembling large sequencing datasets.
The choice of algorithm impacts the initial understanding of experimental results.
Ongoing development aims to improve efficiency and accuracy.

Conclusions:

Alignment and assembly are critical first steps in next-generation sequencing data analysis.
Understanding current algorithms is key to interpreting results.
Future algorithmic advancements will be driven by evolving sequencing technologies.