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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genome Annotation and Assembly03:36

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.
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
Sanger Sequencing01:57

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 Sequencing03:00

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-seq03:21

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: Jun 25, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

SE: an algorithm for deriving sequence alignment from a pair of superimposed structures.

Chin-Hsien Tai1, James J Vincent, Changhoon Kim

  • 1Molecular Modeling and Bioinformatics Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. taic@mail.nih.gov

BMC Bioinformatics
|February 12, 2009
PubMed
Summary
This summary is machine-generated.

A new Seed Extension (SE) algorithm generates accurate sequence alignments from superimposed protein structures without using a gap penalty. This method outperforms traditional dynamic programming approaches, offering faster computation and improved accuracy for structural comparisons.

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Related Experiment Videos

Last Updated: Jun 25, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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Published on: August 14, 2018

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

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Published on: August 16, 2017

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Bioinformatics
  • Structural Biology
  • Computational Biology

Background:

  • Generating sequence alignments from superimposed structures is crucial for structure comparison, recognition, classification, and function prediction.
  • Traditional dynamic programming algorithms for this task can introduce spurious gaps and misalignments due to gap penalties.
  • A novel algorithm, Seed Extension (SE), is proposed to address these limitations.

Purpose of the Study:

  • To develop and evaluate a new algorithm for generating sequence alignments from superimposed protein structures.
  • To compare the accuracy and efficiency of the Seed Extension algorithm against existing dynamic programming methods.

Main Methods:

  • The Seed Extension (SE) algorithm identifies structurally equivalent residues as 'seeds'.
  • Seed segments are extended along the alignment matrix, using residue similarity and distance to resolve conflicts.
  • The algorithm was validated against manually curated alignments in the Conserved Domain Database.

Main Results:

  • SE achieved an average accuracy of 95.9% across 582 protein pairs, significantly outperforming dynamic programming methods (89.9%-91.0%).
  • SE demonstrated up to 18% higher accuracy for proteins with low sequence or structural similarity.
  • Implementing SE in SHEBA improved alignment accuracy by 10% and reduced CPU time compared to dynamic programming.

Conclusions:

  • The Seed Extension algorithm provides a fast and accurate method for sequence alignment from superimposed structures.
  • SE surpasses dynamic programming algorithms in accuracy and efficiency, particularly for challenging structural comparisons.
  • This algorithm offers a valuable improvement for structure-based sequence alignment in bioinformatics.