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Related Concept Videos

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...
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Multi-species Conserved Sequences

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Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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Next-generation Sequencing03:00

Next-generation Sequencing

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RNA-seq03:21

RNA-seq

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Updated: Jun 26, 2026

The ITS2 Database
16:17

The ITS2 Database

Published on: March 12, 2012

Sequence database search using jumping alignments.

R Spang1, M Rehmsmeier, J Stoye

  • 1German Cancer Research Center (DKFZ), Theoretical Bioinformatics, Heidelberg, Germany.

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|September 8, 2000
PubMed
Summary
This summary is machine-generated.

We developed a novel jumping alignment algorithm for protein sequence classification and remote homology detection. This method outperforms hidden Markov models in identifying distant protein relatives by utilizing both vertical and horizontal alignment information.

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Last Updated: Jun 26, 2026

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Protein Sequence Analysis

Background:

  • Established methods like profiles and hidden Markov models primarily focus on vertical information in multiple sequence alignments.
  • There is a need for methods that can effectively exploit both vertical and horizontal information for robust protein classification.
  • Detecting remote homologues is crucial for understanding protein function and evolution.

Purpose of the Study:

  • To introduce a new algorithm for amino acid sequence classification and remote homology detection.
  • To evaluate the performance of this new algorithm against established methods, specifically hidden Markov models.

Main Methods:

  • Developed a 'jumping alignment' algorithm, extending the Smith-Waterman algorithm to compute local alignments between a single sequence and a multiple sequence alignment.
  • The algorithm aligns a candidate sequence to a reference sequence within the multiple alignment, allowing the reference sequence to change with a penalty for jumps.
  • Compared the discriminative quality of the jumping alignment algorithm against hidden Markov models using a subset of the SCOP database, assessing performance via false positive counts.

Main Results:

  • The jumping alignment algorithm exploits both vertical and horizontal information in multiple sequence alignments, unlike methods focusing solely on vertical data.
  • For moderate false positive counts (above five), the new algorithm demonstrated a considerably higher success rate in identifying true positives compared to hidden Markov models.
  • The method effectively selects proteins belonging to a specific superfamily from a candidate database.

Conclusions:

  • The novel jumping alignment algorithm offers a more balanced exploitation of multiple sequence alignment information.
  • This approach shows superior performance in detecting remote protein homologues compared to hidden Markov models, particularly in challenging classification tasks.
  • The algorithm represents a significant advancement in bioinformatics tools for protein family classification and evolutionary analysis.