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Related Concept Videos

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.
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Evolutionary Relationships through Genome Comparisons

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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.
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Gene Duplication and Divergence02:37

Gene Duplication and Divergence

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Updated: Jul 5, 2026

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

DIALIGN-TX: greedy and progressive approaches for segment-based multiple sequence alignment.

Amarendran R Subramanian1, Michael Kaufmann, Burkhard Morgenstern

  • 1University of Tübingen, Wilhelm-Schickard-Institut für Informatik, Sand 13, 72076 Tübingen, Germany. subraman@informatik.uni-tuebingen.de

Algorithms for Molecular Biology : AMB
|May 29, 2008
PubMed
Summary
This summary is machine-generated.

DIALIGN-TX improves sequence alignment by combining greedy and progressive methods, enhancing accuracy for local and global sequence sets without excessive computational cost. This new heuristic offers superior performance, particularly for globally related sequences.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • DIALIGN-T, an improved multiple sequence alignment program, uses a greedy approach that can lead to suboptimal results due to spurious sequence similarities.
  • Previous versions of DIALIGN-T showed good performance but were limited by their straightforward greedy strategy.

Purpose of the Study:

  • To develop DIALIGN-TX, a novel multiple sequence alignment program that integrates a greedy algorithm with a progressive alignment approach.
  • To enhance the accuracy of multiple sequence alignments, especially for locally and globally related sequence sets.

Main Methods:

  • Implemented a new heuristic combining a greedy algorithm with a progressive alignment strategy using a guide tree.
  • Employed a vertex-cover approximation on a conflict graph to identify and mitigate spurious sequence similarities.
  • Benchmarked performance on established databases (BALIBASE 3, IRMBASE 2, BRAliBase II) and a new local alignment database (DIRM-BASE 1).

Main Results:

  • DIALIGN-TX produces significantly better alignments, particularly for globally related sequences, without substantial increases in CPU time or memory usage.
  • The new method demonstrated superior performance on locally related sequence sets (IRMBASE 2, DIRM-BASE 1) compared to existing programs.
  • DIALIGN-TX outperformed DIALIGN-T and CLUSTAL W on the BALIBASE3 benchmark for protein sequences.

Conclusions:

  • DIALIGN-TX offers improved accuracy in multiple sequence alignment, especially for locally related sequences.
  • While DIALIGN-TX is competitive with global aligners like MAFFT, MUSCLE, and T-COFFEE on global benchmarks, it excels in local alignment tasks.
  • The developed heuristic effectively balances alignment quality and computational efficiency.