Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Combining many multiple alignments in one improved alignment.

K Bucka-Lassen1, O Caprani, J Hein

  • 1Object Oriented Ltd, 6004 Luzern, Switzerland, Department of Computer Science and Department of Ecology and Genetics, University of Aarhus, 8000 Aarhus C, Denmark.

Bioinformatics (Oxford, England)
|March 25, 1999
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Using Hologram-Based Augmented Reality in Anatomy Learning: The TEACHANATOMY Randomized Trial.

Academic medicine : journal of the Association of American Medical Colleges·2025
Same author

Characterization and application of nonlinear plastic materials for post-CPA pulse compression.

Optics letters·2020
Same author

Impact of white-spotting alleles, including W20, on phenotype in the American Paint Horse.

Animal genetics·2020
Same author

Efficient Laser-Driven Proton Acceleration from a Cryogenic Solid Hydrogen Target.

Scientific reports·2019
Same author

[Minimally invasive vs. open partial nephrectomy : Perioperative success and complication rates].

Der Urologe. Ausg. A·2018
Same author

[Primary hyperparathyroidism after obesity surgery].

Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen·2014
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces ComAlign, a novel algorithm that extracts and combines high-quality sub-alignments from multiple sequence alignments to generate improved results. ComAlign offers a practical approach to complex alignment challenges, enhancing biological sequence analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is computationally complex, leading to heuristic algorithms with varying degrees of optimality.
  • Existing heuristics often lack guarantees for optimal solutions but can yield valuable sub-alignment information.
  • Improving alignment quality from suboptimal heuristic outputs is crucial for accurate biological sequence analysis.

Purpose of the Study:

  • To develop a method for extracting and combining high-quality sub-alignments from existing multiple sequence alignments.
  • To create a novel algorithm, ComAlign, that integrates these sub-alignments into a potentially improved overall alignment.
  • To address the limitations of heuristic approaches in multiple sequence alignment by leveraging partial alignment data.

Main Methods:

Related Experiment Videos

  • The ComAlign algorithm was developed as a variant of traditional dynamic programming techniques.
  • It systematically extracts qualitatively superior sub-alignments from a set of generated multiple alignments.
  • These selected sub-alignments are then merged to form a new, refined alignment.

Main Results:

  • ComAlign was tested on artificial and 5S RNA sequence datasets, demonstrating its efficacy.
  • The algorithm produced alignment scores comparable to established methods like MSA 2.1.
  • Analysis confirmed that ComAlign integrates distinct alignment segments rather than merely selecting the best individual alignment.

Conclusions:

  • ComAlign effectively combines partial alignments to generate improved overall multiple sequence alignments.
  • The method offers a viable strategy for enhancing results from complex heuristic alignment algorithms.
  • The ComAlign software is freely available, promoting its use in bioinformatics research.