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

Kalign--an accurate and fast multiple sequence alignment algorithm.

Timo Lassmann1, Erik L L Sonnhammer

  • 1Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius vag 35, S-17177 Stockholm, Sweden. timo.lassmann@cgb.ki.se

BMC Bioinformatics
|December 14, 2005
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

Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer.

Scientific reports·2026
Same author

GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression data.

Bioinformatics advances·2026
Same author

Mesothelioma location influences the tumour microenvironment and immune checkpoint therapy response in preclinical models.

Scientific reports·2026
Same author

AI succeeds in diagnosing rare diseases.

Nature·2026
Same author

A precision medicine approach to interpret a GATA4 genetic variant in a paediatric patient with congenital heart disease.

Human genomics·2026
Same author

The United Nations convention on rare diseases-A framework for research prioritization.

Frontiers in public health·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Kalign is a new multiple sequence alignment (MSA) method that is both fast and accurate. It outperforms existing methods, especially for large and distantly related sequence sets, making it ideal for comparative genomics.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment (MSA) is crucial for analyzing protein families, identifying conserved motifs, and understanding evolutionary relationships.
  • Current MSA tools face challenges with accuracy and computational cost, limiting their use in large-scale comparative genomics.
  • The increasing availability of complete genome sequences necessitates more efficient and accurate MSA methods.

Purpose of the Study:

  • To develop a novel method for multiple sequence alignment that improves both accuracy and computational speed.
  • To address the limitations of existing MSA programs in handling large datasets and distantly related sequences.

Main Methods:

  • Developed Kalign, a novel multiple sequence alignment method.

Related Experiment Videos

  • Utilized the Wu-Manber string-matching algorithm within the Kalign framework.
  • Evaluated Kalign's performance against established methods using benchmark datasets (e.g., Balibase, Prefab) and a new large test set.

    • Main Results:

    • Kalign demonstrated comparable accuracy to existing methods on small alignments.
    • Kalign significantly improved accuracy when aligning large and distantly related sequence sets.
    • Kalign achieved substantial speed improvements, being approximately 10 times faster than ClustalW and up to 50 times faster than iterative methods.

    Conclusions:

    • Kalign is a fast, robust, and accurate multiple sequence alignment method.
    • Kalign is particularly well-suited for the demanding task of aligning large numbers of biological sequences.
    • The method offers a valuable tool for large-scale comparative genomics research.