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

Predicting reliable regions in protein sequence alignments.

Melissa Cline1, Richard Hughey, Kevin Karplus

  • 1Center for Biomolecular Science and Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA. cline@soe.ucsc.edu

Bioinformatics (Oxford, England)
|February 16, 2002
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

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline ATM sequence variants.

American journal of human genetics·2026
Same author

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline ATM sequence variants.

American journal of human genetics·2024
Same author

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline <i>ATM</i> sequence variants.

medRxiv : the preprint server for health sciences·2024
Same author

Altering under-represented DNA sequences elevates bacterial transformation efficiency.

mBio·2023
Same author

Big advocacy, little recognition: the hidden work of Black patients in precision medicine.

Journal of community genetics·2023
Same author

Big Advocacy, Little Recognition: The Hidden Work of Black Patients in Precision Medicine.

Research square·2023
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

Identifying unreliable protein sequence alignment positions is crucial for accurate bioinformatics analysis. Near-optimal alignment information effectively removes misaligned positions while preserving accurate ones.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Protein sequence alignments are fundamental to various bioinformatics applications, including structure prediction and phylogenetic analysis.
  • Current alignment methods are prone to errors, which can propagate and lead to inaccurate downstream analyses.
  • A robust method to identify and remove unreliable alignment positions is needed to improve the reliability of protein sequence analysis.

Purpose of the Study:

  • To develop and evaluate methods for identifying and removing unreliable positions in protein sequence alignments.
  • To assess the effectiveness of different predictors of alignment position reliability.

Main Methods:

  • Four predictors of alignment position reliability were tested: near-optimal alignment information, column score, and secondary structural information.

Related Experiment Videos

  • Predictors were validated against a comprehensive library of protein sequence alignments.
  • Positions identified as unreliable by the predictors were systematically removed.

    • Main Results:

    • Near-optimal alignment information demonstrated the highest predictive accuracy.
    • This method successfully removed 70% of substantially misaligned positions and 58% of over-aligned positions.
    • Importantly, 86% of accurately aligned positions were retained, minimizing the loss of valid data.

    Conclusions:

    • The developed method, particularly using near-optimal alignment information, significantly enhances the reliability of protein sequence alignments.
    • Removing unreliable positions improves the accuracy of downstream bioinformatics applications.
    • This approach offers a valuable tool for protein sequence analysis, leading to more dependable structural and evolutionary inferences.