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

GASP: Gapped Ancestral Sequence Prediction for proteins.

Richard J Edwards1, Denis C Shields

  • 1Bioinformatics Core, Clinical Pharmacology, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Ireland. redwards@rcsi.ie

BMC Bioinformatics
|September 8, 2004
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

Peptidomics mapping of proteolysis highlights triple activation of sprouted seeds by germination, homogenisation and species mixture.

Food chemistry·2026
Same author

Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.

Nature methods·2026
Same author

Tissue Damage in Rheumatoid Arthritis Is Genetically Linked to Low Peptidylglycine Alpha-Amidating Monooxygenase Activity in Synovial Fibroblasts.

Arthritis & rheumatology (Hoboken, N.J.)·2026
Same author

Screening for Peptides to Bind and Functionally Inhibit SARS-CoV-2 Fusion Peptide Using Mirrored Combinatorial Phage Display and Human Proteomic Phage Display.

Molecules (Basel, Switzerland)·2026
Same author

How to build machine learning models able to extrapolate from standard to modified peptides.

Journal of cheminformatics·2025
Same author

Ocean Genomes: reference genome resources for marine vertebrates.

npj biodiversity·2025

A new algorithm, GASP (Gapped Ancestral Sequence Prediction), accurately predicts ancestral protein sequences, even with gaps in alignments. This bioinformatics tool handles insertions and deletions, improving ancestral sequence reconstruction.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Predicting ancestral protein sequences is crucial for bioinformatics.
  • Accurate prediction relies on high-quality alignments and phylogenies.
  • Existing methods struggle with gaps representing insertions/deletions.

Purpose of the Study:

  • Introduce GASP (Gapped Ancestral Sequence Prediction), a novel algorithm for ancestral sequence prediction.
  • Develop a method capable of handling gapped multiple sequence alignments.

Main Methods:

  • GASP utilizes a likelihood-based approach with amino acid substitution matrices.
  • It incorporates a two-pass strategy: downward from tips, then upward from the root.
  • Gap positions are assigned within the phylogeny before amino acid prediction.

Related Experiment Videos

Main Results:

  • GASP demonstrates comparable accuracy to existing algorithms on ungapped data.
  • The algorithm effectively handles insertions and deletions without significant accuracy loss.
  • Performance varies, with GASP outperforming or matching alternatives in different scenarios.

Conclusions:

  • GASP successfully predicts ancestral sequences from diverse multiple protein alignments, including those with gaps.
  • While not always surpassing advanced maximum likelihood methods, GASP offers versatility.
  • It reliably predicts ancestral residues for both gapped and ungapped positions.