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

Sarment: Python modules for HMM analysis and partitioning of sequences.

Laurent Guéguen1

  • 1Laboratoire Biométrie et Biologie Evolutive, (UMR 5558); (NRS); Univ Lyon 1, 43 bd 11 Nov, 69622 Villeurbanne cedex, France. gueguen@biomserv.univ-lyon1.fr

Bioinformatics (Oxford, England)
|June 11, 2005
PubMed
Summary

Sarment is a Python package for sequence segmentation. It offers efficient algorithms for hidden Markov models and predictive partitioning, simplifying data analysis.

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

Degrees of convergent evolution in rodent adaptations to arid environments.

Genome research·2026
Same author

External validation of a knowledge-based clinical toxicology consultant for diagnosing single exposures.

Clinical toxicology (Philadelphia, Pa.)·2025
Same author

Comparative transcriptomics in serial organs uncovers early and pan-organ developmental changes associated with organ-specific morphological adaptation.

Nature communications·2025
Same author

SAMD9L acts as an antiviral factor against HIV-1 and primate lentiviruses by restricting viral and cellular translation.

PLoS biology·2024
Same author

Dollo Parsimony Overestimates Ancestral Gene Content Reconstructions.

Genome biology and evolution·2024
Same author

Distinct evolutionary trajectories of SARS-CoV-2-interacting proteins in bats and primates identify important host determinants of COVID-19.

Proceedings of the National Academy of Sciences of the United States of America·2022

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • Sequence segmentation is crucial for analyzing complex biological and data sequences.
  • Existing tools may lack flexibility or efficiency in handling diverse segmentation models.

Purpose of the Study:

  • To introduce Sarment, a Python package designed for streamlined sequence segmentation.
  • To provide efficient implementations of hidden Markov models and maximal predictive partitioning algorithms.

Main Methods:

  • Development of object-oriented Python modules for sequence segmentation.
  • Implementation of standard algorithms for hidden Markov model computation.
  • Integration of maximal predictive partitioning techniques.

Related Experiment Videos

Main Results:

  • Sarment offers a versatile approach to sequence segmentation with a wide range of criteria.
  • Efficient computation of hidden Markov models and predictive partitioning is achieved.
  • Object-oriented design facilitates easy manipulation of segmentation results.

Conclusions:

  • Sarment provides a powerful and user-friendly tool for sequence segmentation tasks.
  • The package supports diverse models and simplifies the analysis of segmented data.