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

Research on parameterized algorithms of the individual haplotyping problem.

Minzhu Xie1, Jian'er Chen, Jianxin Wang

  • 1School of Information Science and Engineering, Central South University, Changsha, Hunan Province 410083, China. xieminzhu@hotmail.com

Journal of Bioinformatics and Computational Biology
|August 11, 2007
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

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks.

BMC bioinformatics·2020
Same author

DeepFrag-k: a fragment-based deep learning approach for protein fold recognition.

BMC bioinformatics·2020
Same author

Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model.

Genome research·2020
Same author

Drug-drug similarity measure and its applications.

Briefings in bioinformatics·2020
Same author

Computational drug repositioning based on multi-similarities bilinear matrix factorization.

Briefings in bioinformatics·2020
Same author

Early evidence for mounted horseback riding in northwest China.

Proceedings of the National Academy of Sciences of the United States of America·2020
Same journal

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026
Same journal

ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Journal of bioinformatics and computational biology·2026
Same journal

When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis.

Journal of bioinformatics and computational biology·2026
Same journal

Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction.

Journal of bioinformatics and computational biology·2026
Same journal

Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks.

Journal of bioinformatics and computational biology·2026
Same journal

Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.

Journal of bioinformatics and computational biology·2026
See all related articles

This study presents efficient algorithms for individual haplotyping, reconstructing two haplotypes from sequencing data. The new methods improve computational efficiency for Minimum SNP Removal and Minimum Fragment Removal problems.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Individual haplotyping is crucial for understanding genetic variation.
  • Existing computational methods for haplotyping can be inefficient for large datasets.

Purpose of the Study:

  • To develop novel, efficient algorithms for the individual haplotyping problem.
  • To address the Minimum SNP Removal (MSR) and Minimum Fragment Removal (MFR) problems in haplotyping.

Main Methods:

  • Introduced parameterized haplotyping problems based on fragment length and SNP coverage.
  • Developed algorithms with time complexities dependent on fragment count (m), SNP sites (n), and small parameters k(1) and k(2).

Main Results:

  • Achieved efficient solutions for gapless MSR and MFR problems.

Related Experiment Videos

  • Algorithms demonstrate improved performance compared to existing methods, especially when k(1) and k(2) are small (approx. 10).

    • Conclusions:

    • The proposed algorithms offer a more efficient and practical approach to individual haplotyping.
    • This advancement has implications for genetic analysis and disease research.