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

Statistical analysis of DNA sequences.

B S Weir1

  • 1Department of Statistics, North Carolina State University, Raleigh 27695-8203.

Journal of the National Cancer Institute
|May 18, 1988
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

Global diversity analysis of plant-associated <i>Pseudopithomyces</i> fungi reveals a new species producing the toxin associated with facial eczema in livestock: <i>Pseudopithomyces toxicarius sp. nov</i>.

Studies in mycology·2026
Same author

<i>Fusarium</i>: more than a node or a foot-shaped basal cell.

Studies in mycology·2021
Same author

Phylogenetic relationships of eight new <i>Dacrymycetes</i> collected from New Zealand.

Persoonia·2017
Same author

Fungal Planet description sheets: 558-624.

Persoonia·2017
Same author

ESTIMATION OF GENE FLOW FROM F-STATISTICS.

Evolution; international journal of organic evolution·2017
Same author

MAINTENANCE OF MALES AND FEMALES IN HERMAPHRODITE POPULATIONS AND THE EVOLUTION OF DIOECY.

Evolution; international journal of organic evolution·2017
Same journal

Response to Wang et al. and Shen et al.

Journal of the National Cancer Institute·2026
Same journal

Cluster randomized controlled trial of decision support for breast cancer chemoprevention, MiCHOICE.

Journal of the National Cancer Institute·2026
Same journal

Beyond R2: Assessing quality of trial level surrogate endpoints in colorectal cancer.

Journal of the National Cancer Institute·2026
Same journal

A novel classification of small bowel adenocarcinoma based on the hidden genome classifier: a multi-institutional study.

Journal of the National Cancer Institute·2026
Same journal

Response to Yu, Wang and Ge.

Journal of the National Cancer Institute·2026
Same journal

Re: Cardiovascular disease risk after radiotherapy and anthracycline-based chemotherapy for diffuse large B-cell lymphoma.

Journal of the National Cancer Institute·2026
See all related articles

Statistical methods for DNA sequence analysis have advanced significantly since 1984. These techniques enable similarity searches, sequence alignment, and the construction of evolutionary trees, aiding in understanding genetic relationships.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • The analysis of DNA sequence data has become increasingly sophisticated.
  • Understanding genetic variation and evolutionary relationships requires robust statistical approaches.

Purpose of the Study:

  • To review the advancements in statistical analysis of DNA sequence data since 1984.
  • To highlight key mathematical and theoretical developments in the field.

Main Methods:

  • Dynamic programming for sequence similarity searching and alignment.
  • Markov chain theory applied to sequence analysis.
  • Modeling biological forces like mutation and genetic drift.
  • Statistical inference using random permutations of sequences.

Related Experiment Videos

Main Results:

  • Development of algorithms for efficient sequence comparison.
  • Integration of evolutionary models for phylogenetic tree construction.
  • Methods for assessing statistical significance of observed genetic patterns.

Conclusions:

  • Statistical analysis has transformed DNA sequence interpretation.
  • Advanced mathematical models are crucial for understanding evolutionary processes.
  • Random permutation testing remains vital for validating complex sequence inferences.