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

BLMT: statistical sequence analysis using N-grams.

Madhavi Ganapathiraju1, Vijayalaxmi Manoharan, Judith Klein-Seetharaman

  • 1Language Technologies Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA.

Applied Bioinformatics
|February 8, 2005
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

Characterization of Recombinant GMPR from <i>Pocillopora damicornis</i> and Potential Mechanisms of Cold-Induced Metabolic Adaptation.

Biology·2026
Same author

Comprehensive and quantitative molecular docking analysis of rhodopsin-retinal interactions.

Biophysical journal·2026
Same author

Author Correction: 7-Dehydrocholesterol is an endogenous suppressor of ferroptosis.

Nature·2026
Same author

Three Unrelated Children With Childhood Apraxia of Speech: Exome Sequencing and Functional Gene Analysis Imply a Role of Laminin-511 in Early Neurodevelopment.

Case reports in genetics·2026
Same author

Allostery-Driven Substrate Gating in the Chlorothalonil Dehalogenase from <i>Pseudomonas</i> sp. CTN-3.

Biology·2026
Same author

Speech and Language Development of Two Brothers With Bainbridge-Ropers Syndrome: Phenotypic and Bioinformatic Support for a Cerebellar ASXL3 Hypothesis.

American journal of medical genetics. Part A·2025
Same journal

Statistically consistent identification of differentially expressed genes in DNA chip data over the whole expression range: relative variance method.

Applied bioinformatics·2006
Same journal

A nonparametric likelihood ratio test to identify differentially expressed genes from microarray data.

Applied bioinformatics·2006
Same journal

Simulation study of ratio calculation formulae of two-colour cDNA microarray data.

Applied bioinformatics·2006
Same journal

Alternative mRNA polyadenylation can potentially affect detection of gene expression by affymetrix genechip arrays.

Applied bioinformatics·2006
Same journal

Comparisons of annotation predictions for affymetrix GeneChips.

Applied bioinformatics·2006
Same journal

Ontology annotation treebrowser : an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists.

Applied bioinformatics·2006
See all related articles

This study introduces the Biological Language Modeling Toolkit (BLMT) for calculating n-gram statistics in biological sequences. This tool addresses the need for efficient analysis of non-consecutive sequence features in bioinformatics.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics
  • Proteomics

Background:

  • Statistical analysis of biological sequences, particularly sequence alignment, is fundamental in molecular biology.
  • Traditional methods often require consecutive sequence features, limiting analysis for certain bioinformatics tasks.
  • N-grams (k-tuples) offer a flexible alternative for representing biological sequence features, adapted from language technologies.

Purpose of the Study:

  • To present a novel, publicly accessible tool for the efficient calculation of n-gram statistics on biological sequence datasets.
  • To overcome the limitations of existing tools that disregard short sequence matches due to lack of statistical significance.
  • To provide a generic solution for analyzing arbitrary biological sequence data using n-gram models.

Related Experiment Videos

Main Methods:

  • Development of the integrated Biological Language Modeling Toolkit (BLMT).
  • Implementation of efficient n-gram computation algorithms.
  • Adaptation of language modeling techniques for biological sequence analysis.

Main Results:

  • The BLMT enables efficient calculation of n-gram statistics for diverse biological sequence datasets.
  • The toolkit provides a solution for analyzing non-consecutive sequence features, enhancing bioinformatics analyses.
  • Demonstrates the utility of n-gram statistics in the biological domain through a dedicated computational tool.

Conclusions:

  • The Biological Language Modeling Toolkit (BLMT) offers a valuable resource for researchers in molecular biology and bioinformatics.
  • BLMT facilitates advanced statistical analysis of biological sequences by enabling efficient n-gram computation.
  • The availability of BLMT promotes wider adoption of n-gram-based approaches in biological sequence analysis.