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 Concept Videos

Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
DNA Base Pairing02:27

DNA Base Pairing

Erwin Chargaff’s rules on DNA equivalence paved the way for the discovery of base pairing in DNA. Chargaff’s rules state that in a double-stranded DNA molecule,
DNA Base Pairing02:27

DNA Base Pairing

Erwin Chargaff’s rules on DNA equivalence paved the way for the discovery of base pairing in DNA. Chargaff’s rules state that in a double-stranded DNA molecule,

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features.

Algorithms for molecular biology : AMB·2026
Same author

Publisher Correction: Geometric morphometrics approach for classifying children's nutritional status on out of sample data.

Scientific reports·2025
Same author

Cophylogeny Reconstruction Allowing for Multiple Associations Through Approximate Bayesian Computation.

Systematic biology·2023
Same author

Depthgram: Visualizing outliers in high-dimensional functional data with application to fMRI data exploration.

Statistics in medicine·2022
Same author

Correction: PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph.

PLoS computational biology·2021
Same author

Patient No-Show Prediction: A Systematic Literature Review.

Entropy (Basel, Switzerland)·2020
Same journal

Balanced mediated pathway detection in genomic data.

Statistical applications in genetics and molecular biology·2026
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
See all related articles

Related Experiment Video

Updated: May 23, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

A context dependent pair hidden Markov model for statistical alignment.

Ana Arribas-Gil1, Catherine Matias

  • 1Universidad Carlos III de Madrid, Spain.

Statistical Applications in Genetics and Molecular Biology
|April 14, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for aligning nucleotide sequences using a context-dependent evolutionary model. The approach enhances alignment accuracy, particularly for genomic regions with high mutation rates.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: May 23, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Genetics

Background:

  • Accurate statistical alignment of nucleotide sequences is crucial for evolutionary and comparative genomics.
  • Existing models often assume context-independent substitution processes, limiting accuracy in regions with varying mutation dynamics.

Purpose of the Study:

  • To develop a novel statistical alignment method incorporating context-dependent substitution rates.
  • To improve the accuracy of nucleotide sequence alignments by considering local sequence context.

Main Methods:

  • A generalized pair-hidden Markov model (HMM) framework is proposed.
  • The model integrates context-dependent mutation rates into the evolutionary model.
  • A stochastic approximation expectation maximization (SAEM) algorithm is employed for parameter and alignment estimation.

Main Results:

  • The proposed method demonstrates improved alignment accuracy on simulated data.
  • Application to vertebrate genomes, particularly regions with high CG dinucleotide mutation rates, shows enhanced performance.
  • Accurate alignment of a human pseudogene and its functional gene is achieved, validating the method's efficacy.

Conclusions:

  • The novel context-dependent evolutionary model significantly improves statistical alignment accuracy.
  • This approach offers a more refined tool for analyzing genomic sequences, especially in mutation-prone regions.
  • The method has practical implications for comparative genomics and evolutionary studies.