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Related Concept Videos

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
P-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects or...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

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Related Experiment Video

Updated: May 22, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Computing posterior probabilities for score-based alignments using ppALIGN.

Stefan Wolfsheimer1, Alexander Hartmann, Ralf Rabus

  • 1Department of Applied Mathematics (MAP5), University of Paris Descartes, France.

Statistical Applications in Genetics and Molecular Biology
|May 26, 2012
PubMed
Summary
This summary is machine-generated.

ppALIGN quantifies the reliability of pairwise sequence alignments using hidden Markov models. This bioinformatics tool helps identify questionable alignment regions, improving accuracy for DNA and protein sequence analysis.

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Last Updated: May 22, 2026

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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Published on: December 10, 2012

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Score-based pairwise alignments are crucial in bioinformatics tools like BLAST.
  • Current methods lack statistical descriptions of alignment reliability, especially in complex regions.
  • This uncertainty hinders accurate analysis of DNA and protein sequences.

Purpose of the Study:

  • To develop a method for computing position-wise reliability of score-based pairwise alignments.
  • To create a software package, ppALIGN, that uses hidden Markov models for this purpose.
  • To enable analysis of popular aligners without complex parameter tuning.

Main Methods:

  • Utilized hidden Markov model (HMM) techniques to model alignment reliability.
  • Developed a direct connection between scoring functions and probabilistic model parameters.
  • Implemented algorithms with linear time complexity for efficient computation.
  • Included alternative decoding algorithms for generating diverse alignments.

Main Results:

  • ppALIGN computes position-wise reliability for DNA and protein sequence alignments.
  • The package analyzes outcomes from popular tools like BLAST using only basic score parameters.
  • Demonstrated empirical usefulness in predicting reliable regions and identifying specific sequence features.

Conclusions:

  • ppALIGN provides a fast and reliable method for assessing pairwise alignment quality.
  • The software facilitates the detection and quantification of questionable alignment regions.
  • Its design allows seamless integration with other bioinformatics post-processing tools.