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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.

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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Sequence alignment as hypothesis testing.

Lu Meng1, Fengzhu Sun, Xuegong Zhang

  • 1MOE Key Lab of Bioinformatics and Bioinformatics Division of TNLIST/Department of Automation, Tsinghua University, Beijing, P.R. China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 11, 2011
PubMed
Summary
This summary is machine-generated.

Choosing the right scoring function is crucial for accurate sequence alignment. Our study shows the log-likelihood ratio scoring function offers the highest statistical power and accuracy for detecting sequence segments.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • Sequence alignment is fundamental in bioinformatics for comparing biological sequences.
  • The scoring function critically influences the accuracy of local alignment algorithms.
  • Understanding the statistical properties of scoring functions is essential for reliable segment detection.

Purpose of the Study:

  • To investigate the relationship between scoring functions and the distribution of aligned pairs in sequence alignment.
  • To evaluate different scoring functions within a hypothesis testing framework.
  • To identify the most statistically powerful and accurate scoring function for local sequence alignment.

Main Methods:

  • Formulating sequence alignment as a hypothesis testing problem.
  • Conducting extensive simulation experiments to analyze scoring function performance.
  • Analyzing the statistical distribution of letter pairs under various scoring functions.

Main Results:

  • Demonstrated that scoring functions with negative expectation in local alignment equate to a hypothesis test.
  • Identified the log-likelihood ratio scoring function as statistically most powerful.
  • Showcased the superior accuracy of the log-likelihood ratio for detecting segments defined by specific letter pair distributions.

Conclusions:

  • The choice of scoring function significantly impacts sequence alignment outcomes.
  • The log-likelihood ratio scoring function provides optimal statistical power and accuracy for local alignment.
  • This framework enhances the detection of biologically relevant sequence segments.