Related Experiment Video
Updated: Jul 13, 2026
07:08
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Effects of long-range correlations in DNA on sequence alignment score statistics
Philipp W Messer1, Ralf Bundschuh, Martin Vingron
1Max Planck Institute for Molecular Genetics, Berlin, Germany. philipp.messer@molgen.mpg.de
Summary
Long-range correlations in genomic base composition significantly alter sequence alignment scores. Accounting for these correlations is crucial for accurate statistical analysis of genomic data.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Long-range correlations in base composition are common in eukaryotic genomes.
- These correlations affect statistical properties of genomic sequences.
Purpose of the Study:
- To investigate how long-range correlations influence sequence alignment score statistics.
- To quantify the impact of these correlations on the extreme value distribution of alignment scores.
Main Methods:
- Developed a Gaussian approximation to model correlated score landscapes.
- Calculated corrections to the scale parameter (lambda) of the alignment score distribution.
- Performed numerical studies using a novel algorithm for generating correlated random sequences.
Main Results:
- Long-range correlations substantially shift both the mean and the tail of the alignment score distribution.
- The calculated analytic results align with numerical findings.
- The significance of alignment scores changes when correlations are included in the null model.
Conclusions:
- Genomic base composition correlations necessitate adjustments in sequence alignment score interpretation.
- The study provides a framework for incorporating these correlations into statistical null models.
- Understanding these effects is vital for accurate biological interpretation of sequence alignments.
Related Concept Videos
02:54Evolutionary Relationships through Genome Comparisons
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
02:05Gene Evolution - Fast or Slow?
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
In contrast, regions which code...
00:53Correlation and Regression
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
01:20Spearman's Rank Correlation Test
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
Spearman's test calculates correlation by...
01:09Correlation
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
01:10Calibration Curves: Correlation Coefficient
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...