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Statistical framework to determine indel-length distribution.

Elya Wygoda¹, Gil Loewenthal¹, Asher Moshe¹

¹The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.

Bioinformatics (Oxford, England)

|January 25, 2024

Summary

This summary is machine-generated.

Accurate inference of indel-length distribution is crucial for evolutionary studies. A Zipf distribution best fits most alignments, outperforming traditional gap-counting methods.

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Area of Science:

Molecular Evolution
Bioinformatics
Computational Biology

Background:

Insertions and deletions (indels) are frequent molecular evolutionary events impacting macro-evolution.
Accurate indel-length distribution inference is vital for evolutionary applications, especially alignment software.
Previous methods using gap-counting are statistically limited and alignment-biased.

Purpose of the Study:

To rigorously determine the best-fitting indel-length distribution for alignments.
To develop statistically sound methodologies for indel model selection.
To assess the adequacy of current indel models in representing evolutionary processes.

Main Methods:

Reduced alignment bias using a machine-learning algorithm.
Applied Approximate Bayesian Computation (ABC) for rigorous model selection.
Developed a novel method to test the fit of existing indel models.

Main Results:

The optimal indel-length distribution model varies across different alignments.
A Zipf length distribution was found to be the best fit for the majority of alignments analyzed.
The developed methods provide a statistically rigorous approach to indel model selection.

Conclusions:

The Zipf distribution is a superior model for characterizing indel lengths in many alignments.
This work offers improved statistical rigor for analyzing indel evolutionary processes.
The findings have significant implications for the development of more accurate bioinformatics alignment software.