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Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...

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Updated: Oct 14, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Fast and accurate bootstrap confidence limits on genome-scale phylogenies using little bootstraps.

Sudip Sharma^1,2, Sudhir Kumar^1,2,3

¹Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA.

Nature Computational Science

|November 4, 2021

Summary

The little bootstraps method offers a faster way to assess species relationships using phylogenetics. This computational approach provides similar confidence levels to standard bootstrap but with significantly reduced time and memory.

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Area of Science:

Phylogenetics
Computational Biology
Bioinformatics

Background:

Felsenstein's bootstrap is standard for assessing confidence in species phylogenies from sequence alignments.
Standard bootstrap involves computationally intensive resampling of alignment sites.
High computational burden limits its application in large phylogenomic datasets.

Purpose of the Study:

Introduce a computationally efficient bootstrapping method for phylogenetics.
Compare the effectiveness of the new method against standard bootstrap.
Enhance the feasibility of phylogenomic analyses with large datasets.

Main Methods:

Developed the "bag of little bootstraps" approach for phylogenetics.
Involves bootstrapping small subsets of sites rather than entire alignments.
Aggregated results from multiple small bootstrap samples to estimate confidence.

Main Results:

The "bag of little bootstraps" yields confidence limits comparable to standard bootstrap.
Achieved similar confidence in species relationships with a fraction of computational time and memory.
Demonstrated significant improvements in efficiency and reduced computational load.

Conclusions:

The "bag of little bootstraps" provides a rigorous and efficient alternative to standard bootstrap.
This method can enhance the rigor, efficiency, and parallelization of big data phylogenomic analyses.
Facilitates more accessible and scalable phylogenetic inference.