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Updated: Oct 29, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Haplotype-based membership inference from summary genomic data.

Diyue Bu¹, Xiaofeng Wang¹, Haixu Tang¹

¹Department of Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA.

Bioinformatics (Oxford, England)

|July 12, 2021

Summary

This summary is machine-generated.

Researchers can now infer human haplotypes from genomic data summaries without needing a target genome. Novel haplotypes can be reconstructed, enhancing privacy-preserving membership inference in genomic studies.

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

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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

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Published on: May 6, 2010

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Advancements in human genomic data processing drive biomedical innovation.
Privacy concerns hinder public genomic data sharing.
Current membership inference methods require a target genome, limiting privacy.

Purpose of the Study:

To demonstrate haplotype inference from genomic data summaries without a target genome.
To reconstruct novel haplotypes solely from allele frequencies.
To enhance membership inference algorithms for improved privacy.

Main Methods:

Inference of haplotypes (linked single nucleotide variations) from genomic data summaries.
Reconstruction of novel haplotypes using allele frequencies.
Development of a haplotype-based membership inference algorithm.

Main Results:

Haplotypes can be inferred from genomic data summaries without direct access to a target genome.
Novel haplotypes not present in databases can be reconstructed from allele frequencies.
The new algorithm offers greater power for membership inference compared to SNV-based methods.

Conclusions:

Haplotype inference from summary statistics enhances genomic data privacy.
Reconstructed haplotypes enable powerful, privacy-preserving membership inference.
The developed algorithms offer a significant advancement in secure genomic data analysis.