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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Secure Similar Patients Query on Encrypted Genomic Data.

Md Safiur Rahman Mahdi, Md Momin Al Aziz, Dima Alhadidi

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    This study introduces a secure indexing algorithm for genomic data privacy. The prefix-tree method ensures data, query, and output privacy for cloud-based analysis.

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    Area of Science:

    • Bioinformatics
    • Genomic Data Security
    • Cloud Computing

    Background:

    • Genomic data generation is increasing rapidly, necessitating cloud outsourcing for flexibility and storage management.
    • Protecting sensitive genomic data during cloud outsourcing requires robust encryption methods.
    • Efficient and secure execution of researcher queries on large genomic datasets is a significant challenge.

    Purpose of the Study:

    • To develop a novel indexing algorithm for secure and efficient querying of outsourced genomic data.
    • To ensure data privacy, query privacy, and output privacy for genomic datasets in the cloud.
    • To provide a scalable and fast solution for real-world biomedical applications.

    Main Methods:

    • An indexing algorithm based on the prefix-tree structure was developed.
    • Privacy guarantees (data, query, output) are achieved using encryption and garbled circuits.
    • The approach is designed to be secure against a semi-honest adversary model.

    Main Results:

    • The proposed method successfully guarantees data, query, and output privacy.
    • Experimental results demonstrate the method's scalability and speed for biomedical applications.
    • Performance comparisons show the algorithm outperforms existing state-of-the-art techniques.

    Conclusions:

    • The prefix-tree based indexing algorithm offers a secure and efficient solution for querying outsourced genomic data.
    • The method effectively addresses privacy concerns in cloud-based genomic data analysis.
    • This approach is suitable for practical implementation in real-life biomedical research.