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Related Concept Videos

Genomics02:02

Genomics

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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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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Genomic DNA in Eukaryotes00:58

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Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Updated: Aug 4, 2025

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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A Bin-Based Indexing for Scalable Range Join on Genomic Data.

Aman Sinha, Bo-Cheng Lai, Jhih-Yong Mai

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
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    PubMed
    Summary
    This summary is machine-generated.

    This study introduces BIndex, a novel algorithm for efficient range-join operations on genomic data. BIndex significantly speeds up analysis and reduces memory usage, overcoming limitations of existing tools for large-scale genome processing.

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

    • Bioinformatics
    • Computational Biology
    • Genomic Data Analysis

    Background:

    • Range-join is crucial for genomic data analysis, including variant annotation and comparison.
    • Current range-join algorithms struggle with large datasets due to quadratic complexity, limiting efficiency and scalability.
    • Existing tools face challenges in parallelism, scalability, and memory consumption for big genomic data.

    Purpose of the Study:

    • To propose BIndex, a novel bin-based indexing algorithm for high-throughput range-join processing.
    • To develop a distributed implementation of BIndex to enhance performance on large genomic datasets.
    • To address the limitations of existing range-join tools in terms of efficiency, parallelism, scalability, and memory usage.

    Main Methods:

    • Developed BIndex, a novel bin-based indexing algorithm with near-constant search complexity.
    • Implemented a distributed version of BIndex utilizing balanced partitioning for scalability.
    • Exploited the algorithm's parallel data structure for parallel computing architectures, including GPU acceleration.
    • Created add-in modules for Apache Spark for distributed big data processing.

    Main Results:

    • BIndex achieves significant speedups: up to 933.5x on Message Passing Interface (MPI) compared to state-of-the-art tools.
    • GPU-based acceleration of BIndex shows a 3.72x speedup over CPU implementations.
    • Apache Spark modules provide up to 4.65x speedup compared to the previous best tool.
    • The BIndex index structure is memory-efficient, consuming up to two orders of magnitude less RAM without compromising speed.

    Conclusions:

    • BIndex offers a highly efficient and scalable solution for range-join operations in genomic data analysis.
    • The algorithm's parallel nature and memory efficiency make it suitable for modern big data solutions and diverse bioinformatics workflows.
    • BIndex overcomes the performance bottlenecks of existing methods, enabling faster and more resource-efficient genome analysis.