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Related Concept Videos

Genomics02:02

Genomics

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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

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Related Experiment Video

Updated: May 9, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

'Big data', Hadoop and cloud computing in genomics.

Aisling O'Driscoll1, Jurate Daugelaite, Roy D Sleator

  • 1Department of Computing, Cork Institute of Technology, Rossa Avenue, Bishopstown, Cork, Ireland.

Journal of Biomedical Informatics
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

Genomic data is rapidly expanding. Cloud computing and big data technologies like Apache Hadoop are essential for analyzing these large datasets to drive future medical discoveries.

Keywords:
Big dataBioinformaticsCloud computingGenomicsHadoop

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

Related Experiment Videos

Last Updated: May 9, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • The Human Genome Project completion spurred massive growth in genomic sequence data.
  • Future medical discoveries rely on analyzing increasingly large genomic datasets.
  • Decreasing sequencing costs exacerbate the challenge of managing big data in biology.

Purpose of the Study:

  • To provide an overview of cloud computing and big data technologies.
  • To discuss the application of these technologies in handling biological big data.
  • To highlight the Apache Hadoop project for processing petabyte-scale datasets.

Main Methods:

  • Overview of cloud computing principles.
  • Explanation of big data technologies, focusing on Apache Hadoop.
  • Discussion of Hadoop's distributed and parallelized data processing capabilities.

Main Results:

  • Apache Hadoop enables efficient processing and analysis of petabyte-scale genomic data.
  • Cloud computing offers scalable infrastructure for big data challenges in biology.
  • Hadoop is increasingly adopted within the bioinformatics community.

Conclusions:

  • Cloud computing and big data technologies are crucial for advancing genomic research.
  • Apache Hadoop provides a robust framework for analyzing large-scale biological datasets.
  • Effective data analysis strategies are vital for realizing the potential of genomic medicine.