Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

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...

Genomics

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...

Genome-wide Association Studies-GWAS

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...

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...

Applications of Molecular Taxonomy

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos.

Algorithms·2026

Same author

Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder.

Algorithms·2026

Same author

Abstention and Threshold Identification for Uncertainty Management in Clinical Decision Tools: A Case Study using Human-In-The-Loop Pediatric Autism Classifiers.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026

Same author

Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10.

Scientific reports·2025

Same author

Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation.

JMIR AI·2025

Same author

Mondo: integrating disease terminology across communities.

Genetics·2025

Same journal

Proteins Rich in Prime Mass Residues and Their Role in Cell Biology.

Evolutionary bioinformatics online·2026

Same journal

Hybrid Feature Selection-Based Machine Learning and Deep Learning Framework for Biomarker Prediction From RNA-seq Data During Dengue Fever to Severe Dengue Progression.

Evolutionary bioinformatics online·2026

Same journal

Functional Annotation and Molecular Characterization of a Hypothetical Protein From <i>Mycobacterium leprae</i> Through <i>In Silico</i> Approaches: A Potential Therapeutic Target.

Evolutionary bioinformatics online·2026

Same journal

Evolutionary Bioinformatics Expands its Breadth.

Evolutionary bioinformatics online·2026

Same journal

Gene Expression Underlying Teloblast Formation in Leech (Annelida: Hirudinae).

Evolutionary bioinformatics online·2026

Same journal

Computational Optimization of CRISPR-Cas13a sgRNAs Targeting the SARS-CoV-2 Spike Gene for SHERLOCK-Based Diagnostics.

Evolutionary bioinformatics online·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 18, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Cloud computing for comparative genomics with windows azure platform.

Insik Kim¹, Jae-Yoon Jung, Todd F Deluca

¹Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA. ; School of Electrical and Computer Engineering, Ulsan National Institute of Technology, Ulsan, Korea.

Evolutionary Bioinformatics Online

|October 4, 2012

Summary

This summary is machine-generated.

Cloud computing offers a cost-effective solution for analyzing large genomic datasets. This study details using Microsoft Azure and compares its costs to Amazon Web Services for genomic analysis.

Keywords:

Roundup Windows Azure cloud computing comparative genomics computational genomics genomics ortholog orthologs

More Related Videos

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

Related Experiment Videos

Last Updated: May 18, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

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

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Increasing genome data volume necessitates scalable computational resources.
Traditional cluster systems face challenges in handling growing computational demands.
Cloud computing presents a viable, cost-effective alternative for genomic analysis.

Purpose of the Study:

To introduce the Microsoft Azure platform for genomic data analysis.
To provide detailed execution steps for utilizing Azure in bioinformatics workflows.
To compare the cost-effectiveness of Microsoft Azure against Amazon Web Services for these tasks.

Main Methods:

Deployment of genomic analysis pipelines on Microsoft Azure.
Step-by-step guide for Azure platform implementation.
Comparative cost analysis between Azure and Amazon Web Services.

Main Results:

Successful implementation of genomic analysis on Microsoft Azure.
Detailed cost breakdown and comparison between the two cloud platforms.
Identification of Azure as a cost-effective option for large-scale genomic computations.

Conclusions:

Microsoft Azure provides a practical and cost-efficient cloud solution for genomic data analysis.
Cloud platforms like Azure are essential for managing the computational challenges in modern genomics.
Cost-performance analysis supports the adoption of cloud services for bioinformatics research.