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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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Updated: May 13, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

GEnomes Management Application (GEM.app): a new software tool for large-scale collaborative genome analysis.

Michael A Gonzalez¹, Rafael F Acosta Lebrigio, Derek Van Booven

¹Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA.

|March 7, 2013

Summary

This summary is machine-generated.

A new software tool, GEM.app, helps manage and analyze large genomic datasets for identifying genetic causes of disease. It empowers non-bioinformaticians, accelerating gene discovery for Mendelian and complex disorders.

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Area of Science:

Genomics
Bioinformatics
Medical Genetics

Background:

Rapid identification of novel genes for Mendelian and complex disorders presents challenges in managing large genomic datasets.
Smaller labs and research teams face difficulties in direct analysis and interpretation of variant data.
Data sharing among global research teams is crucial for increasing gene discovery opportunities.

Purpose of the Study:

To develop a user-friendly software tool, GEM.app, for annotating, managing, visualizing, and analyzing large genomic datasets.
To address the challenges of data management and analysis for non-bioinformaticians in genomic research.
To facilitate the identification of genetic causes for human diseases by making next-generation sequencing data more accessible.

Main Methods:

Development of the GEnomes Management Application (GEM.app) software.
Inclusion of approximately 1,600 whole exomes from 50 phenotypes, contributed by 40 principal investigators across 15 countries.
Implementation of user-friendly analysis features with powerful filtering options (single family, cross-family/phenotype, nested filtering, segregation analysis).

Main Results:

GEM.app provides efficient management and analysis of large-scale genomic data.
The software enables non-bioinformaticians to perform direct, hands-on analysis of variant data.
Analysis across approximately 1,200 exomes is achieved within 4 seconds, demonstrating system speed.

Conclusions:

GEM.app enhances the ability to identify genetic causes of human diseases.
The tool democratizes access to next-generation sequencing data analysis for a wider research community.
Facilitating collaborative data analysis through GEM.app is expected to accelerate genetic discovery.