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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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Related Experiment Video

Updated: Mar 7, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
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ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.

Sergio Gonzalez1, Bernardo Clavijo2, Máximo Rivarola3,4

  • 1Instituto de Biotecnología, Centro Investigación en Ciencias Veterinarias y Agronómicas (CICVyA) INTA, Hurlingham, Buenos Aires, Argentina. gonzalez.sergio@inta.gob.ar.

BMC Bioinformatics
|February 23, 2017
PubMed
Summary
This summary is machine-generated.

ATGC transcriptomics is a new web application simplifying RNA sequencing (RNA-seq) data analysis for non-model species. It offers easy data exploration and integration for researchers without extensive bioinformatics expertise.

Keywords:
Data integrationDe novo transcriptomicsOntology storageWeb application

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Massively parallelized RNA sequencing (RNA-seq) is crucial for studying non-model organisms.
  • RNA-seq facilitates novel transcript detection, genetic variation analysis, and differential gene expression studies.
  • Managing and analyzing large, complex RNA-seq datasets poses challenges, particularly for small research groups.

Purpose of the Study:

  • To develop a user-friendly application for analyzing next-generation sequencing (NGS) transcriptomic data.
  • To simplify data exploration, visualization, and integration for RNA-seq results.
  • To provide a scalable storage and data integration solution for researchers, especially those with limited bioinformatics resources.

Main Methods:

  • Development of a web-based application named ATGC transcriptomics.
  • Implementation of a flexible and adaptable interface.
  • Utilization of an ontology-driven database for managing NGS transcriptomic analysis results.

Main Results:

  • ATGC transcriptomics simplifies the exploration, visualization, and integration of RNA-seq data.
  • The application enhances comprehension of complex transcriptomic results.
  • It offers a scalable storage solution and straightforward data integration.

Conclusions:

  • ATGC transcriptomics empowers non-expert computer users and small research groups.
  • The software provides accessible database administration and management.
  • It is freely available under the GNU public license.