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Related Experiment Videos

Arkas: Rapid reproducible RNAseq analysis.

Anthony R Colombo1, Timothy J Triche1, Giridharan Ramsingh1

  • 1Jane Anne Nohl Division of Division of Hematology and Center for the Study of Blood Diseases, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA.

F1000Research
|September 20, 2017
PubMed
Summary

Arkas cloud pipelines streamline RNA sequencing analysis using Kallisto for faster transcript quantification and differential gene expression. These tools simplify complex bioinformatics workflows on the BaseSpace platform.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequencing (RNA-seq) experiments generate vast amounts of data requiring efficient analysis.
  • Transcript quantification is a critical step in RNA-seq for understanding gene expression.
  • Existing workflows can be computationally intensive and time-consuming.

Purpose of the Study:

  • To introduce cloud-scale RNA-seq pipelines, Arkas-Quantification and Arkas-Analysis, for streamlined transcript quantification and downstream analysis.
  • To leverage Illumina's BaseSpace platform for scalable and efficient bioinformatics computations.
  • To facilitate the integration of Kallisto pseudoaligner into a user-friendly cloud-based workflow.

Main Methods:

  • Deployment of Kallisto pseudoaligner within the Arkas-Quantification pipeline for parallel cloud computations.
Keywords:
RNAseqautomationcloud computingsequencingtranscriptome

Related Experiment Videos

  • Integration with the BaseSpace Sequence Read Archive (SRA) Import application for seamless data ingestion and conversion.
  • Development of Arkas-Analysis for annotating Kallisto results, calculating differential gene expression, and performing gene-set enrichment analysis (REACTOME pathways).

    • Main Results:

    • Arkas pipelines expedite Kallisto preparatory routines, improving the efficiency of RNA-seq data analysis.
    • Reliable calculation of differential gene expression and gene-set enrichment is achieved.
    • The pipelines support ENSEMBL transcriptomes and facilitate end-to-end RNA-seq analysis from raw data import to final analysis.

    Conclusions:

    • The Arkas cloud pipelines offer a scalable and efficient solution for RNA-seq data analysis, enhancing the utility of tools like Kallisto.
    • Integration with BaseSpace and SRA Import simplifies data management and analysis workflows.
    • These pipelines empower researchers to perform robust transcript quantification, differential expression, and pathway analysis in a cloud environment.