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SAMSA2: a standalone metatranscriptome analysis pipeline.

Samuel T Westreich1, Michelle L Treiber1,2,3, David A Mills2,3

  • 1Genome Center, University of California, Davis, California, USA.

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|May 23, 2018
PubMed
Summary
This summary is machine-generated.

SAMSA2 is a new pipeline for analyzing microbial gene expression from large RNA-seq datasets. This tool enhances speed and flexibility for complex metatranscriptomic data analysis on supercomputing clusters.

Keywords:
AnnotationBacteriaBioinformaticsClusterFunctionsGALAXYMetagenomicsMetatranscriptomeMetatranscriptomicsMicrobiomeOpen accessPipelineRNA-seqSAMSASoftwareTool

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Complex microbial communities are crucial in biological research.
  • Metatranscriptomics enables quantification of microbial gene expression in environmental samples using high-throughput sequencing.
  • Metatranscriptomic analyses are computationally demanding due to large datasets and extensive sequence comparisons.

Purpose of the Study:

  • To introduce SAMSA2, an upgraded and standalone pipeline for metatranscriptome analysis.
  • To improve the speed, flexibility, and reproducibility of metatranscriptomic data processing.
  • To provide a user-friendly tool for analyzing large RNA-seq datasets in a supercomputing environment.

Main Methods:

  • SAMSA2 is a redesigned pipeline based on the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA).
  • It utilizes the DIAMOND aligner for increased speed.
  • Employs local databases for enhanced flexibility and reproducibility, and is designed for standalone use on supercomputing clusters.

Main Results:

  • SAMSA2 demonstrates increased speed compared to previous versions.
  • The pipeline offers greater flexibility through the use of customizable local databases.
  • It provides simplified output for direct examination or further analysis.

Conclusions:

  • SAMSA2 is a rapid and efficient pipeline for processing large metatranscriptomic RNA-seq datasets.
  • The tool is suitable for supercomputing cluster environments.
  • SAMSA2 offers adaptable reference databases to meet diverse experimental requirements.