SAMSA2: a standalone metatranscriptome analysis pipeline

Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments gener...

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Bibliographic Details
Published in:BMC bioinformatics 2018-05, Vol.19 (1), p.175-175, Article 175
Main Authors: Westreich, Samuel T, Treiber, Michelle L, Mills, David A, Korf, Ian, Lemay, Danielle G
Format: Article
Language:English
Subjects:
Acids
Analysis
Annotations
Bioinformatics
Clusters
Computational biology
Diamonds
Gene expression
Gene Expression Profiling - methods
Gene sequencing
Genomes
High-Throughput Nucleotide Sequencing - methods
Metagenomics
Metagenomics - methods
Metatranscriptomics
Microbial activity
Microbiota - genetics
Microorganisms
Next-generation sequencing
Open access
Open source software
Pipeline
Proteins
Researchers
Ribonucleic acid
RNA
Sequence Analysis, RNA - methods
Software
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Summary:Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms. SAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution. SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2189-z