SAMSA2: A standalone metatranscriptome analysis pipeline

Westreich, Samuel T.; Treiber, Michelle L.; Mills, David A.; Korf, Ian F; Lemay, Danielle G.

doi:10.1101/195826

Cited by 11 publications

(20 citation statements)

References 23 publications

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“…Metagenomic data were analyzed using Kraken2 and Bracken to identify species-level shifts in community composition (Lu et al, 2017;Wood and Salzberg, 2014). Metatranscriptomic reads were analyzed at the whole-community level with the HMP Unified metabolic access network 2 (HUMAnN2) (Franzosa et al, 2018) and simple annotation of Metatranscriptomes by sequence analysis 2 (SAMSA2) (Westreich et al, 2018) pipelines to identify differentially abundant pathways, subsystems, and transcripts. Lastly, a previously published pipeline (Deng et al, 2018) was utilized to align metatranscriptomic reads to the genomes of individual bacterial species to identify species-specific transcriptional changes among highly abundant members of the microbiota in response to different classes of antibiotics (Figure 1A).…”

Section: Resultsmentioning

confidence: 99%

Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome

et al. 2019

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Section: Resultsmentioning

confidence: 99%

Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome

et al. 2019

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“…RNA-Seq, in principle, enables studies of both the composition of active microbial communities and the biological functions being expressed by the constituent members. While several pipelines for the analysis of metatranscriptome data have been published (34)(35)(36)(37), including evaluation of how the results from such data compare to those of amplicon-or whole-genome shotgun-based methods, these studies primarily focused on bacterial communities or relatively low-complexity species mixes or used the total RNA pool (21,37,38). Other studies have used both RNA-Seq and amplicon sequencing together to describe microbial communities from a taxonomic and functional perspective (18,39).…”

Section: Discussionmentioning

confidence: 99%

Comparative Fungal Community Analyses Using Metatranscriptomics and Internal Transcribed Spacer Amplicon Sequencing from Norway Spruce

et al. 2021

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The health, growth, and fitness of boreal forest trees are impacted and improved by their associated microbiomes. Microbial gene expression and functional activity can be assayed with RNA sequencing (RNA-Seq) data from host samples. In contrast, phylogenetic marker gene amplicon sequencing data are used to assess taxonomic composition and community structure of the microbiome. Few studies have considered how much of this structural and taxonomic information is included in transcriptomic data from matched samples. Here, we described fungal communities using both host-derived RNA-Seq and fungal ITS1 DNA amplicon sequencing to compare the outcomes between the methods. We used a panel of root and needle samples from the coniferous tree species Picea abies (Norway spruce) growing in untreated (nutrient-deficient) and nutrient-enriched plots at the Flakaliden forest research site in boreal northern Sweden. We show that the relationship between samples and alpha and beta diversity indicated by the fungal transcriptome is in agreement with that generated by the ITS data, while also identifying a lack of taxonomic overlap due to limitations imposed by current database coverage. Furthermore, we demonstrate how metatranscriptomics data additionally provide biologically informative functional insights. At the community level, there were changes in starch and sucrose metabolism, biosynthesis of amino acids, and pentose and glucuronate interconversions, while processing of organic macromolecules, including aromatic and heterocyclic compounds, was enriched in transcripts assigned to the genus Cortinarius. IMPORTANCE A deeper understanding of microbial communities associated with plants is revealing their importance for plant health and productivity. RNA extracted from plant field samples represents the host and other organisms present. Typically, gene expression studies focus on the plant component or, in a limited number of studies, expression in one or more associated organisms. However, metatranscriptomic data are rarely used for taxonomic profiling, which is currently performed using amplicon approaches. We created an assembly-based, reproducible, and hardware-agnostic workflow to taxonomically and functionally annotate fungal RNA-Seq data obtained from Norway spruce roots, which we compared to matching ITS amplicon sequencing data. While we identified some limitations and caveats, we show that functional, taxonomic, and compositional insights can all be obtained from RNA-Seq data. These findings highlight the potential of metatranscriptomics to advance our understanding of interaction, response, and effect between host plants and their associated microbial communities.

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“…An argument against using point-and-click computational computational pipelines is the inability to assign appropriate cut-offs for genes of interest [7]. An advantage of creating one's own pipeline or modifying one that is open source, such as SAMSA2 [31], is the ability for users to define e-value thresholds and use customized databases. SAMSA2, although built for metatranscriptomes, also works to assess gene abundances in metagenomes with the omission of the rRNA removal step, which is unnecessary for metagenomes.…”

Section: Discussionmentioning

confidence: 99%

“…The SAMSA2 pipeline [55] was modified for metagenomics analysis with the following steps: 1) reads mapping to the human genome were first removed with BMTagger [23], 2) paired-end reads were merged using PEAR [56], 3) sequence adaptor contamination and low quality bases were removed using Trimmomatic [57] and 4) the quality reads were then annotated against a protein reference database using DIAMOND, a high-throughput squence aligner [10]. DIAMOND is highly sensitive and runs at a speed that is up to 20,000 times faster than BLASTX and up to 2,500 times faster with the "sensitive" option.…”

Section: Metagenomic Sequence Analysismentioning

confidence: 99%

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

Treiber

Taft

Korf

et al. 2020

Preprint

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Background Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX. Results Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene. Conclusions Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.

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SAMSA2: A standalone metatranscriptome analysis pipeline

Cited by 11 publications

References 23 publications

Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome

Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome

Comparative Fungal Community Analyses Using Metatranscriptomics and Internal Transcribed Spacer Amplicon Sequencing from Norway Spruce

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

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