SAMSA2: a standalone metatranscriptome analysis pipeline

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

doi:10.1186/s12859-018-2189-z

Cited by 118 publications

(88 citation statements)

References 25 publications

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“…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. 2019

Preprint

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BackgroundShotgun 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 postsequencing 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. ResultsUsing 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. ConclusionsShotgun 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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Section: Metagenomic Sequence Analysismentioning

confidence: 99%

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

Treiber

Taft

Korf

et al. 2019

Preprint

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“…Several workflows for metagenomic analyses have been published, including MetaWRAP(v1.2.1) 15 , Anvi’o 16 , SAMSA2 17 , Humann 18 , or MG-Rast 19 . Unlike those, MUFFIN allows for a hybrid metagenomic approach combining the strengths of short and long reads.…”

Section: Introductionmentioning

confidence: 99%

Metagenomics workflow for hybrid assembly, differential coverage binning, transcriptomics and pathway analysis (MUFFIN)

Damme

Hölzer

Viehweger

et al. 2020

Preprint

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Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used by providing RNA-Seq data (optionally) for quantification and annotation. We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and if provided de novo transcript annotations across the metagenomic sample and for each bin.Author SummaryRVD did the development and design of MUFFIN and wrote the first draft; BM and EBR did the critical reading and correction of the manuscript; MH did the critical reading of the manuscript and the general adjustments for the metagenomic workflow; AV did the critical reading of the manuscript and adjustments for the taxonomic classifications. CB supervised the project, did the workflow design, helped with the implementation, and revised the manuscript.

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“…The application of metatranscriptomics is less common than other DNA-based genomics techniques and thus most analysis pipelines are built ad hoc [17]. An assembly-free approach is used in a few pipelines/workflows such as COMAN [18], Metatrans [9], and SAMSA2 [19], while an assembly-based approach is used in a few such as IMP [7]. The lack of thorough benchmarking studies and standardized workflows in metatranscriptomics has made it a more challenging task to analyse the typically big datasets produced.…”

Section: Discussionmentioning

confidence: 99%

To assemble or not to resemble – A validated Comparative Metatranscriptomics Workflow (CoMW)

Anwar

Lanzén

Bang‐Andreasen

et al. 2019

Preprint

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BackgroundMetatranscriptomics has been used widely for investigation and quantification of microbial communities’ activity in response to external stimuli. By assessing the genes expressed, metatranscriptomics provide an understanding of the interactions between different major functional guilds and the environment. Here, we present de-novo assembly-based Comparative Metatranscriptomics Workflow (CoMW) implemented in a modular, reproducible structure, significantly improving the annotation and quantification of metatranscriptomes. Metatranscriptomics typically utilize short sequence reads, which can either be directly aligned to external reference databases (“assembly-free approach”) or first assembled into contigs before alignment (“assembly-based approach”). We also compare CoMW (assembly-based implementation) with assembly-free alternative workflow, using simulated and real-world metatranscriptomes from Arctic and Temperate terrestrial environments. We evaluate their accuracy in precision and recall using generic and specialized hierarchical protein databases.ResultsCoMW provided significantly fewer false positives resulting in more precise identification and quantification of functional genes in metatranscriptomes. Using the comprehensive database M5nr, the assembly-based approach identified genes with only 0.6% false positives at thresholds ranging from inclusive to stringent compared to the assembly-free approach yielding up to 15% false positives. Using specialized databases (Carbohydrate Active-enzyme and Nitrogen Cycle), the assembly-based approach identified and quantified genes with 3-5x less false positives. We also evaluated the impact of both approaches on real-world datasets.ConclusionsWe present an open source de-novo assembly-based Comparative Metatranscriptomics Workflow (CoMW). Our benchmarking findings support the argument of assembling short reads into contigs before alignment to a reference database, since this provides higher precision and minimizes false positives.

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SAMSA2: a standalone metatranscriptome analysis pipeline

Cited by 118 publications

References 25 publications

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

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

Metagenomics workflow for hybrid assembly, differential coverage binning, transcriptomics and pathway analysis (MUFFIN)

To assemble or not to resemble – A validated Comparative Metatranscriptomics Workflow (CoMW)

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