General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

Murovec, Boštjan; Deutsch, Leon; Stres, Blaž

doi:10.3390/metabo11060336

Cited by 2 publications

(1 citation statement)

References 73 publications

(129 reference statements)

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“…Two predominant transformation methods applied to deal with uneven library sizes in sequencing microbiome data are relative abundance (Statnikov et al, 2013;Ning and Beiko, 2015;Wu et al, 2018Wu et al, , 2021Bogart et al, 2019;Gupta et al, 2019;Lo and Marculescu, 2019;Vangay et al, 2019;Yachida et al, 2019;Fernández-Edreira et al, 2021;Lloréns-Rico et al, 2021), and rarefaction (Stämmler et al, 2016;Weiss et al, 2017;Baksi et al, 2018), used to solve the problem of different sequencing depths (Murovec et al, 2021).…”

Section: Normalization Methodsmentioning

confidence: 99%

Overview of data preprocessing for machine learning applications in human microbiome research

Ibrahimi,

Lopes,

Dhamo

et al. 2023

Front. Microbiol.

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Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

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Section: Normalization Methodsmentioning

confidence: 99%

Overview of data preprocessing for machine learning applications in human microbiome research

Ibrahimi,

Lopes,

Dhamo

et al. 2023

Front. Microbiol.

Self Cite

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MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information

Murovec,

Deutsch,

Osredkar

et al. 2024

Front. Microbiol.

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In this study, we present MetaBakery (http://metabakery.fe.uni-lj.si), an integrated application designed as a framework for synergistically executing the bioBakery workflow and associated utilities. MetaBakery streamlines the processing of any number of paired or unpaired fastq files, or a mixture of both, with optional compression (gzip, zip, bzip2, xz, or mixed) within a single run. MetaBakery uses programs such as KneadData (https://github.com/bioBakery/kneaddata), MetaPhlAn, HUMAnN and StrainPhlAn as well as integrated utilities and extends the original functionality of bioBakery. In particular, it includes MelonnPan for the prediction of metabolites and Mothur for calculation of microbial alpha diversity. Written in Python 3 and C++ the whole pipeline was encapsulated as Singularity container for efficient execution on various computing infrastructures, including large High-Performance Computing clusters. MetaBakery facilitates crash recovery, efficient re-execution upon parameter changes, and processing of large data sets through subset handling and is offered in three editions with bioBakery ingredients versions 4, 3 and 2 as versatile, transparent and well documented within the MetaBakery Users’ Manual (http://metabakery.fe.uni-lj.si/metabakery_manual.pdf). It provides automatic handling of command line parameters, file formats and comprehensive hierarchical storage of output to simplify navigation and debugging. MetaBakery filters out potential human contamination and excludes samples with low read counts. It calculates estimates of alpha diversity and represents a comprehensive and augmented re-implementation of the bioBakery workflow. The robustness and flexibility of the system enables efficient exploration of changing parameters and input datasets, increasing its utility for microbiome analysis. Furthermore, we have shown that the MetaBakery tool can be used in modern biostatistical and machine learning approaches including large-scale microbiome studies.

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General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

Cited by 2 publications

References 73 publications

Overview of data preprocessing for machine learning applications in human microbiome research

Overview of data preprocessing for machine learning applications in human microbiome research

MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information

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