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TAXAPRO: A Streamlined Pipeline to Analyze Shotgun Metagenomes

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International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023) (AI2SD 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 905))

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Abstract

Background: The ability to promptly sequence whole genomes at a relatively low cost has revolutionized how we study the microbiome. Analyzing whole genome sequencing (WGS) data enables metagenomics at scale. Still, it is a complex process that involves multiple moving parts and can be unintuitive for scientists that do not typically work with this type of data.

Methods: Thus, to help lower the barrier for less computationally inclined individuals, TAXAPRO, a metagenomics pipeline that accurately assembles organelle genomes from WGS, data was developed. TAXAPRO seamlessly combines WGS analysis tools to create a pipeline that automatically processes raw WGS data and presents information on microorganisms’ diversity and relative abundance.

Results: TAXAPRO was evaluated using gut microbiome data from COVID-19 patients. Analysis performed by TAXAPRO demonstrated a relatively high abundance of Clostridia and Bacteroides genera and a low abundance of Proteobacteria were detected in the gut microbiome of patients hospitalized with COVID-19, consistent with the original findings results derived using a different analysis methodology.

Conclusion: Our results provide evidence that the TAXAPRO workflow dispenses quick and automated microorganism diversity and relative abundance information without the hassle of manually performing the analysis.

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Acknowledgments

This research was supported through cloud computational resources provided by the National Institutes of Health Office of Data Science Strategy (NIH ODSS). This research was also supported through computational resources of HPC-MARWAN (www.marwan.ma/hpc) provided by the National Center for Scientific and Technical Research (CNRST), Rabat, Morocco. Hassan Ghazal is a US NIH grant recipient through the H3abionet/H3africa consortium U24HG006941. The authors thank Brigit Shea Sullivan and Alicia Lillich from the NIH Library Editing Service for manuscript editing assistance.

Author information

Authors and Affiliations

  1. Laboratory of Genomics, Bioinformatics and Digital Health School of Medicine, Mohammed VI University of Health Sciences, Casablanca, Morocco

    Sofia Sehli, Zainab El Ouafi & Hassan Ghazal

  2. Lab of Genomics and Bioinformatics and Digital Health, Mohammed VI Center for Research and Innovation, Rabat, Rabat, Morocco

    Sofia Sehli, Zainab El Ouafi & Hassan Ghazal

  3. University of Minnesota, Minneapolis, 55455, MN, United States

    Casey Eddington

  4. Biology of Human Pathologies Laboratory, Genomics Center of Human Pathologies (Genopath), Department of Biology, Faculty of Sciences, University Mohammed V, Rabat, Morocco

    Soumaya Jbara & Islam El Jaddaoui

  5. Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University, P.O Box 7062, Kampala, Uganda

    Kasambula Arthur Shem

  6. Department of Pharmaceutical Microbiology, Faculty of Pharmacy, University of Ibadan, Ibadan, Oyo State, Nigeria

    Ayorinde O. Afolayan & Olaitan I. Awe

  7. African Society for Bioinformatics and Computational Biology, Cape Town, South Africa

    Olaitan I. Awe

  8. National Institutes of Health, Bethesda, MD, USA

    Allissa Dillman

  9. BioData Sage LLC, Bethesda, MD, 20814, USA

    Allissa Dillman

  10. Scientific Department, National Center for Scientific and Technical Research, Rabat, Morocco

    Hassan Ghazal

  11. Royal Institute of Sports, Royal Institute for Managerial Training in Youth and Sport, Department of Sports Sciences, Laboratory of Sports Sciences and Performance Optimization, Salé, 10102, Morocco

    Hassan Ghazal

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  1. Sofia Sehli
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Contributions

SS: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision; ZEO: Visualization, Investigation, Writing – Original Draft Preparation; CE: Data Curation, Formal Analysis, Methodology, Software, Writing – Original Draft Preparation; AIA: Data Curation, Formal Analysis; KAS: Formal Analysis, Investigation; IEJ: Conceptualization; S. Jbara: Formal Analysis, Methodology; A. Afolayan: Investigation, Resources; OIA: Funding Acquisition, Formal Analysis, Investigation, Project Administration, Resources, Supervision, Review & Editing; A. Dillman: Funding Acquisition, Resources, Supervision; H. Ghazal: Conceptualization, Review & Editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hassan Ghazal .

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Editors and Affiliations

  1. Computer Sciences Department, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  2. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Automatics and Applied Software at the Faculty of Engineering, University "Aurel Vlaicu" Arad and Romanian Academy of Scientists, Arad, Romania

    Valentina Emilia Balas

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Sehli, S. et al. (2024). TAXAPRO: A Streamlined Pipeline to Analyze Shotgun Metagenomes. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 905. Springer, Cham. https://doi.org/10.1007/978-3-031-52385-4_8

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