Bioinformatics analysis of complex DNA-sequencing data to assess microbial community in diverse industrial and clinical samples.

Bacteria plays a vital role in industry, from the production of traditional foods such as yoghurt, cheese, and vinegar; to the production substances such as drugs and vitamins. They also affect the human health, being involved in the immune responses and food digestion and contributing to the pathogenicity of various diseases. Several important aspects of bacteria in various environments are poorly understood, making the identification and monitoring microbial communities of utter importance to the industry and patient care. Recent developments in new high-throughput sequencing technologies have revolutionized molecular biology, including the study of microorganisms without the need for culturing them in the lab, an approach often referred to as metagenomics. Such metagenomics applications allow the simultaneous high-throughput analysis of genetic material of most of the microbes present in a given sample, without the need for culturing the bacteria first. Although this approach has nowadays been adopted in many project, it is far from straightforward necessitating various bioinformatics optimization.

In this work, we aimed at optimizing and standardizing the analysis of 16S metagenomics with a final aim to develop a pipeline to starts from the raw sequencing reads and deliver their microbial composition. Therefore, different tools were developed to deal with chimera (Mysara, Saeys, et al., 2015) and sequencing errors (Mysara, Leys, et al., 2015; Mysara et al., 2016), each of them found to outperform the other existing state-of-the-art tools. Additionally, a new method was introduced to bring closer correspondence between the number of microorganisms detected and the actual diversity within the samples (Mysara, Vandamme, et al., 2017). A one stop-shop software, named OCToPUS, assembles these various algorithms, thereby leading to a highly accurate assessment of microbial diversity starting from the raw sequencing reads (Mysara, Njima, et al., 2017).

These tailored algorithms have already been successfully applied to assess complex microbial communities in a wide range of environments such as deep subsurface geological clay formations (HADES, Boom clay, Belgium; and Mont Terri, Opalinus clay Swiss, Moors et al. 2013), the human gut microbiome after radiotherapy treatment, and cooling water circuits of nuclear reactors (Props et al., 2016), as such revealing microbial diversity at unseen depth.

Authors

Mohamed Mysara
Natalie Leys
Pieter Monsieurs

Organisations

Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Mol, Belgium

Presenting author

Mohamed Mysara, Scientific staff (Bioinformatician), Belgian Nuclear Research Centre (SCK-CEN)
mohamed.mysara@sckcen.be
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