The sequencing of the 16S subunit of the bacterial rRNA gene is extensively used as marker gene for the identification and quantification of the individual components of microbial communities. This approach, known as metataxonomics, is applied across a wide range of scientific disciplines, from plant and animal science, to human biology and medicine, to ecology. Several bioinformatic pipelines of analysis have been developed to process this type of data; however, this is a relatively recent field, and it is not yet clear how different pipelines compare. In this work, we are comparing five bioinformatic pipelines: QIIME (quantitative insights into microbial ecology) 1 & 2; MICCA (microbial community analysis); VSEARCH and MOTHUR. We have fixed the set of parameters to process 16S sequencing data (e.g. reads filtering criteria), to make the pipelines as comparable as possible, and investigated the use of different versions of the SILVA and RDP reference microbial databases. These different pipelines have been applied to mock communities (3 samples) for which the bacterial composition was known, and to 64 cow milk samples from the EU-PRIMA project MILKQUA (Milk Quality along the Dairy Chain for a Safe and Sustainable Milk; MILKQUA-H2020-PRIMA 2018--Section 2, MIUR -Italian Ministry of Education, University and Research- Decree n. 593, 26/07/2016). The pipelines are being run up to the production of the OTU/ASV table (table of abundances of bacterial taxa per sample). Results are currently in progress and will be compared in terms of: i) for the mock community data, how well they predict the actual composition of bacterial communities; ii) for milk samples data, how much the detected bacterial composition changes as a function of the pipeline+database combination.
Comparison of Bioinformatic Pipelines and Microbial Databases for the Analysis of 16S rRNA-Gene Sequencing Data
Filippo Biscarini;
2023
Abstract
The sequencing of the 16S subunit of the bacterial rRNA gene is extensively used as marker gene for the identification and quantification of the individual components of microbial communities. This approach, known as metataxonomics, is applied across a wide range of scientific disciplines, from plant and animal science, to human biology and medicine, to ecology. Several bioinformatic pipelines of analysis have been developed to process this type of data; however, this is a relatively recent field, and it is not yet clear how different pipelines compare. In this work, we are comparing five bioinformatic pipelines: QIIME (quantitative insights into microbial ecology) 1 & 2; MICCA (microbial community analysis); VSEARCH and MOTHUR. We have fixed the set of parameters to process 16S sequencing data (e.g. reads filtering criteria), to make the pipelines as comparable as possible, and investigated the use of different versions of the SILVA and RDP reference microbial databases. These different pipelines have been applied to mock communities (3 samples) for which the bacterial composition was known, and to 64 cow milk samples from the EU-PRIMA project MILKQUA (Milk Quality along the Dairy Chain for a Safe and Sustainable Milk; MILKQUA-H2020-PRIMA 2018--Section 2, MIUR -Italian Ministry of Education, University and Research- Decree n. 593, 26/07/2016). The pipelines are being run up to the production of the OTU/ASV table (table of abundances of bacterial taxa per sample). Results are currently in progress and will be compared in terms of: i) for the mock community data, how well they predict the actual composition of bacterial communities; ii) for milk samples data, how much the detected bacterial composition changes as a function of the pipeline+database combination.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
