RNA Bioinformatics

Fosso Bruno, Marzano Marinella, Monica Santamaria

Ernesto Picardi

In recent years, thanks to the essential support provided by the Next-Generation Sequencing (NGS) technologies, Metagenomics is enabling the direct access to the taxonomic and functional composition of mixed microbial communities living in any environmental niche, without the prerequisite to isolate or culture the single organisms. This approach has already been successfully applied for the analysis of many habitats, such as water or soil natural environments, also characterized by extreme physical and chemical conditions, food supply chains, and animal organisms, including humans. A shotgun sequencing approach can lead to investigate both organisms and genes diversity. Anyway, if the purpose is limited to explore the taxonomic complexity, an amplicon-based approach, based on PCR-targeted sequencing of selected genetic species markers, commonly named "meta-barcodes", is desirable. Among the genomic regions most widely used for the discrimination of bacterial organisms, in some cases up to the species level, some hypervariable domains of the gene coding for the 16S rRNA occupy a prominent place.The amplification of a certain meta-barcode from a microbial community through the use of PCR primers able to work in the entire considered taxonomic group is the first task after the extraction of the total DNA. Generally, this step is followed by the high-throughput sequencing of the resulting amplicons libraries by means of a selected NGS platform. Finally, the interpretation of the huge amount of produced data requires appropriate bioinformatics tools and know-how in addition to efficient computational resources.Here a computational methodology suitable for the taxonomic characterization of 454 meta-barcode sequences is described in detail. In particular, a dataset covering the V1-V3 region belonging to the bacterial 16S rRNA coding gene and produced in the Human Microbiome Project (HMP) from a palatine tonsils sample is analyzed. The proposed exercise includes the basic steps to manage raw sequencing data, remove amplification and pyrosequencing errors, and finally map sequences on the taxonomy.