Microbiome Taxonomic Profiling (MTP) – Basic Concept
What is Microbiome Taxonomic Profiling (MTP)?
One of the major goals of microbiome analyses is to obtain the taxonomic profile of a sample. The most widely used and cost-effective method is to sequence PCR amplicons of a phylogenetic marker gene. For Bacteria and Archaea, the 16S rRNA gene is generally chosen, whereas the ITS gene is used for fungal taxonomic profiling. In EzBioCloud, 16S-based Microbiome Taxonomic Profiling is a cloud app that allows users to generate taxonomic profiles from NGS data and easily group and compare the profiles from different samples.
How does this profiling work?
The below is the general procedure of EzBioCloud’s 16S-based MTP app:
- NGS raw data (as FASTQ or FASTA format) are uploaded to www.ezbiocloud.net. Our MTP pipeline will automatically process your data which are converted to a data unit called an MTP. An MTP represents single metagenomic or microbiome sample. In addition, data from public sources including the Human Microbiome Project and Short Read Archive (SRA) have been processed in advance, so they can be grouped and compared with your own MTPs. Each MTP contains information about run QC such as read length and number reads matched. You also get alpha-diversity statistics along with taxonomic hierarchy and composition which can be explored interactively with the visualizations in EzBioCloud 16S-based MTP app.
- Your own MTPs or those from the public domain database can then be grouped into MTP sets for comparisons. The best way to group samples is to use metadata tags [Learn more].
- Two or more MTP sets are then compared for beta-diversity analytics or biomarker discovery, in what we call secondary analysis. For example, you may find differentially present bacterial species between the 30 healthy and 35 obese human subjects. This task would take <10 seconds using EzBioCloud 16S-based MTP app. Of course, you can change the statistical algorithm and parameters, then run again instantly and interactively.
The EzBioCloud team / Last edited on Feb. 8, 2019