16s rRNA Data Analysis / Microbial Diversity Analysis

16s rRNA Short read libraries target variable V3 and V4 regions of 16s rRNA genes. Although, 16s rRNA sequencing is an amplicon sequencing technique, usually the environment or clinical samples are as clean and need expert hands to process and amplify 16s rRNA genes. Our 16s rRNA sequencing service makes sure that your precious samples are managed in the best possible manner to generate quality sequencing data. Our 16s rRNA sequencing service can help achieve species level sensitivity for biodiversity analysis of bacterial population.

16s rRNA sequencing data analysis requires deep understanding of various steps to be able to generate precise information about biodiversity or phylogeny. A typical data analysis overview for 16s rRNA data analysis pipeline is represented here.

As with any NGS data analysis workflow, it is essentially to preprocess reads before actual analysis. Data preprocessing includes steps to determine quality and coverage required for analysis. Read trimming for adaptors and quality bases is a standard procedure applied to NGS dataset. 16s rRNA sequencing involves PCR amplification step which in general introduces chimeric reads that need to be removed before any OTU analysis could be performed else several spurious OTU could be reported. A paired end stitching may be required if the sequencing read lengths are longer then size of 16s rRNA. Our 16s rRNA data analysis pipeline determine overlap across paired end reads and selects higher quality bases to improve quality of stitched read.

Microbiome Analysis

Typical chimera seen during Illumina and Pacbio library preparation. Microbial phylogenetic profiling with the Pacific Biosciences sequencing platform. Microbiome, 2013.

16s rRNA taxonomic classification step starts with clustering of reads based on sequence similarity into OTUs with a representative sequence for each OTU. In general, reads with more than 97% sequence similarity are assumed to belong to same bacterial species. Information about bacterial diversity and required cluster resolution can be used to fine tune sequence identity parameters to be used for clustering and OTU generation. Once OTU are established, the representative sequences are compared to known reference databases to generate taxonomical identities. Currently, there are 3 popular databases – Green Genes, Silva and Ribosomal Database (RDP) with each of them having their set of strengths and weakness. There are number of tools for taxonomic classification, we have developed a robust 16s rRNA analysis and classification pipeline for phylogenetic assignment using above mentioned reference databases.

With OTU assignment in place, it is key to understand the evolutionary relationship across the specifies present in sample(s). All OTU sequences are multiple aligned against reference database to determine evolutionary distance across them and generate a phylogenetic tree that represented evolutionary distance in form of branches and nodes. Sequence or OTUs that are closer in a phylogenetic tree are more related to each other than those that are future apart or belong to other nodes in the tree.

16s rRNA Gene Tag Sequences

Diversity analysis can be performed at two levels – within a sample and across samples.  Diversity measure within a sample is called Alpha diversity while when comparing diversity across samples is called Beta diversity. Alpha diversity provides a gauge of intra sample distribution of species. Majority of published alpha diversity studies indicate towards present of few dominant species in a sample. A high sequencing coverage depth is required to be able to capture those minority species present in each sample. Our team of experts run rarefaction analysis to determine actual diversity within a sample. Samples with high sequence depth show higher species diversity. There are many alpha diversity measures like Shanon index, observed species and Chao1 that can be generated upon request.

Illumina Sequencing Of 16s rRNA

Diversity analysis can be performed at two levels – within a sample and across samples.  Diversity measure within a sample is called Alpha diversity while when comparing diversity across samples is called Beta diversity. Alpha diversity provides a gauge of intra sample distribution of species. Majority of published alpha diversity studies indicate towards present of few dominant species in a sample. A high sequencing coverage depth is required to be able to capture those minority species present in each sample. Our team of experts run rarefaction analysis to determine actual diversity within a sample. Samples with high sequence depth show higher species diversity. There are many alpha diversity measures like Shanon index, observed species and Chao1 that can be generated upon request.

Microbiome Sequencing

For beta diversity analysis, our pipeline compares samples using the phylogenetic information like Unifrac distance generated in steps above. Samples can be compared either in a pairwise or all-vs-all manner to generate beta diversity matrix. Beta diversity is represented in several ways by means of network diagrams, phylogenetic trees or graphs.
UPGMA clustering of Porites surface microbiomes based on the Morisita-Horn beta diversity of V4 region of 16S rRNA genes revealed two distinct clusters of samples.