Second Genome offers expert analysis and data reports to support your project and decision priorities. Custom bioinformatics and data analysis are also available:

• Microbial Signatures/Indicators
• Diversity Tracking
• Classification

Data analysis is tailored to the study design, experimental question and statistical power of the study. Generalized portions of the analysis include data normalization against quantitative standards and tables are generated containing the relative abundance of each detected organism across the experiment. Inter-sample distances are calculated (Bray, Euclidean) and samples are clustered and ordinated (UPGMA, PCoA, NMDS). Plots are created to overlay researcher's metadata and appropriate legends are created. Classification of unknown samples against knowns is performed if applicable. Additionally barplots are rendered to visualize taxonomic fluctuations between samples.

Microbial Signatures/Indicators

PhyloChip analysis is the ideal platform for identifying communities of coexisting microbes that are present in sample related by a criterion such as location, timepoint, or treatment. These microbial community signatures or indicators can be used to discriminate disease or environmental states (e.g. healthy vs. diseased, uncontaminated vs. contaminated) or to derive information about the biological systems underlying certain states (e.g. oil degrading or anaerobic bacterial communities). PhyloChip analysis not only employs the most comprehensive survey of bacteria today, the quantitative performance, repatability and reproducibility of the assay makes it possible to uncover subtle , but highly important community changes.

Figure 1 is a selection of typical data plots and tables delivered for a study aimed at identifying the microbial changes associated with two different states BM and QV. Figure 1a illustrates the top bacterial taxa that discriminate the two states. Those bacterial taxa with the largest difference between the two states are listed toward the top of the table. In this study, Rhodobacterales (OTU 15243*) is more abundant in group BM samples than OV samples.

Figure 1b illustrates the summarized intensity value for individual bacterial taxa on a sample basis to cross check the validity of the group level results in Figure 1a. Figure 1c summarizes the known biological information about the signature/indicator bacteria, often including where the bacteria was first isolated and associated known biological processes.

Diversity Tracking

The PhyloChip assay enables the survey of bacterial diversity present in different environments, at different time-points, or under varying conditions. Studies have shown that <1% of bacterial species in a given environment are culturable and culture-independent methods like the PhyloChip assay have revealed abundant microbial diversity in unexpected areas, such as cleanrooms. Microbial diversity data on the phyla and family level can help you gauge the effectiveness of different anti-microbial treatments and understand large and small scale fluctuations that may be occuring in your biological or environmental system.

Figure 2 illustrates microbial diversity changes between different individual samples at the phylum and family level. Each horizontal bar represents a single sample and the size of each color block is proportional to the number of taxa detected in a given phyla or family.

Classification

Microbial community structure can be greatly impacted by a number of variables. Measurements on individual microbial members in samples can help stratify samples into groups with similar microbial community compositions. This can be used to reveal the factors influencing a key microbial community shift (ie. age, water source) or to assign "unknown" samples to a specific class (ie. matching a specific contamination source).

Figure 3a and 3b illustrate different methods of hierarchical clustering that reveal underlying similarity between different samples. Figure 3a (left) is a matrix indicating the level of similarity between samples based on non- biological environmental data such as depth and salinity. Figure 3b illustrates another hierarchical clustering method where each row represents a sample and the similarity between samples is represented by tree branch length. Figure 3c illustrates an ordination plot where samples with greater similarity are grouped more closely together.