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Microbial Bioinformatics and Computational Biology

NTS uses a standardized operating procedure workflow based on Microbiome Helper, which allows for demultiplexing and classification using the Qiime 1.6 platform and bioinformatic filtering of the sequence data.

It removes low-quality reads and denoised to exclude sequences with read lengths of less than 100 bp.  De novo operational taxonomic units are then clustered using the Uclust algorithm. Classification at the species level is then referenced using the UNITE 5.8S database, and taxa are assigned using the nBlast method with a 90% confidence cutoff.

Abundance profiles for the bacteriome and mycobiome are generated and imported into the Partek Discover Suite for principal component analysis. The statistical programming language R can be used to analyze diversity and correlation analyses and Kruskal-Wallis (nonparametric) analysis of variance using abundance data. Species diversity and richness is analyzed through the Shannon diversity index at all taxonomic levels using the R package vegan. Groupwise comparisons are conducted with SPSS, and a P value of <0.05 are considered statistically significant.

NTS has bioinformatic expertise to assist with interpretation of analyses of Next Generation Sequencing microbiome data and inter-relationships of these data sets, along with other integrated data sets.

The NTS bioinformatics group interfaces with the Integrated Microbiome Core bioinformatics team to provide a quorum of staff scientists focused on the integration of micro- and myco-biome data with other collaborative disciplines.

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