Computational protocol: Qualitative Analysis of Microbial Dynamics during Anaerobic Digestion of Microalgal Biomass in a UASB Reactor

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Protocol publication

[…] Analysis of 16S rRNA gene data was performed using a MiSeq SOP pipeline, described by Kozich et al. [] and implemented on MOTHUR software []. Analysis included (1) quality trimming of the reads, (2) chimera check with UCHIME algorithm, (3) extraction of unique reads and alignment to the classification databases, (4) actual classification using Bayesian classifier, and (5) OTU identification. Sequences generated from PCR with both types of primers, universal bacterial 338F and 785R and methanogen-specific MLr-MLf, were processed in a similar pipeline, with the only difference regarding database used for the sequences alignment and classification. For sequences generated with 338F and 785R primer set, SILVA V4 database (http://www.arb-silva.de/) was used for the classification and alignment. For sequences generated with mcrA gene specific primer set, a database for classification and alignment was manually created from pooling the mcrA sequences from FunGene database (http://fungene.cme.msu.edu/). The algorithm for analysis of mcrA sequences in MOTHUR software was previously described []. To build a phylogenetic tree of the classified mcrA sequences, MEGA 6.06 package was used, incorporating Tamura-Nei model with maximum likelihood analysis and 1000 bootstraps.The internal MOTHUR command unifrac.weighted was used to calculate the significance of separate clustering of sequences from the samples taken at different time points of anaerobic digestion. A statistical tool in MOTHUR, HOMOVA, was used to calculate the level of variation among samples depending on the duration of anaerobic digestion. In more detail, algorithm assessed variability of OTU composition at different time points during AD, comparing level of variation for one pair of samples at a time (e.g., difference in variation of OTU composition between initial inoculum and samples taken at the end of AD). Beta-diversity for each sample amplified and sequenced with universal bacterial primer pair was estimated in a comparative heat map, while looking at the relative abundance of each OTU across all samples. Bacterial OTUs of interest were pulled from the classification table with custom Python scripts. Finally, depth of the conducted sequencing effort (rarefaction curve) was calculated using summary.single command with estimation of Good's coverage. […]

Pipeline specifications

Software tools mothur, UCHIME, UniFrac
Applications Phylogenetics, 16S rRNA-seq analysis
Organisms Bacteria, Methanosarcina mazei
Diseases Epilepsies, Partial
Chemicals Methane