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Controversy surrounding bacterial phylogenies has become one of the most important challenges for microbial ecology. Comparative analyses with nucleotide databases and phylogenetic reconstruction of the amplified 16S rRNA genes from DGGE (Denaturing Gradient Gel Electrophoresis) excised bands have been used by several researchers for the identification of organisms in complex samples. Here, we individually analyzed DGGE-excised 16S rRNA gene bands from 10 certified bacterial strains of different species, and demonstrated that this kind of approach can deliver erroneous outcomes to researchers, besides causing/emphasizing errors in public databases., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Progress in microbial ecology is confounded by problems when evaluating results from different sequencing methodologies. Contrary to existing expectations, here we demonstrate that the same biological conclusion is reached using different NGS technologies when stringent sequence quality filtering and accurate clustering algorithms are applied., (Copyright © 2014 Elsevier B.V. All rights reserved.)

The analysis of amplified and sequenced 16S rRNA genes has become the most important single approach for microbial diversity studies. The new sequencing technologies allow for sequencing thousands of reads in a single run and a cost-effective option is split into a single run across many samples. However for this type of investigation the key question that needs to be answered is how many samples can be sequenced without biasing the results due to lack of sequence representativeness? In this work we demonstrated that the level of sequencing effort used for analyzing soil microbial communities biases the results and determines the most effective type of analysis for small and large datasets. Many simulations were performed with four independent pyrosequencing-generated 16S rRNA gene libraries from different environments. The analysis performed here illustrates the lack of resolution of OTU-based approaches for datasets with low sequence coverage. This analysis should be performed with at least 90% of sequence coverage. Diversity index values increase with sample size making normalization of the number of sequences in all samples crucial. An important finding of this study was the advantage of phylogenetic approaches for examining microbial communities with low sequence coverage. However, if the environments being compared were closely related, a deeper sequencing would be necessary to detect the variation in the microbial composition., (Copyright © 2011 Elsevier B.V. All rights reserved.)

High-throughput DNA sequencing can identify organisms and describe population structures in many environmental and clinical samples. Current technologies generate millions of reads in a single run, requiring extensive computational strategies to organize, analyze and interpret those sequences. A series of bioinformatics tools for high-throughput sequencing analysis, including pre-processing, clustering, database matching and classification, have been compiled into a pipeline called PANGEA. The PANGEA pipeline was written in Perl and can be run on Mac OSX, Windows or Linux. With PANGEA, sequences obtained directly from the sequencer can be processed quickly to provide the files needed for sequence identification by BLAST and for comparison of microbial communities. Two different sets of bacterial 16S rRNA sequences were used to show the efficiency of this workflow. The first set of 16S rRNA sequences is derived from various soils from Hawaii Volcanoes National Park. The second set is derived from stool samples collected from diabetes-resistant and diabetes-prone rats. The workflow described here allows the investigator to quickly assess libraries of sequences on personal computers with customized databases. PANGEA is provided for users as individual scripts for each step in the process or as a single script where all processes, except the chi(2) step, are joined into one program called the 'backbone'.