scholarly journals A Meta-Analysis of the Bacterial and Archaeal Diversity Observed in Wetland Soils

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaofei Lv ◽  
Junbao Yu ◽  
Yuqin Fu ◽  
Bin Ma ◽  
Fanzhu Qu ◽  
...  
Keyword(s):  
16S Rrna ◽  
Meta Analysis ◽  
Wetland Soils ◽  
Rrna Gene ◽  
Archaeal Diversity ◽  
16S Rrna Gene Sequences ◽  
Rarefaction Analysis ◽  
Bacterial Phyla ◽  
Operational Taxonomic Units ◽  
Soils And Sediments

This study examined the bacterial and archaeal diversity from a worldwide range of wetlands soils and sediments using a meta-analysis approach. All available 16S rRNA gene sequences recovered from wetlands in public databases were retrieved. In November 2012, a total of 12677 bacterial and 1747 archaeal sequences were collected in GenBank. All the bacterial sequences were assigned into 6383 operational taxonomic units (OTUs 0.03), representing 31 known bacterial phyla, predominant with Proteobacteria (2791 OTUs), Bacteroidetes (868 OTUs), Acidobacteria (731 OTUs), Firmicutes (540 OTUs), and Actinobacteria (418 OTUs). The genusFlavobacterium(11.6% of bacterial sequences) was the dominate bacteria in wetlands, followed by Gp1,Nitrosospira, andNitrosomonas. Archaeal sequences were assigned to 521 OTUs from phyla Euryarchaeota and Crenarchaeota. The dominating archaeal genera wereFervidicoccusandMethanosaeta. Rarefaction analysis indicated that approximately 40% of bacterial and 83% of archaeal diversity in wetland soils and sediments have been presented. Our results should be significant for well-understanding the microbial diversity involved in worldwide wetlands.

scholarly journals Cellulolytic Bacteria in the Foregut of the Dromedary Camel (Camelus dromedarius)

2012 ◽  
Vol 78 (24) ◽  
pp. 8836-8839 ◽  
Author(s):  
Anjas A. Samsudin ◽  
André-Denis G. Wright ◽  
Rafat Al Jassim
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Species Level ◽  
Neutral Detergent Fiber ◽  
Dromedary Camel ◽  
Rrna Gene ◽  
Cellulolytic Bacteria ◽  
16S Rrna Gene Sequences ◽  
Dromedary Camels ◽  
Operational Taxonomic Units

ABSTRACTForegut digesta from five feral dromedary camels were inoculated into three different enrichment media: cotton thread, filter paper, and neutral detergent fiber. A total of 283 16S rRNA gene sequences were assigned to 33 operational taxonomic units by using 99% species-level identity. LIBSHUFF revealed significant differences in the community composition across all three libraries.

scholarly journals OptiClust: Improved method for assigning amplicon-based sequence data to operational taxonomic units

2016 ◽  
Author(s):  
Sarah L. Westcott ◽  
Patrick D. Schloss
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Sequence Data ◽  
Significant Advance ◽  
Rrna Gene ◽  
Gene Sequences ◽  
16S Rrna Gene Sequences ◽  
Operational Taxonomic Units ◽  
Computational Bottleneck

AbstractAssignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) is a computational bottleneck in the process of analyzing microbial communities. Although this has been an active area of research, it has been difficult to overcome the time and memory demands while improving the quality of the OTU assignments. Here we developed a new OTU assignment algorithm that iteratively reassigns sequences to new OTUs to optimize the Matthews correlation coefficient (MCC), a measure of the quality of OTU assignments. To assess the new algorithm, OptiClust, we compared it to ten other algorithms using 16S rRNA gene sequences from two simulated and four natural communities. Using the OptiClust algorithm, the MCC values averaged 15.2 and 16.5% higher than the OTUs generated when we used the average neighbor and distance-based greedy clustering with VSEARCH, respectively. Furthermore, on average, OptiClust was 94.6-times faster than the average neighbor algorithm and just as fast as distance-based greedy clustering with VSEARCH. An empirical analysis of the efficiency of the algorithms showed that the time and memory required to perform the algorithm scaled quadratically with the number of unique sequences in the dataset. The significant improvement in the quality of the OTU assignments over previously existing methods will significantly enhance downstream analysis by limiting the splitting of similar sequences into separate OTUs and merging of dissimilar sequences into the same OTU. The development of the OptiClust algorithm represents a significant advance that is likely to have numerous other applications.

scholarly journals Application of database-independent approach to assess the quality of OTU picking methods

2016 ◽  
Author(s):  
Patrick D. Schloss
Keyword(s):  
16S Rrna ◽  
Clustering Algorithms ◽  
Reference Database ◽  
Rrna Gene ◽  
Robust Method ◽  
16S Rrna Gene Sequences ◽  
Bacterial Taxonomy ◽  
Operational Taxonomic Units ◽  
Matthew’S Correlation Coefficient

AbstractAssigning 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database. I have applied the Matthew’s correlation coefficient to assess the ability of 15 reference-independent and ‐dependent clustering algorithms to assign sequences to OTUs. This metric quantifies the ability of an algorithm to reflect the relationships between sequences without the use of a reference and can be applied to any dataset or method. The most consistently robust method was the average neighbor algorithm; however, for some datasets other algorithms matched its performance.

scholarly journals Application of a Database-Independent Approach To Assess the Quality of Operational Taxonomic Unit Picking Methods

mSystems ◽  
2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Patrick D. Schloss
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Clustering Algorithms ◽  
Reference Database ◽  
Rrna Gene ◽  
Gene Sequences ◽  
16S Rrna Gene Sequences ◽  
Bacterial Taxonomy ◽  
Data Set ◽  
Operational Taxonomic Units

ABSTRACT Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database. Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database. I have applied the Matthews correlation coefficient to assess the ability of 15 reference-independent and -dependent clustering algorithms to assign sequences to OTUs. This metric quantifies the ability of an algorithm to reflect the relationships between sequences without the use of a reference and can be applied to any data set or method. The most consistently robust method was the average neighbor algorithm; however, for some data sets, other algorithms matched its performance.

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