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.

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 OptiClust, an Improved Method for Assigning Amplicon-Based Sequence Data to Operational Taxonomic Units

mSphere ◽  
2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Sarah L. Westcott ◽  
Patrick D. Schloss
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Significant Advance ◽  
Rrna Gene ◽  
Gene Sequences ◽  
16S Rrna Gene Sequences ◽  
Operational Taxonomic Units ◽  
Taxonomic Groups

ABSTRACT Assignment 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 10 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 data set. 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. IMPORTANCE The analysis of microbial communities from diverse environments using 16S rRNA gene sequencing has expanded our knowledge of the biogeography of microorganisms. An important step in this analysis is the assignment of sequences into taxonomic groups based on their similarity to sequences in a database or based on their similarity to each other, irrespective of a database. In this study, we present a new algorithm for the latter approach. The algorithm, OptiClust, seeks to optimize a metric of assignment quality by shuffling sequences between taxonomic groups. We found that OptiClust produces more robust assignments and does so in a rapid and memory-efficient manner. This advance will allow for a more robust analysis of microbial communities and the factors that shape them. Podcast: A podcast concerning this article is available.

scholarly journals De novo clustering methods out-perform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units

2015 ◽  
Author(s):  
Sarah L Westcott ◽  
Patrick Schloss
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
De Novo ◽  
Rrna Gene ◽  
Clustering Methods ◽  
Gene Sequences ◽  
16S Rrna Gene Sequences ◽  
Reference Methods ◽  
Reference Sequences

Background. 16S rRNA gene sequences are routinely assigned to operational taxonomic units (OTUs) that are then used to analyze complex microbial communities. A number of methods have been employed to carry out the assignment of 16S rRNA gene sequences to OTUs leading to confusion over which method is optimal. A recent study suggested that a clustering method should be selected based on its ability to generate stable OTU assignments that do not change as additional sequences are added to the dataset. In contrast, we contend that the quality of the OTU assignments, the ability of the method to properly represent the distances between the sequences, is more important.Methods. Our analysis implemented six de novo clustering algorithms including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. Using two previously published datasets we used the Matthew’s Correlation Coefficient (MCC) to assess the stability and quality of OTU assignments.Results. The stability of OTU assignments did not reflect the quality of the assignments. Depending on the dataset being analyzed, the average linkage and the distance and abundance-based greedy clustering methods generated OTUs that were more likely to represent the actual distances between sequences than the open and closed-reference methods. We also demonstrated that for the greedy algorithms VSEARCH produced assignments that were comparable to those produced by USEARCH making VSEARCH a viable free and open source alternative to USEARCH. Further interrogation of the reference-based methods indicated that when USEARCH or VSEARCH were used to identify the closest reference, the OTU assignments were sensitive to the order of the reference sequences because the reference sequences can be identical over the region being considered. More troubling was the observation that while both USEARCH and VSEARCH have a high level of sensitivity to detect reference sequences, the specificity of those matches was poor relative to the true best match.Discussion. Our analysis calls into question the quality and stability of OTU assignments generated by the open and closed-reference methods as implemented in current version of QIIME. This study demonstrates that de novo methods are the optimal method of assigning sequences into OTUs and that the quality of these assignments needs to be assessed for multiple methods to identify the optimal clustering method for a particular dataset.

scholarly journals DAIRYdb: a manually curated reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Marco Meola ◽  
Etienne Rifa ◽  
Noam Shani ◽  
Céline Delbès ◽  
Hélène Berthoud ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Dairy Products ◽  
Reference Database ◽  
Rrna Gene ◽  
Gene Sequences ◽  
16S Rrna Gene Sequences

scholarly journals Assessing and Improving Methods Used in Operational Taxonomic Unit-Based Approaches for 16S rRNA Gene Sequence Analysis

2011 ◽  
Vol 77 (10) ◽  
pp. 3219-3226 ◽  
Author(s):  
Patrick D. Schloss ◽  
Sarah L. Westcott
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Operational Taxonomic Unit ◽  
Rrna Gene ◽  
Operational Taxonomic Units ◽  
Gene Sequence Analysis ◽  
Technical Advances ◽  
Heuristic Clustering

ABSTRACTIn spite of technical advances that have provided increases in orders of magnitude in sequencing coverage, microbial ecologists still grapple with how to interpret the genetic diversity represented by the 16S rRNA gene. Two widely used approaches put sequences into bins based on either their similarity to reference sequences (i.e., phylotyping) or their similarity to other sequences in the community (i.e., operational taxonomic units [OTUs]). In the present study, we investigate three issues related to the interpretation and implementation of OTU-based methods. First, we confirm the conventional wisdom that it is impossible to create an accurate distance-based threshold for defining taxonomic levels and instead advocate for a consensus-based method of classifying OTUs. Second, using a taxonomic-independent approach, we show that the average neighbor clustering algorithm produces more robust OTUs than other hierarchical and heuristic clustering algorithms. Third, we demonstrate several steps to reduce the computational burden of forming OTUs without sacrificing the robustness of the OTU assignment. Finally, by blending these solutions, we propose a new heuristic that has a minimal effect on the robustness of OTUs and significantly reduces the necessary time and memory requirements. The ability to quickly and accurately assign sequences to OTUs and then obtain taxonomic information for those OTUs will greatly improve OTU-based analyses and overcome many of the challenges encountered with phylotype-based methods.

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 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.

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