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Defining reference sequences for Nocardia species by similarity and clustering analyses of 16S rRNA gene sequence data.

ABSTRACT: BACKGROUND: The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. METHODS: A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. RESULTS: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578. CONCLUSION: The identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra-species variability.

SUBMITTER: Helal M

PROVIDER: S-EPMC3110597 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Defining reference sequences for Nocardia species by similarity and clustering analyses of 16S rRNA gene sequence data.

Helal Manal M Kong Fanrong F Chen Sharon C A SC Bain Michael M Christen Richard R Sintchenko Vitali V

PloS one 20110608 6

<h4>Background</h4>The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene w ...[more]

PMID: 21687706

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Project description: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.

Project description:Advances in high-throughput sequencing have increased the availability of microbiome sequencing data that can be exploited to characterize microbiome community structure in situ. We explore using word and sentence embedding approaches for nucleotide sequences since they may be a suitable numerical representation for downstream machine learning applications (especially deep learning). This work involves first encoding ("embedding") each sequence into a dense, low-dimensional, numeric vector space. Here, we use Skip-Gram word2vec to embed k-mers, obtained from 16S rRNA amplicon surveys, and then leverage an existing sentence embedding technique to embed all sequences belonging to specific body sites or samples. We demonstrate that these representations are meaningful, and hence the embedding space can be exploited as a form of feature extraction for exploratory analysis. We show that sequence embeddings preserve relevant information about the sequencing data such as k-mer context, sequence taxonomy, and sample class. Specifically, the sequence embedding space resolved differences among phyla, as well as differences among genera within the same family. Distances between sequence embeddings had similar qualities to distances between alignment identities, and embedding multiple sequences can be thought of as generating a consensus sequence. In addition, embeddings are versatile features that can be used for many downstream tasks, such as taxonomic and sample classification. Using sample embeddings for body site classification resulted in negligible performance loss compared to using OTU abundance data, and clustering embeddings yielded high fidelity species clusters. Lastly, the k-mer embedding space captured distinct k-mer profiles that mapped to specific regions of the 16S rRNA gene and corresponded with particular body sites. Together, our results show that embedding sequences results in meaningful representations that can be used for exploratory analyses or for downstream machine learning applications that require numeric data. Moreover, because the embeddings are trained in an unsupervised manner, unlabeled data can be embedded and used to bolster supervised machine learning tasks.

Project description:Next-generation sequencing (NGS)-based 16S rRNA sequencing by jointly using the PCR amplification and NGS technology is a cost-effective technique, which has been successfully used to study the phylogeny and taxonomy of samples from complex microbiomes or environments. Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is often the first step for many downstream analyses. Heuristic clustering is one of the most widely employed approaches for generating OTUs. However, most heuristic OTUs clustering methods just select one single seed sequence to represent each cluster, resulting in their outcomes suffer from either overestimation of OTUs number or sensitivity to sequencing errors. In this paper, we present a novel dynamic multi-seeds clustering method (namely DMSC) to pick OTUs. DMSC first heuristically generates clusters according to the distance threshold. When the size of a cluster reaches the pre-defined minimum size, then DMSC selects the multi-core sequences (MCS) as the seeds that are defined as the n-core sequences (n ≥ 3), in which the distance between any two sequences is less than the distance threshold. A new sequence is assigned to the corresponding cluster depending on the average distance to MCS and the distance standard deviation within the MCS. If a new sequence is added to the cluster, dynamically update the MCS until no sequence is merged into the cluster. The new method DMSC was tested on several simulated and real-life sequence datasets and also compared with the traditional heuristic methods such as CD-HIT, UCLUST, and DBH. Experimental results in terms of the inferred OTUs number, normalized mutual information (NMI) and Matthew correlation coefficient (MCC) metrics demonstrate that DMSC can produce higher quality clusters with low memory usage and reduce OTU overestimation. Additionally, DMSC is also robust to the sequencing errors. The DMSC software can be freely downloaded from https://github.com/NWPU-903PR/DMSC.

Dataset Information

Defining reference sequences for Nocardia species by similarity and clustering analyses of 16S rRNA gene sequence data.

Publications

Defining reference sequences for Nocardia species by similarity and clustering analyses of 16S rRNA gene sequence data.

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