Dataset Information

Classifying short genomic fragments from novel lineages using composition and homology.

ABSTRACT:

Background

The assignment of taxonomic attributions to DNA fragments recovered directly from the environment is a vital step in metagenomic data analysis. Assignments can be made using rank-specific classifiers, which assign reads to taxonomic labels from a predetermined level such as named species or strain, or rank-flexible classifiers, which choose an appropriate taxonomic rank for each sequence in a data set. The choice of rank typically depends on the optimal model for a given sequence and on the breadth of taxonomic groups seen in a set of close-to-optimal models. Homology-based (e.g., LCA) and composition-based (e.g., PhyloPythia, TACOA) rank-flexible classifiers have been proposed, but there is at present no hybrid approach that utilizes both homology and composition.

Results

We first develop a hybrid, rank-specific classifier based on BLAST and Naïve Bayes (NB) that has comparable accuracy and a faster running time than the current best approach, PhymmBL. By substituting LCA for BLAST or allowing the inclusion of suboptimal NB models, we obtain a rank-flexible classifier. This hybrid classifier outperforms established rank-flexible approaches on simulated metagenomic fragments of length 200 bp to 1000 bp and is able to assign taxonomic attributions to a subset of sequences with few misclassifications. We then demonstrate the performance of different classifiers on an enhanced biological phosphorous removal metagenome, illustrating the advantages of rank-flexible classifiers when representative genomes are absent from the set of reference genomes. Application to a glacier ice metagenome demonstrates that similar taxonomic profiles are obtained across a set of classifiers which are increasingly conservative in their classification.

Conclusions

Our NB-based classification scheme is faster than the current best composition-based algorithm, Phymm, while providing equally accurate predictions. The rank-flexible variant of NB, which we term ?-NB, is complementary to LCA and can be combined with it to yield conservative prediction sets of very high confidence. The simple parameterization of LCA and ?-NB allows for tuning of the balance between more predictions and increased precision, allowing the user to account for the sensitivity of downstream analyses to misclassified or unclassified sequences.

SUBMITTER: Parks DH

PROVIDER: S-EPMC3173459 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Publications

Classifying short genomic fragments from novel lineages using composition and homology.

Parks Donovan H DH MacDonald Norman J NJ Beiko Robert G RG

BMC bioinformatics 20110809

<h4>Background</h4>The assignment of taxonomic attributions to DNA fragments recovered directly from the environment is a vital step in metagenomic data analysis. Assignments can be made using rank-specific classifiers, which assign reads to taxonomic labels from a predetermined level such as named species or strain, or rank-flexible classifiers, which choose an appropriate taxonomic rank for each sequence in a data set. The choice of rank typically depends on the optimal model for a given seque ...[more]

PMID: 21827705

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Project description:BackgroundClassification is difficult for shotgun metagenomics data from environments such as soils, where the diversity of sequences is high and where reference sequences from close relatives may not exist. Approaches based on sequence-similarity scores must deal with the confounding effects that inheritance and functional pressures exert on the relation between scores and phylogenetic distance, while approaches based on sequence alignment and tree-building are typically limited to a small fraction of gene families. We describe an approach based on finding one or more exact matches between a read and a precomputed set of peptide 10-mers.ResultsAt even the largest phylogenetic distances, thousands of 10-mer peptide exact matches can be found between pairs of bacterial genomes. Genes that share one or more peptide 10-mers typically have high reciprocal BLAST scores. Among a set of 403 representative bacterial genomes, some 20 million 10-mer peptides were found to be shared. We assign each of these peptides as a signature of a particular node in a phylogenetic reference tree based on the RNA polymerase genes. We classify the phylogeny of a genomic fragment (e.g., read) at the most specific node on the reference tree that is consistent with the phylogeny of observed signature peptides it contains. Using both synthetic data from four newly-sequenced soil-bacterium genomes and ten real soil metagenomics data sets, we demonstrate a sensitivity and specificity comparable to that of the MEGAN metagenomics analysis package using BLASTX against the NR database. Phylogenetic and functional similarity metrics applied to real metagenomics data indicates a signal-to-noise ratio of approximately 400 for distinguishing among environments. Our method assigns ~6.6 Gbp/hr on a single CPU, compared with 25 kbp/hr for methods based on BLASTX against the NR database.ConclusionsClassification by exact matching against a precomputed list of signature peptides provides comparable results to existing techniques for reads longer than about 300 bp and does not degrade severely with shorter reads. Orders of magnitude faster than existing methods, the approach is suitable now for inclusion in analysis pipelines and appears to be extensible in several different directions.

Project description:BackgroundMetagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning.ResultsOur novel strategy was extensively evaluated using the leave-one-out cross validation strategy on fragments of variable length (800 bp - 50 Kbp) from 373 completely sequenced genomes. TACOA is able to classify genomic fragments of length 800 bp and 1 Kbp with high accuracy until rank class. For longer fragments > or = 3 Kbp accurate predictions are made at even deeper taxonomic ranks (order and genus). Remarkably, TACOA also produces reliable results when the taxonomic origin of a fragment is not represented in the reference set, thus classifying such fragments to its known broader taxonomic class or simply as "unknown". We compared the classification accuracy of TACOA with the latest intrinsic classifier PhyloPythia using 63 recently published complete genomes. For fragments of length 800 bp and 1 Kbp the overall accuracy of TACOA is higher than that obtained by PhyloPythia at all taxonomic ranks. For all fragment lengths, both methods achieved comparable high specificity results up to rank class and low false negative rates are also obtained.ConclusionAn accurate multi-class taxonomic classifier was developed for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp. The proposed method is transparent, fast, accurate and the reference set can be easily updated as newly sequenced genomes become available. Moreover, the method demonstrated to be competitive when compared to the most current classifier PhyloPythia and has the advantage that it can be locally installed and the reference set can be kept up-to-date.

Dataset Information

Classifying short genomic fragments from novel lineages using composition and homology.

Background

Results

Conclusions

Publications

Classifying short genomic fragments from novel lineages using composition and homology.

Similar Datasets

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