Dataset Information

Evaluating techniques for metagenome annotation using simulated sequence data.

ABSTRACT: The advent of next-generation sequencing has allowed huge amounts of DNA sequence data to be produced, advancing the capabilities of microbial ecosystem studies. The current challenge is to identify from which microorganisms and genes the DNA originated. Several tools and databases are available for annotating DNA sequences. The tools, databases and parameters used can have a significant impact on the results: naïve choice of these factors can result in a false representation of community composition and function. We use a simulated metagenome to show how different parameters affect annotation accuracy by evaluating the sequence annotation performances of MEGAN, MG-RAST, One Codex and Megablast. This simulated metagenome allowed the recovery of known organism and function abundances to be quantitatively evaluated, which is not possible for environmental metagenomes. The performance of each program and database varied, e.g. One Codex correctly annotated many sequences at the genus level, whereas MG-RAST RefSeq produced many false positive annotations. This effect decreased as the taxonomic level investigated increased. Selecting more stringent parameters decreases the annotation sensitivity, but increases precision. Ultimately, there is a trade-off between taxonomic resolution and annotation accuracy. These results should be considered when annotating metagenomes and interpreting results from previous studies.

SUBMITTER: Randle-Boggis RJ

PROVIDER: S-EPMC4892715 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Evaluating techniques for metagenome annotation using simulated sequence data.

Randle-Boggis Richard J RJ Helgason Thorunn T Sapp Melanie M Ashton Peter D PD

FEMS microbiology ecology 20160508 7

The advent of next-generation sequencing has allowed huge amounts of DNA sequence data to be produced, advancing the capabilities of microbial ecosystem studies. The current challenge is to identify from which microorganisms and genes the DNA originated. Several tools and databases are available for annotating DNA sequences. The tools, databases and parameters used can have a significant impact on the results: naïve choice of these factors can result in a false representation of community compos ...[more]

PMID: 27162180

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Project description:Non-sequence gene data (images, literature, etc.) can be found in many different public databases. Access to these data is mostly by text based methods using gene names; however, gene annotation is neither complete, nor fully systematic between organisms, and is also not generally stable over time. This provides some challenges for text based access, especially for cross-species searches. We propose a method for non-sequence data retrieval based on sequence similarity, which removes dependence on annotation and text searches. This work was motivated by the need to provide better access to large numbers of in situ images, and the observation that such image data were usually associated with a specific gene sequence. Sequence similarity searches are found in existing gene oriented databases, but mostly give indirect access to non-sequence data via navigational links.Three applications were built to explore the proposed method: accessing image data, literature and gene names. Searches are initiated with the sequence of the user's gene of interest, which is searched against a database of sequences associated with the target data. The matching (non-sequence) target data are returned directly to the user's browser, organised by sequence similarity. The method worked well for the intended application in image data management. Comparison with text based searches of the image data set showed the accuracy of the method. Applied to literature searches it facilitated retrieval of mostly high relevance references. Applied to gene name data it provided a useful analysis of name variation of related genes within and between species.This method makes a powerful and useful addition to existing methods for searching gene data based on text retrieval or curated gene lists. In particular the method facilitates cross-species comparisons, and enables the handling of novel or otherwise un-annotated genes. Applications using the method are quick and easy to build, and the data require little maintenance. This approach largely circumvents the need for annotation, which can be a major obstacle to the development of genomic scale data resources.

Dataset Information

Evaluating techniques for metagenome annotation using simulated sequence data.

Publications

Evaluating techniques for metagenome annotation using simulated sequence data.

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