Dataset Information

Uncovering bacterial and functional diversity in macroinvertebrate mitochondrial-metagenomic datasets by differential centrifugation.

ABSTRACT: PCR-free techniques such as meta-mitogenomics (MMG) can recover taxonomic composition of macroinvertebrate communities, but suffer from low efficiency, as >90% of sequencing data is mostly uninformative due to the great abundance of nuclear DNA that cannot be identified with current reference databases. Current MMG studies do not routinely check data for information on macroinvertebrate-associated bacteria and gene functions. However, this could greatly increase the efficiency of MMG studies by revealing yet overlooked diversity within ecosystems and making currently unused data available for ecological studies. By analysing six 'mock' communities, each containing three macroinvertebrate taxa, we tested whether this additional data on bacterial taxa and functional potential of communities can be extracted from MMG datasets. Further, we tested whether differential centrifugation, which is known to greatly increase efficiency of macroinvertebrate MMG studies by enriching for mitochondria, impacts on the inferred bacterial community composition. Our results show that macroinvertebrate MMG datasets contain a high number of mostly endosymbiont bacterial taxa and associated gene functions. Centrifugation reduced both the absolute and relative abundance of highly abundant Gammaproteobacteria, thereby facilitating detection of rare taxa and functions. When analysing both taxa and gene functions, the number of features obtained from the MMG dataset increased 31-fold ('enriched') respectively 234-fold ('not enriched'). We conclude that analysing MMG datasets for bacteria and gene functions greatly increases the amount of information available and facilitates the use of shotgun metagenomic techniques for future studies on biodiversity.

SUBMITTER: Macher JN

PROVIDER: S-EPMC6635389 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Uncovering bacterial and functional diversity in macroinvertebrate mitochondrial-metagenomic datasets by differential centrifugation.

Macher Jan-Niklas JN Speksnijder Arjen A Choo Le Qin LQ van der Hoorn Berry B Renema Willem W

Scientific reports 20190716 1

PCR-free techniques such as meta-mitogenomics (MMG) can recover taxonomic composition of macroinvertebrate communities, but suffer from low efficiency, as >90% of sequencing data is mostly uninformative due to the great abundance of nuclear DNA that cannot be identified with current reference databases. Current MMG studies do not routinely check data for information on macroinvertebrate-associated bacteria and gene functions. However, this could greatly increase the efficiency of MMG studies by ...[more]

PMID: 31312027

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Project description:BackgroundRecent advances in sequencing technologies have resulted in an unprecedented increase in the number of metagenomes that are being sequenced world-wide. Given their volume, functional annotation of metagenomic sequence datasets requires specialized computational tools/techniques. In spite of having high accuracy, existing stand-alone functional annotation tools necessitate end-users to perform compute-intensive homology searches of metagenomic datasets against "multiple" databases prior to functional analysis. Although, web-based functional annotation servers address to some extent the problem of availability of compute resources, uploading and analyzing huge volumes of sequence data on a shared public web-service has its own set of limitations. In this study, we present COGNIZER, a comprehensive stand-alone annotation framework which enables end-users to functionally annotate sequences constituting metagenomic datasets. The COGNIZER framework provides multiple workflow options. A subset of these options employs a novel directed-search strategy which helps in reducing the overall compute requirements for end-users. The COGNIZER framework includes a cross-mapping database that enables end-users to simultaneously derive/infer KEGG, Pfam, GO, and SEED subsystem information from the COG annotations.ResultsValidation experiments performed with real-world metagenomes and metatranscriptomes, generated using diverse sequencing technologies, indicate that the novel directed-search strategy employed in COGNIZER helps in reducing the compute requirements without significant loss in annotation accuracy. A comparison of COGNIZER's results with pre-computed benchmark values indicate the reliability of the cross-mapping database employed in COGNIZER.ConclusionThe COGNIZER framework is capable of comprehensively annotating any metagenomic or metatranscriptomic dataset from varied sequencing platforms in functional terms. Multiple search options in COGNIZER provide end-users the flexibility of choosing a homology search protocol based on available compute resources. The cross-mapping database in COGNIZER is of high utility since it enables end-users to directly infer/derive KEGG, Pfam, GO, and SEED subsystem annotations from COG categorizations. Furthermore, availability of COGNIZER as a stand-alone scalable implementation is expected to make it a valuable annotation tool in the field of metagenomic research.Availability and implementationA Linux implementation of COGNIZER is freely available for download from the following links: http://metagenomics.atc.tcs.com/cognizer, https://metagenomics.atc.tcs.com/function/cognizer.

Dataset Information

Uncovering bacterial and functional diversity in macroinvertebrate mitochondrial-metagenomic datasets by differential centrifugation.

Publications

Uncovering bacterial and functional diversity in macroinvertebrate mitochondrial-metagenomic datasets by differential centrifugation.

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