Dataset Information

Some considerations for analyzing biodiversity using integrative metagenomics and gene networks.

ABSTRACT:

Background

Improving knowledge of biodiversity will benefit conservation biology, enhance bioremediation studies, and could lead to new medical treatments. However there is no standard approach to estimate and to compare the diversity of different environments, or to study its past, and possibly, future evolution.

Presentation of the hypothesis

We argue that there are two conditions for significant progress in the identification and quantification of biodiversity. First, integrative metagenomic studies - aiming at the simultaneous examination (or even better at the integration) of observations about the elements, functions and evolutionary processes captured by the massive sequencing of multiple markers - should be preferred over DNA barcoding projects and over metagenomic projects based on a single marker. Second, such metagenomic data should be studied with novel inclusive network-based approaches, designed to draw inferences both on the many units and on the many processes present in the environments.

Testing the hypothesis

We reached these conclusions through a comparison of the theoretical foundations of two molecular approaches seeking to assess biodiversity: metagenomics (mostly used on prokaryotes and protists) and DNA barcoding (mostly used on multicellular eukaryotes), and by pragmatic considerations of the issues caused by the 'species problem' in biodiversity studies.

Implications of the hypothesis

Evolutionary gene networks reduce the risk of producing biodiversity estimates with limited explanatory power, biased either by unequal rates of LGT, or difficult to interpret due to (practical) problems caused by type I and type II grey zones. Moreover, these networks would easily accommodate additional (meta)transcriptomic and (meta)proteomic data.

SUBMITTER: Bittner L

PROVIDER: S-EPMC2921367 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Some considerations for analyzing biodiversity using integrative metagenomics and gene networks.

Bittner Lucie L Halary Sébastien S Payri Claude C Cruaud Corinne C de Reviers Bruno B Lopez Philippe P Bapteste Eric E

Biology direct 20100730

<h4>Background</h4>Improving knowledge of biodiversity will benefit conservation biology, enhance bioremediation studies, and could lead to new medical treatments. However there is no standard approach to estimate and to compare the diversity of different environments, or to study its past, and possibly, future evolution.<h4>Presentation of the hypothesis</h4>We argue that there are two conditions for significant progress in the identification and quantification of biodiversity. First, integrati ...[more]

PMID: 20673351

Similar Datasets

Project description:Xiaoqu, one of three traditional jiuqu in China, is a saccharifying and fermenting agent used in Xiaoqu jiu brewing, with different ingredient compositions and preparation techniques used in various regions. The yield and quality of Xiaoqu jiu are significantly affected by the metabolites and microbiota of Xiaoqu; however, the associated relationship remains poorly understood. This study aimed to analyze this relationship in three typical traditional Xiaoqu from the Guizhou province in China. The non-volatile metabolites of Xiaoqu were detected using gas chromatography time-of-flight mass spectrometry, whereas the classification and metabolic potential of the microbiota were investigated using metagenomic sequencing. Results show that Firmicutes, Proteobacteria, and Actinobacteria represent the dominant bacterial phyla, with Lactobacillus, Bacillus, Acinetobacter, Leuconostoc, and Weissella found to be the dominant bacterial genera. Meanwhile, Ascomycota, Mucoromycota, and Basidiomycota are the dominant fungal phyla with Aspergillus, Saccharomyces, Pichia, Rhizopus, and Phycomyces being the predominant fungal genera. Functional annotation of the microbiota revealed a major association with metabolism of carbohydrates, cofactors, and vitamins, as well as amino acids. A total of 39 significantly different metabolites (SDMs) were identified that are involved in 47 metabolic pathways, primarily that of starch and sucrose; glycine, serine, and threonine; glyoxylate and dicarboxylate; pyruvate; as well as biosynthesis of pantothenate and CoA. Further, based on Spearman's correlation analysis, Aspergillus, Saccharomyces, Lactobacillus, Acetobacter, Weissella, Pantoea, Desmospora, and Bacillus are closely correlated with production of physicochemical indexes and SDMs. Moreover, the metabolic network generated for the breakdown of substrates and formation of SDMs in Xiaoqu was found to primarily center on the metabolism of carbohydrates and the tricarboxylic acid cycle. These results provide insights into the functional microorganisms and metabolic patterns present in traditional Guizhou Xiaoqu and might guide researchers in the production of stable and efficient Xiaoqu in the future.

Dataset Information

Some considerations for analyzing biodiversity using integrative metagenomics and gene networks.

Background

Presentation of the hypothesis

Testing the hypothesis

Implications of the hypothesis

Publications

Some considerations for analyzing biodiversity using integrative metagenomics and gene networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets