Clann: investigating phylogenetic information through supertree analyses

Summary: Clann has been developed in order to provide methods of investigating phylogenetic information through the application of supertrees. Availability: Clann has been precompiled for Linux, Apple Macintosh and Windows operating systems and is available from http://bioinf.may.ie/software/clann. Source code is available on request from the authors. Supplementary information: Clann has been written in the C programming language. Source code is available on request

Clann: investigating phylogenetic information through supertree analyses

Summary: Clann has been developed in order to provide methods of investigating phylogenetic information through the application of supertrees. Availability: Clann has been precompiled for Linux, Apple Macintosh and Windows operating systems and is available from http://bioinf.may.ie/software/clann. Source code is available on request from the authors. Supplementary information: Clann has been written in the C programming language. Source code is available on request

MGkit: Metagenomic Framework For The Study Of Microbial Communities

<p><strong>Introduction</strong></p> <p>While metagenomics has been used extensively to study microbial communities from a taxonomic and functional perspective, little has been done to address how the species in a microbiome are adapted to and maintain specific roles in dynamic environments like the rumen.</p> <p><strong>Rationale</strong></p> <p>To address this issue we have developed a framework for the robust analysis of metagenomic data that includes fully automated analysis from next-generation sequencing (NGS) reads to assembly, gene predicition and taxonomic identification. Furthermore we imple- ment approaches to estimate SNP diversity in metagenomic samples and carry out statistical tests to identify genes where sequence diver- sity exists.</p

Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

International audienceThe rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiom

Metagenomic analysis of the Rumen microbiome reveals functional isoforms drive niche differentiation for nutrient acquisition and use

<p>Metagenomics has provided insights into the species composi- tion and function of the rumen microbial community and revealed that many species seem to share the same genes for acquiring and utilising nutrients. This questions whether niche specialisation between rumen microbes exists and if so, how it is maintained.</p> <p>One possibility is that niche specialisation is not driven by gene presence or absence, but by the diversity of functional isoforms, where we would expect to see isoform diversity to be related to niche specialisation.</p