1,397 research outputs found
Recovering complete and draft population genomes from metagenome datasets.
Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution
Blueprint: descrição da complexidade da regulação metabólica através da reconstrução de modelos metabólicos e regulatórios integrados
Tese de doutoramento em Biomedical EngineeringUm modelo metabólico consegue prever o fenótipo de um organismo. No entanto, estes modelos
podem obter previsões incorretas, pois alguns processos metabólicos são controlados por mecanismos
reguladores. Assim, várias metodologias foram desenvolvidas para melhorar os modelos metabólicos
através da integração de redes regulatórias. Todavia, a reconstrução de modelos regulatórios e metabólicos à escala genómica para diversos organismos apresenta diversos desafios.
Neste trabalho, propõe-se o desenvolvimento de diversas ferramentas para a reconstrução e análise
de modelos metabólicos e regulatórios à escala genómica. Em primeiro lugar, descreve-se o Biological
networks constraint-based In Silico Optimization (BioISO), uma nova ferramenta para auxiliar a curação
manual de modelos metabólicos. O BioISO usa um algoritmo de relação recursiva para orientar as previsões de fenótipo. Assim, esta ferramenta pode reduzir o número de artefatos em modelos metabólicos,
diminuindo a possibilidade de obter erros durante a fase de curação.
Na segunda parte deste trabalho, desenvolveu-se um repositório de redes regulatórias para procariontes que permite suportar a sua integração em modelos metabólicos. O Prokaryotic Transcriptional
Regulatory Network Database (ProTReND) inclui diversas ferramentas para extrair e processar informação regulatória de recursos externos. Esta ferramenta contém um sistema de integração de dados que
converte dados dispersos de regulação em redes regulatórias integradas. Além disso, o ProTReND dispõe
de uma aplicação que permite o acesso total aos dados regulatórios.
Finalmente, desenvolveu-se uma ferramenta computacional no MEWpy para simular e analisar modelos regulatórios e metabólicos. Esta ferramenta permite ler um modelo metabólico e/ou rede regulatória,
em diversos formatos. Esta estrutura consegue construir um modelo regulatório e metabólico integrado
usando as interações regulatórias e as ligações entre genes e proteínas codificadas no modelo metabólico e na rede regulatória. Além disso, esta estrutura suporta vários métodos de previsão de fenótipo
implementados especificamente para a análise de modelos regulatórios-metabólicos.Genome-Scale Metabolic (GEM) models can predict the phenotypic behavior of organisms. However,
these models can lead to incorrect predictions, as certain metabolic processes are controlled by regulatory
mechanisms. Accordingly, many methodologies have been developed to extend the reconstruction and
analysis of GEM models via the integration of Transcriptional Regulatory Network (TRN)s. Nevertheless,
the perspective of reconstructing integrated genome-scale regulatory and metabolic models for diverse
prokaryotes is still an open challenge.
In this work, we propose several tools to assist the reconstruction and analysis of regulatory and
metabolic models. We start by describing BioISO, a novel tool to assist the manual curation of GEM
models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and
guide debugging of in silico phenotype predictions. Hence, this tool can reduce the number of artifacts in
GEM models, decreasing the burdens of model refinement and curation.
A state-of-the-art repository of TRNs for prokaryotes was implemented to support the reconstruction
and integration of TRNs into GEM models. The ProTReND repository comprehends several tools to extract
and process regulatory information available in several resources. More importantly, this repository contains a data integration system to unify the regulatory data into standardized TRNs at the genome scale.
In addition, ProTReND contains a web application with full access to the regulatory data.
Finally, we have developed a new modeling framework to define, simulate and analyze GEnome-scale
Regulatory and Metabolic (GERM) models in MEWpy. The GERM model framework can read a GEM
model, as well as a TRN from different file formats. This framework assembles a GERM model using
the regulatory interactions and Genes-Proteins-Reactions (GPR) rules encoded into the GEM model and
TRN. In addition, this modeling framework supports several methods of phenotype prediction designed
for regulatory-metabolic models.I would like to thank Fundação para a Ciência e Tecnologia for the Ph.D. studentship I was awarded
with (SFRH/BD/139198/2018)
A new Bayesian piecewise linear regression model for dynamic network reconstruction
Background: Linear regression models are important tools for learning regulatory networks from gene expression time series. A conventional assumption for non-homogeneous regulatory processes on a short time scale is that the network structure stays constant across time, while the network parameters are time-dependent. The objective is then to learn the network structure along with changepoints that divide the time series into time segments. An uncoupled model learns the parameters separately for each segment, while a coupled model enforces the parameters of any segment to stay similar to those of the previous segment. In this paper, we propose a new consensus model that infers for each individual time segment whether it is coupled to (or uncoupled from) the previous segment. Results: The results show that the new consensus model is superior to the uncoupled and the coupled model, as well as superior to a recently proposed generalized coupled model. Conclusions: The newly proposed model has the uncoupled and the coupled model as limiting cases, and it is able to infer the best trade-off between them from the data
Prospects for multi-omics in the microbial ecology of water engineering
Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions – including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.BT/Industriele Microbiologi
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