Microorganisms play central roles in planet Earth’s geochemical cycles, in food production, and in health and disease of humans and livestock. In spite of this, most microbial life forms remain unknown and unnamed, their ecological importance and potential technological applications beyond the realm of speculation. This is due both to the magnitude of microbial diversity and to technological limitations. Of the many advances that have enabled microbiology to reach new depth and breadth in the past decade, one of the most important is affordable high-throughput DNA sequencing. This technology plays a central role in each paper in this thesis. Papers I and II are focused on developing methods to survey microbial diversity based on marker gene amplification and sequencing. In Paper I we proposed a computational strategy to design primers with the highest coverage among a given set of sequences and applied it to drastically improve one of the most commonly used primer pairs for ecological surveys of prokaryotes. In Paper II this strategy was applied to an eukaryotic marker gene. Despite their importance in the food chain, eukaryotic microbes are much more seldom surveyed than bacteria. Paper II aimed at making this domain of life more amenable to high-throughput surveys. In Paper III, the primers designed in papers I and II were applied to water samples collected up to twice weekly from 2011 to 2013 at an offshore station in the Baltic proper, the Linnaeus Microbial Observatory. In addition to tracking microbial communities over these three years, we created predictive models for hundreds of microbial populations, based on their co-occurrence with other populations and environmental factors. In paper IV we explored the entire metagenomic diversity in the Linnaeus Microbial Observatory. We used computational tools developed in our group to construct draft genomes of abundant bacteria and archaea and described their phylogeny, seasonal dynamics and potential physiology. We were also able to establish that, rather than being a mixture of genomes from fresh and saline water, the Baltic Sea plankton community is composed of brackish specialists which diverged from other aquatic microorganisms thousands of years before the formation of the Baltic itself. / <p>QC 20150505</p>

Thesis (PhD)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: The grape and wine ecosystem contains fungi, bacteria and yeasts whose interactions contribute to the final wine product. While the non-Saccharomyces yeasts are dominant in the early stage of alcoholic fermentation, the later stage is always dominated by Saccharomyces cerevisiae. Although their presence in wine fermentation is often short-lived, the non-Saccharomyces yeasts are known to produce an array of extracellular hydrolytic enzymes which facilitate the extraction and release of aroma compounds, but might also play a role in microbial interactions. The present study aimed to investigate the microbial diversity of grape juice and to evaluate the potential of non-Saccharomyces yeasts to produce hydrolytic enzymes and display anti-fungal properties. To capture the microbial diversity, culture-dependent (plating) and –independent (Automated Ribosomal Intergenic Spacer Analysis (ARISA)) techniques were used in parallel. The fungal and bacterial ARISA displayed a wider range of operational taxonomic units (OTUs) in comparison to cultivation-based technique, demonstrating that ARISA is a powerful culture-independent technique applicable to ecological studies in wine. Some of the uncommon yeast isolates derived from our cultivation-based study were subjected to an enzymatic screening process. Hydrolases, such as chitinases, β-1,4-cellulases, β-1,3-1,6-glucanases, β-glucosidases, pectinases and acid proteases were specifically sought. Most of the yeast isolates exhibited chitinase, β-1,4-cellulase as well as β-1,3-1,6-glucanase activities. Only Metschnikowia chrysoperlae exhibited β-glucosidase activity. We also retrieved the partial chitinase gene sequences from M. chrysoperlae, Pichia burtonii, Hyphopichia pseudoburtonii that exhibited chitinase activity. Among the isolates, Pseudozyma fusiformata exhibited a strong antagonistic activity against the wine spoilage yeasts B. bruxellensis AWRI 1499 and B. anomalus IWBT Y105. Furthermore, we showed that the killer phenotype of P. fusiformata cannot be attributed to a viral encoded dsRNA. Finally, two metagenomic approaches were employed in an attempt to explore the indigenous microbiome in a more holistic manner, where we adopted whole metagenome Roche GS-FLX 454-pyrosequencing and construction of a fosmid library. The whole metagenome sequencing revealed a wide range of hydrolytic enzymes that showed homology to enzymes from different fungal and non-Saccharomyces yeast species. Moreover, the metagenomic library screening resulted in the retrieval of 22 chitinase and 11 β-glucosidase positive fosmid clones originating from yeasts. Two clones of interest, BgluFos-G10 and ChiFos-C21, were subjected to next generation sequencing. BgluFos-G10 revealed 2 ORFs exhibiting homology to glycosyl hydrolase family 16 proteins whereas no ORFs encoding chitinase enzymes could be identified in the ChiFos-C21 clone. However, all the potential ORFs identified exhibited homology to a gene cluster from Clavispora lusitaniae ATCC 42720, suggesting that the cloned DNA fragments belonged to a yeast species closely related to C. lusitaniae or members of the family Metschnikowiaceae. Overall, our study identified a variety of novel hydrolytic enzymes. However, retrieving the full gene sequences of these identified enzymes would be the immediate follow-up of our study. Moreover, the hydrolytic and antifungal activities exhibited by the yeast isolate could be of major interest in evaluating their potential as biocontrol agents against grapevine fungal pathogens and subsequently the wine spoilage yeasts. It would be interesting to evaluate as well the potential impact of these enzymes under wine making condition and could be our next step of investigation. / AFRIKAANSE OPSOMMING: Die druif en wyn ekosisteme bevat swamme, bakterië en giste en die interaksies van hierdie organismes dra by tot die finale wyn produk. Die nie-Saccharomyces giste is dominant in die vroeë stadium van die alkoholiese fermentasie, maar die latere fase word altyd gedomineer deur Saccharomyces cerevisiae. Alhoewel hulle teenwoordigheid in wyngistings gewoonlik kortstondig is, is die nie-Saccharomyces giste bekend vir die produksie van ‘n verskeidenheid ekstrasellulêre hidrolitiese ensieme wat die ekstraksie en vrylating van aroma komponente fasiliteer, en ook moontlik ‘n rol kan speel in mikrobiese interaksie. Hierdie studie beoog om die mikrobiese diversiteit van druiwesap te bestudeer en die potensiaal van nie-Saccharomyces giste te evalueer ten opsigte van die produksie van hidrolitiese ensieme, asook die demonstrasie van anti-swam eienskappe. Kweking-afhanklike (uitplating), asook –onafhanklike (Automatiese Ribosomale Intergeniese Spasieerder Analise (ARISA)) tegnieke is in parallel gebruik om die mikrobiese diversiteit te bepaal. Die swam en bakteriële ARISA het ‘n groter verskeidenheid van operasionele taksinomiese eenhede (OTUe) vertoon in vergelyking met die kweking-gebasseerde tegniek en dit demonstreer dat ARISA ‘n kragtige kweking-onafhanklike tegniek is, wat toepasbaar is in ekologiese studies van wyn . Sommige van die skaarser gisisolate, uit ons kweking -gebasseerde studie was vir ensiemaktiwiteite geskandeer. Daar is spesifiek gesoek vir hidrolases soos chitinases,β-1,4-sellulases, β-1,3-1,6-glukunases, β-glukosidases, pektinases en suur proteases. Die meeste gisisolate het chitinase,β-1,4-sellulase asook β-1,3-1,6-glukunase aktiwiteit vertoon. Slegs Metschinikowia chrysoperlae het β-glukosidase aktiwiteit vertoon. Ons het verder die gedeeltelike chitinase geensekwensies van M. chrysoperlae, Pichia burtonii en Hyphopichia pseudoburtonii wat chitinase aktiwiteit vertoon het, bepaal. Een isolaat, Pseudozyma fusiformata, het ‘n sterk antagonistiese aktiwiteit teenoor die wyn bederfgiste, Bretanomyces bruxellensis AWRI 1499 en B. anomalus IWBT Y105 vertoon. Verder het ons gewys dat die killer fenotipe van P. fusiformata nie gekoppel kan word aan’n viraal gekodeerde dsRNA nie. Ten laaste is twee metagenomiese benaderings, naamlik die volledige metagenoom Roche GS-FLX 454-pirovolgordebepaling en konstruksie van ‘n fosmied biblioteek, gebruik om die inheemse mikrobioom op ‘n meer holistiese wyse te bestudeer. Die volgordebepaling van die volledige metagenoom het ‘n wye verskeidenheid hidrolitiese ensieme aan die lig gebring wat homologie met ensieme van verskillende swamme en nie-Saccharomyces gisspesies getoon het. Verder het die skandering van die metagenomiese biblioteek die isolasie van fosmiedklone van gisoorsprong wat positief is vir chitinase aktiwiteit (22 klone) en β-glukosidase aktiwiteit (11 klone) tot gevolg gehad. Twee van hierdie klone, BgluFos-G10 en ChiFos-C21, is met volgende generasie volgordebepaling ontleed. BgluFos-G10 het twee oopleesrame (OLRe) wat homologie met glikosiel hidrolase familie 16 proteïene het, vertoon maar geen OLRe wat chitinase ensieme enkodeer kon in die ChiFos-C21 kloon geïdentifiseer word nie. Al die potensiële OLRe wat geïdentifiseer is, het homologie aan ‘n genepoel van Clavispora lusitaniae ATCC 42720 vertoon, wat daarop dui dat die gekloneerde DNS fragmente aan ‘n gisspesie behoort wat naverwant aan C. lusitaniae of lede van die Metschinikowiaceae familie is. In geheel gesien het ons studie ‘n verskeidenheid van nuwe hidrolitiese ensieme geïdentifiseer. Die bepaling van die volledige geenvolgordes van hierdie geïdentifiseerde ensieme sal die onmiddelike opvolg aksie van hierdie studie wees. Verder is die hidrolitiese en anti-swam aktiwiteite wat deur die gisisolate gedemonstreer is, van hoof belang, asook die evaluering van hulle potensiaal as biokontrole agente teen wingerd swampatogene en wyn bederfgiste. Dit sal ook interessant wees om die potensiële impak van hierdie ensieme onder wynmaakkondisies te bepaal, en dit kan dus ons volgende ondersoek stap wees.

Motivation: Network interpretation using correlations has several known difficulties. Firstly, the data structure has discrete counts with an excess of zeros creating non-normal non-continuous data. Secondly, correlations, often used as similarity measures in network inference, are not causal. Thirdly, there is a masking effect of mutualism on commensalism and competition on amensalism in ecological networks that interfere with interpretation (Faust and Raes, 2012). More explicitly, the symmetric nature of correlations (cor(X,Y)=cor(Y,X)) can mask the affect of the asymmetric ecology relationship (commensalism and amensalism). We aim to solve the third issue which may speed up targeted drug therapies or disease diagnosis based on specific relationships in gut microbiomes. Methods: We apply a non-symmetric correlation method, Gini Correlations which should serve as a better classifier of ecological relationships revealing a fuller picture of microbiomes. First, create simulated correlated and independent Zero-Inflated Negative Binomial data. Second, validate Gini correlations by comparing Gini with Pearson Spearman and Kendall correlations; calculate false positive rate, true positive rate, accuracy, ROC, AUC after applying Benjamini-Hochberg (1995) multiple testing correction. Simulation Result: Gini is consistent and out performs other methods for small sample sizes of 10 and 25 producing consistently low false positive rates across 64+ simulation settings as well as consistently high accuracy rates. When sample size is increased to 50 Gini performs as well as other methods. Real Data Result: For well-defined microbial communities Gini correlations found novel biologically and medically relevant relationships. However, Gini's ability to unmask non-symmetric ecological relationships is yet to be determined.

No description available.

A Profile Hidden Markov Model (HMM) is a statistical model for representing a multiple sequence alignment (MSA). Profile HMMs are important tools for sequence homology detection and have been used in wide a range of bioinformatics applications including protein structure prediction, remote homology detection, and sequence alignment. Profile HMM methods result in accurate alignments on datasets with evolutionarily similar sequences; however, I will show that on datasets with evolutionarily divergent sequences, the accuracy of HMM-based methods degrade. My dissertation presents a new statistical model for representing an MSA by using a set of HMMs. The family of HMM (fHMM) approach uses multiple HMMs instead of a single HMM to represent an MSA. I present a new algorithm for sequence alignment using the fHMM technique. I show that using the fHMM technique for sequence alignment results in more accurate alignments than the single HMM approach. As sequence alignment is a fundamental step in many bioinformatics pipelines, improvements to sequence alignment result in improvements across many different fields. I show the applicability of fHMM to three specific problems: phylogenetic placement, taxonomic profiling and identification, and MSA estimation. In phylogenetic placement, the problem addressed is how to insert a query sequence into an existing tree. In taxonomic identification and profiling, the problems addressed are how to taxonomically classify a query sequence, and how to estimate a taxonomic profile on a set of sequences. Finally, both profile HMM and fHMM require a backbone MSA as input in order to align the query sequences. In MSA estimation, the problem addressed is how to estimate a ``de novo'' MSA without the use of an existing backbone alignment. For each problem, I present a software pipeline that implements the fHMM specifically for that domain: SEPP for phylogenetic placement, TIPP for taxonomic profiling and identification, and UPP for MSA estimation. I show that SEPP has improved accuracy compared to the single HMM approach. I also show that SEPP results in more accurate phylogenetic placements compared to existing placement methods, and SEPP is more computationally efficient, both in peak memory usage and running time. I show that TIPP more accurately classifies novel sequences compared to the single HMM approach, and TIPP estimates more accurate taxonomic profiles than leading methods on simulated metagenomic datasets. I show how UPP can estimate ``de novo'' alignments using fHMM. I present results that show UPP is more accurate and efficient than existing alignment methods, and estimates accurate alignments and trees on datasets containing both full-length and fragmentary sequences. Finally, I show that UPP can estimate a very accurate alignment on a dataset with 1,000,000 sequences in less than 2 days without the need of a supercomputer. / Computer Sciences / text

Microbial communities are recognized as major drivers of global biogeochemical processes. However, the genetic diversity and composition, as well as processes leading to the origin and diversification of these communities in space and time, are poorly understood. Character- ization of microbial communities using high-throughput sequencing of 16S tags shows that Operational Taxonomic Unit (OTU) abundances can be approximated by a gamma distribu- tion, which suggests structuring around small numbers of highly abundant OTUs and a large proportion of rare OTUs. The current methods used to characterize how communities are structured rely on multivariate statistics, which operate on pair-wise distance matrices. My analyses demonstrate that use of these methods, by reducing a highly-dimensional data set (tens of samples, thousands of OTUs), results in a significant loss of information. I demon- strate that, in some cases, up to 80% of the least abundant OTUs may be removed while still recovering the same community relationships; this indicates these metrics are biased toward the highly abundant OTUs. I also demonstrate that the observed patterns of OTU abundance detected from microbial communities can be robustly modeled using techniques similar to those used to model the presence and absence of genes in genome evolution. Using simulation studies, I show that general Markov models in a Bayesian inference framework out- perform traditional, multivariate ecological methods in recovering true community structure. Applying this new methodology to Atlantic Ocean communities uncovered a distance-decay effect which was not revealed by the traditional methods; applying to communities discov- ered on Hog Island point toward mechanisms of thicket establishment. Although the ocean data set operated on a much larger, continental scale, characterization of the sequence data generated from the nutrient-poor soil on Hog Island, a barrier island off the Virginia coast, allows for a better characterization of the processes affecting these communities on a much smaller scale. Finally, using 16S data from the Human Vaginal Microbiome Project, gener- ated here at VCU under the umbrella of the overall NIH HMP initiative, I give examples of the quality control, analysis and visualization pipeline that I developed to support the efforts of this project. In conclusion, my analyses of the metagenomic sequence data from bacterial communities sampled from different environments demonstrate that the proper identification of the biological processes influencing these communities requires the development and im- plementation of new statistical and computational methodologies that take advantage of the extensive amount of information generated in next-generation, high-throughput sequencing projects.

Microorganisms are an indispensable part of our ecosystem, yet the natural metabolic and ecological diversity of these organisms is poorly understood due to a historical reliance of microbiology on laboratory grown cultures. The awareness that this diversity cannot be studied by laboratory isolation, together with recent advances in low cost scalable sequencing technology, have enabled the foundation of culture-independent microbiology, or metagenomics. The study of environmental microbial samples with metagenomics has led to many advances, but a number of technological and methodological challenges still remain. A potentially diverse set of taxa may be represented in anyone environmental sample. Existing tools for representing the genetic composition of such samples sequenced with short-read data, and tools for identifying variation amongst them, are still in their infancy. This thesis makes the case that a new framework based on a joint-genome graph can constitute a powerful tool for representing and manipulating the joint genomes of population samples. I present the development of a collection of methods, called SCRAPS, to construct these efficient graphs in small communities without the availability or bias of a reference genome. A key novelty is that genetic variation is identified from the data structure using a probabilistic algorithm that can provide a measure of the confidence in each call. SCRAPS is first tested on simulated short read data for accuracy and efficiency. At least 95% of non-repetitive small-scale genetic variation with a minor allele read depth greater than 10x is correctly identified; the number false positives per conserved nucleotide is consistently better than 1 part in 333 x 103. SCRAPS is then applied to artificially pooled experimental datasets. As part of this study, SCRAPS is used to identify genetic variation in an epidemiological 11 sample Neisseria meningitidis dataset collected from the African meningitis belt". In total 14,000 sites of genetic variation are identified from 48 million Illumina/Solexa reads. The results clearly show the genetic differences between two waves of infection that has plagued northern Ghana and Burkina Faso.

Os produtos naturais microbianos (NP) tem demonstrado ser inestimáveis pontos de partida na descoberta e desenvolvimento de medicamentos aprovados pelo FDA. A abordagem tradicional para a identificação de produtos naturais microbianos exige a cultura em laboratório. Infelizmente, os métodos convencionais baseados nesta metodologia foram desestimulados devido a altas taxas de redescoberta de moléculas. Os métodos independentes de cultura que se baseiam no sequenciamento do metagenoma microbiano sugerem a ocorrência de um enorme reservatório inexplorado de clusters biossintéticos de produtos naturais (BGCs) no meio ambiente. Neste trabalho utilizamos uma metodologia baseada em PCR e barcoding amplicon-sequencing para buscar importantes famílias de produtos naturais como peptídeos não ribossomais (NRP), ácido 3-amino-5-hidroxibenzóico (AHBA), dímeros de triptofano (TD), policetídeos, aminoglicosídeos e outros. Para isto desenvolvemos um script chamado SecMetPrimer que nos permitiu bioinformaticamente desenhar conjuntos de primers contendo um gradiente de degenerâncias. No total, desenhamos 165 conjuntos de primers. Os amplicons foram obtidos por PCR padrão, tendo sido concatenados barcodes específicos por amostra e sequenciados através de Illumina MiSeq. Para validar, utilizamos eDNA (environmental DNA) de bibliotecas metagenômicas, totalizando 223 milhões de clones. Através das análises bioinformáticas, as curvas de rarefação foram calculadas e a diversidade para cada família foi determinada. Foi realizada uma reamplificação dos domínios de adenilação de peptídeo não ribossomal e domínios de cetosintase de policetídeos utilizando eDNA isolado de 25 amostras diferentes coletadas em Mata Atlântica, Cerrado e ambiente marinho. Nossos dados indicaram a correlação entre distância geográfica e o tipo ecológico dos biomas. Deste modo, foi possível assim atribuir genes relacionados à clusters biossintéticos que codificam importantes produtos naturais à informações taxonômicas e metabólicas. Deste modo identificamos os melhores hotspots para busca de diversidade biossintética dentre as amostras analisadas. / Microbial natural products (NP) have proven to be invaluable starting points in the discovery and development of many drugs approved by FDA. The traditional approach to identify microbial natural products requires the culturing in the laboratory. Unfortunately, conventional culture-based methods have been deemphasized due to high rediscovery rates. Culture-independent methods applying microbial (meta)genome sequencing suggest the occurrence of an enormous untapped reservoir of natural-product-encoding biosynthetic gene clusters (BGCs) in the environment. Here we have used a PCR-based approach and barcoding ampliconsequencing derived from important families of microbial natural products such as nonribosomal peptides (NRP), polyketides (PK), 3-amino-5-hydroxybenzoic acidcontaining NPs (AHBA), tryptophan dimmers (TD), aminoglycosides, phosphonocontaining NPs and others. We have written an internal script called SecMetPrimer that allowed us to bioinformatically design sets of primers containing a range of degeneracy to amplify these genes. At the total, we designed 165 different sets of primers. The amplicons were obtained by standard PCR containing double-barcodedtarget primers and sequenced by Illumina MiSeq platform. The validation process was conducted using eDNA from metagenomic libraries containing a 223 millions of clones. The rarefaction and diversity analyses were assigned, and the best-hit primer for each family was chosen. We have re-amplified the nonribosomal peptide adenylation domains and polyketide ketosynthase domains, using as substrate environmental DNA isolated from 25 different samples collected in Atlantic Forest, Cerrado and marine environment. Our data indicate a correlation between geographic distance and biome-type, and the biosynthetic diversity found in these environments. Thus, by assigning reads to known BGCs against taxonomic and metabolic profiles, we have identified the hotspots of relevant biosynthetic diversity among the analyzed samples.

Selon les projections démographiques de la FAO, la population mondiale atteindra 9 milliards de personnes d'ici 2050. Cette augmentation sera associée à une demande accrue de produits agricoles et à une augmentation de la production de déchets. Par conséquent, des approches alternatives dans les pratiques agricoles, tels que l'utilisation permanente de la couverture végétale et/ou l'application de boues d'épuration, sont envisagées pour répondre aux exigences mondiales et préserver l'environnement. Ces nouvelles pratiques pourraient influencer le fonctionnement et les propriétés du sol et des organismes microbiens présents dans cet environnement. Par conséquent, passer de l'agriculture intensive à une agriculture écologiquement intensive pourrait entraîner des modifications de la biodiversité des sols. En utilisant différents systèmes expérimentaux permettant une comparaison entre différentes pratiques agricoles, des études de la diversité microbienne taxonomique et fonctionnelle du sol (bactéries et champignons) ont été entreprises. La diversité taxonomique des organismes microbiens a été obtenue par séquençage à haut débit des régions hypervariables des gènes codant l'ARN16S et l'ITS1. Nous avons évalué les rôles écologiques des microorganismes du sol en utilisant des identifications taxonomiques, puis des études permettant d'examiner leur physiologie et leurs fonctions par rapport à différentes propriétés physicochimiques du sol. Nous avons constaté que la fertilisation azotée avait une incidence négative sur la diversité microbienne du sol et modifié leur fonctionnalité. Ces effets peuvent être modulés par l'utilisation de PPC ou l'application de boues. Ces travaux indiquent que les pratiques agricoles conventionnelles ont un impact sur la biodiversité microbienne du sol et peuvent être remplacées par des pratiques agricoles plus respectueuses de l'environnement afin de préserver l'écosystème et ses services / According to demographic projections, world population will reach 9 billion people by 2050. This increase will be associated with higher demand of agricultural products and an increase in wastes production. Therefore, alternative approaches in agricultural practices; such as permanent plant cover usage and/or sewage sludge application, are envisaged to meet global demands and preserve the environment. These new practices could therefore influence the properties of the soil and its functioning. Therefore moving from intensive to ecologically intensive agriculture could lead to modifications in soil biodiversity. Using different experimental systems allowing comparison between different agricultural practices, studies of the taxonomic and functional soil microbial diversity (bacteria and fungi) had been undertaken. This was achieved by next generation high-throughput sequencing of the hypervariable regions of the genes encoding RNA16S and ITS1. Sequencing was performed using an Illumina platform and the sequences obtained were analyzed using various bioinformatic tools. We inferred the ecological roles of soil micro-organisms by using taxonomic identifications, moving on to the examination of their physiology and functions in comparison with different soil physiochemical properties. We found that nitrogen fertilization negatively impacted the soil microbial diversity and altered their functionality. These negative effects have been modulated by the PPC usage or SS application. Proving that conventional agricultural practices effects the soil biodiversity and can be replaced by ecofriendly farming applications in order to preserve the ecosystem and its services

Na última década tem sido possível reconstruir o genoma de bactérias e arquéias presentes em comunidades microbianas de ambientes naturais a partir de dados metagenômicos. Isso tem revolucionado nosso entendimento sobre a topologia da árvore da vida e a descoberta de novas capacidades metabólicas, bem como auxiliado na identificação mais acurada de genes de interesse industrial, visto que os dados estão mais completos e menos fragmentados. Com base neste contexto, o objetivo geral deste projeto foi reconstruir o genoma de bactérias ligadas a degradação de biomassa vegetal em comunidades microbianas da compostagem, focando em análises de diversidade de enzimas de Glicosil Hidrolases (GHs), a partir de dados de sequências metagenômicas gerados no projeto temático processo 11/50870-6. Para alcançar os nossos objetivos, foram desenvolvidos pipelines computacionais com softwares já disponíveis na literatura e foram utilizados dois conjuntos principais de dados de sequenciamento massivo (um conjunto de dados seriados que engloba inúmeros estágios do processamento da compostagem e um conjunto de dados do metagenoma de um consórcio microbiano celulolítico e termofílico construído a partir de amostras da compostagem). Foram reconstruídos 13 genomas (sete genomas em amostras dos dados seriados e seis genomas na amostra do consórcio microbiano), sendo identificado no mínimo quatro novas espécies. As análises baseadas em filogenômica indicam a presença de pelo menos uma nova classe dentro do filo Firmicutes, uma nova espécie da família Paenibacillaceae e a reconstrução pela primeira vez do genoma da espécie Bacillus thermozeamaize. Também foram identificadas 33 lacunas/ilhas metagenômicas (IMs). Essas regiões apresentaram genes diretamente ligados a biossíntese de polissacarídeos do envelope celular, pseudogenes e proteínas hipotéticas. Algumas dessas proteínas estão diretamente ligadas ao reconhecimento de bacteríofagos durante a fase de infecção viral. A presença de IMs também indica uma divergência entre as populações microbianas presentes na compostagem com a espécie de referência. Quanto ao potencial de degradação de biomassa vegetal, todos os microrganismos apresentam genes com potencial para degradação de material lignocelulolítico durante o processamento de diferentes estágios da compostagem, indicando a importância do papel funcional dessas bactérias na compostagem. / In the last decade it has been possible to reconstruct Bacteria and Archaea genomes that are in natural microbial communities from metagenomic samples. This has revolutionized our understanding of the topology of the tree of life and the discovery of new metabolic functions, as well as aided in more accurate identification of industrial bioprospecting genes, since the genomic data are more complete and less fragmented. Based on this background, the aim of this project was to reconstruct the bacterial genomes linked to plant biomass degradation in composting communities, focusing on diversity analysis of Glycosyl Hydrolases (GHs) from metagenomic sequence data generated in the Thematic Project (Process 11/50870-6). To achieve our objectives, computational pipelines have been developed (this pipelines were based on software already available in the literature) and we use these pipelines in two massive data sets generated by high-throughput sequencing (one data set of time series compost sample which includes several stages of the composting process and other data set from a cellu- lolytic and thermophilic microbial consortium). Thirteen genomes were reconstructed (seven genomes from time series metagenomic data and six genomes from microbial consortium). At least four new species have been identified, and the analyzes based on phylogenomic inferences indicate the presence of at least one new class of Firmicutes phylum, and a new Paenibacillaceae family and the reconstruction for the first time the Bacillus thermozeamaize genome. They also identified 33 gaps/metagenomic Islands (IMs). These gaps had genes directly linked to polysaccharide biosynthesis of the cell envelope, pseudogenes and hypothetical proteins. Some of these proteins are directly linked to the bacteriophage during the recognition phase of viral infection. The presence of gaps also indicates a divergence between microbial populations present in the compost with the reference genome. All microbial genomes reconstructed in this studyhave genes linked to lignocellulolytic potential degradation during the different stages of composting process, indicating the functional role this bactéria in this environment.