Phylogenetic profiling: how much input data is enough?

Phylogenetic profiling is a well-established approach for predicting gene function based on patterns of gene presence and absence across species. Much of the recent developments have focused on methodological improvements, but relatively little is known about the effect of input data size on the quality of predictions. In this work, we ask: how many genomes and functional annotations need to be considered for phylogenetic profiling to be effective? Phylogenetic profiling generally benefits from an increased amount of input data. However, by decomposing this improvement in predictive accuracy in terms of the contribution of additional genomes and of additional annotations, we observed diminishing returns in adding more than ∼ 100 genomes, whereas increasing the number of annotations remained strongly beneficial throughout. We also observed that maximising phylogenetic diversity within a clade of interest improves predictive accuracy, but the effect is small compared to changes in the number of genomes under comparison. Finally, we show that these findings are supported in light of the Open World Assumption, which posits that functional annotation databases are inherently incomplete. All the tools and data used in this work are available for reuse from http://lab.dessimoz.org/14_phylprof. Scripts used to analyse the data are available on request from the authors

Differential Functional Constraints Cause Strain-Level Endemism in Polynucleobacter Populations.

The adaptation of bacterial lineages to local environmental conditions creates the potential for broader genotypic diversity within a species, which can enable a species to dominate across ecological gradients because of niche flexibility. The genus Polynucleobacter maintains both free-living and symbiotic ecotypes and maintains an apparently ubiquitous distribution in freshwater ecosystems. Subspecies-level resolution supplemented with metagenome-derived genotype analysis revealed that differential functional constraints, not geographic distance, produce and maintain strain-level genetic conservation in Polynucleobacter populations across three geographically proximal riverine environments. Genes associated with cofactor biosynthesis and one-carbon metabolism showed habitat specificity, and protein-coding genes of unknown function and membrane transport proteins were under positive selection across each habitat. Characterized by different median ratios of nonsynonymous to synonymous evolutionary changes (dN/dS ratios) and a limited but statistically significant negative correlation between the dN/dS ratio and codon usage bias between habitats, the free-living and core genotypes were observed to be evolving under strong purifying selection pressure. Highlighting the potential role of genetic adaptation to the local environment, the two-component system protein-coding genes were highly stable (dN/dS ratio, < 0.03). These results suggest that despite the impact of the habitat on genetic diversity, and hence niche partition, strong environmental selection pressure maintains a conserved core genome for Polynucleobacter populations. IMPORTANCE Understanding the biological factors influencing habitat-wide genetic endemism is important for explaining observed biogeographic patterns. Polynucleobacter is a genus of bacteria that seems to have found a way to colonize myriad freshwater ecosystems and by doing so has become one of the most abundant bacteria in these environments. We sequenced metagenomes from locations across the Chicago River system and assembled Polynucleobacter genomes from different sites and compared how the nucleotide composition, gene codon usage, and the ratio of synonymous (codes for the same amino acid) to nonsynonymous (codes for a different amino acid) mutations varied across these population genomes at each site. The environmental pressures at each site drove purifying selection for functional traits that maintained a streamlined core genome across the Chicago River Polynucleobacter population while allowing for site-specific genomic adaptation. These adaptations enable Polynucleobacter to become dominant across different riverine environmental gradients

The Echinococcus canadensis (G7) genome: A key knowledge of parasitic platyhelminth human diseases

Background: The parasite Echinococcus canadensis (G7) (phylum Platyhelminthes, class Cestoda) is one of the causative agents of echinococcosis. Echinococcosis is a worldwide chronic zoonosis affecting humans as well as domestic and wild mammals, which has been reported as a prioritized neglected disease by the World Health Organisation. No genomic data, comparative genomic analyses or efficient therapeutic and diagnostic tools are available for this severe disease. The information presented in this study will help to understand the peculiar biological characters and to design species-specific control tools. Results: We sequenced, assembled and annotated the 115-Mb genome of E. canadensis (G7). Comparative genomic analyses using whole genome data of three Echinococcus species not only confirmed the status of E. canadensis (G7) as a separate species but also demonstrated a high nucleotide sequences divergence in relation to E. granulosus (G1). The E. canadensis (G7) genome contains 11,449 genes with a core set of 881 orthologs shared among five cestode species. Comparative genomics revealed that there are more single nucleotide polymorphisms (SNPs) between E. canadensis (G7) and E. granulosus (G1) than between E. canadensis (G7) and E. multilocularis. This result was unexpected since E. canadensis (G7) and E. granulosus (G1) were considered to belong to the species complex E. granulosus sensu lato. We described SNPs in known drug targets and metabolism genes in the E. canadensis (G7) genome. Regarding gene regulation, we analysed three particular features: CpG island distribution along the three Echinococcus genomes, DNA methylation system and small RNA pathway. The results suggest the occurrence of yet unknown gene regulation mechanisms in Echinococcus. Conclusions: This is the first work that addresses Echinococcus comparative genomics. The resources presented here will promote the study of mechanisms of parasite development as well as new tools for drug discovery. The availability of a high-quality genome assembly is critical for fully exploring the biology of a pathogenic organism. The E. canadensis (G7) genome presented in this study provides a unique opportunity to address the genetic diversity among the genus Echinococcus and its particular developmental features. At present, there is no unequivocal taxonomic classification of Echinococcus species; however, the genome-wide SNPs analysis performed here revealed the phylogenetic distance among these three Echinococcus species. Additional cestode genomes need to be sequenced to be able to resolve their phylogeny.Fil: Maldonado, Lucas Luciano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Assis, Juliana. Fundación Oswaldo Cruz; BrasilFil: Gomes Araújo, Flávio M.. Fundación Oswaldo Cruz; BrasilFil: Salim, Anna C. M.. Fundación Oswaldo Cruz; BrasilFil: Macchiaroli, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Cucher, Marcela Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Camicia, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Fox, Adolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Rosenzvit, Mara Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Oliveira, Guilherme. Instituto Tecnológico Vale; Brasil. Fundación Oswaldo Cruz; BrasilFil: Kamenetzky, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; Argentin

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.

MOTIVATION: Protein-protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies. RESULTS: PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi-a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations. AVAILABILITY AND IMPLEMENTATION: https://github.com/ima23/maxent-ppi. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

Improving co-evolution basedmethods for protein-protein interaction prediction

Tesis doctoral inédita. Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 18-06-201

Characterizing the human vaginal microbiome using high-throughput sequencing

The human vaginal microbiome undoubtedly has a significant role in reproductive health and for protection from infectious organisms. Recent efforts to characterize the bacterial species of the vagina using molecular techniques have uncovered an unexpected diversity. Using high-throughput sequencing I sought to describe the structure and function of the vaginal microbiome under different physiological states including healthy, bacterial vaginosis (BV), post-menopausal vaginal atrophy, and acute vulvovaginal candidiasis (VVC). Partial 16S rRNA gene sequencing revealed that healthy, asymptomatic women most often have vaginal biotas dominated by Lactobacillus iners or L. crispatus. In contrast, BV is a heterogeneous, highly diversified condition with reduced Lactobacillus abundance. Similar to BV, post-menopausal women experiencing vaginal dryness were depleted in lactobacilli and had a more diverse vaginal profile. In the case of VVC, the biotas were not significantly altered compared to healthy women despite the fungal overgrowth. One organism, Lactobacillus iners was ubiquitously present in all conditions, and became predominant following antibiotic and probiotic treatment of BV. To uncover the potential role of this bacterium, I used whole genome sequencing of vaginal isolate AB-1. The genome is predicted to be the smallest of any Lactobacillus at 1.3 Mbp, but having a higher proportion of horizontally acquired genes. These results, along with predicted adhesins and a cholesterol-dependent cytolysin, indicate L. iners is highly adapted for the vagina and could have an uncharacterized role in the etiology of BV. As BV is the most common vaginal ailment with severe implications on acquisition and transmission of sexually transmitted infections, and complications during pregnancy, I sought iito examine the functional contribution of the organisms during BV using meta-RNA sequencing. L. iners drastically modulates gene expression in response to BV, and notably increases expression of a cholesterol-dependent cytolysin, mucin and glycerol transport and metabolic enzymes, and genes belonging to a CRISPR system - suggestive of bacteriophage influence in the community. Although diverse in taxonomic membership, there is evidence of functional conservation in BV including preference for glycogen and glycerol as carbon sources, and predicted end products of metabolism including an abundance of succinate and short-chain fatty acids. These studies add significantly to our understanding of the role lactobacilli can play in vaginal and reproductive health

Homology inference with specific molecular constraints

Evolutionary processes can be considered at multiple levels of biological organization. The work developed in this thesis focuses on protein molecular evolution. Although proteins are linear polymers composed from a basic set of 20 amino acids, they generate an enormous variety of form and function. Proteins that have arisen by a common descent are classified into families; they often share common properties including similarities in sequence, structure, and function. Multiple methods have been developed to infer evolutionary relationships between proteins and classify them into families. Yet, those generic methods are often inaccurate, especially when specific protein properties limit their applications. In this thesis, we analyse two protein classes that are often difficult for the evolutionary analysis: the coiled-coils – repetitive protein domains defined by a simple widespread peptide motif (chapters 2 and 3) and Rab small GTPases – a large family of closely related proteins (chapters 4 and 5). In both cases, we analyse the specific properties that determine protein structure and function and use them to improve their evolutionary inference