Gene Correlation Guided Gene Selection for Microarray Data Classification

The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.

Extensive microbial diversity within the chicken gut microbiome revealed by metagenomics and culture

Background The chicken is the most abundant food animal in the world. However, despite its importance, the chicken gut microbiome remains largely undefined. Here, we exploit culture-independent and culture-dependent approaches to reveal extensive taxonomic diversity within this complex microbial community. Results We performed metagenomic sequencing of fifty chicken faecal samples from two breeds and analysed these, alongside all (n = 582) relevant publicly available chicken metagenomes, to cluster over 20 million non-redundant genes and to construct over 5,500 metagenome-assembled bacterial genomes. In addition, we recovered nearly 600 bacteriophage genomes. This represents the most comprehensive view of taxonomic diversity within the chicken gut microbiome to date, encompassing hundreds of novel candidate bacterial genera and species. To provide a stable, clear and memorable nomenclature for novel species, we devised a scalable combinatorial system for the creation of hundreds of well-formed Latin binomials. We cultured and genome-sequenced bacterial isolates from chicken faeces, documenting over forty novel species, together with three species from the genus Escherichia, including the newly named species Escherichia whittamii. Conclusions Our metagenomic and culture-based analyses provide new insights into the bacterial, archaeal and bacteriophage components of the chicken gut microbiome. The resulting datasets expand the known diversity of the chicken gut microbiome and provide a key resource for future high-resolution taxonomic and functional studies on the chicken gut microbiome.

The evolution of interdependence in a four-way mealybug symbiosis

Mealybugs are insects that maintain intracellular bacterial symbionts to supplement their nutrientpoor plant sap diets. Some mealybugs have a single betaproteobacterial endosymbiont, a Candidatus Tremblaya species (hereafter Tremblaya) that alone provides the insect with its required nutrients. Other mealybugs have two nutritional endosymbionts that together provide these nutrients, where Tremblaya has gained a gammaproteobacterial partner that resides in the cytoplasm of Tremblaya. Previous work had established that Pseudococcus longispinus mealybugs maintain not one but two species of gammaproteobacterial endosymbionts along with Tremblaya. Preliminary genomic analyses suggested that these two gammaproteobacterial endosymbionts have large genomes with features consistent with a relatively recent origin as insect endosymbionts, but the patterns of genomic complementarity between members of the symbiosis and their relative cellular locations were unknown. Here, using long-read sequencing and various types of microscopy, we show that the two gammaproteobacterial symbionts of P. longispinus are mixed together within Tremblaya cells, and that their genomes are somewhat reduced in size compared to their closest non-endosymbiotic relatives. Both gammaproteobacterial genomes contain thousands of pseudogenes, consistent with a relatively recent shift from a free-living to endosymbiotic lifestyle. Biosynthetic pathways of key metabolites are partitioned in complex interdependent patterns among the two gammaproteobacterial genomes, the Tremblaya genome, and horizontally acquired bacterial genes that are encoded on the mealybug nuclear genome. Although these two gammaproteobacterial endosymbionts have been acquired recently in evolutionary time, they have already evolved co-dependencies with each other, Tremblaya, and their insect host.SignificanceMealybugs are sap-feeding insects that house between one and three bacterial endosymbionts to supplement their nutritionally poor diets. Many mealybug-bacteria relationships were established tens or hundreds of millions of years ago, and these ancient examples show high levels host-endosymbiont genomic and metabolic integration. Here, we describe the complete genomes and cellular locations for two bacterial endosymbiont which have recently transitioned from a free-living to an intracellular state. Our work reveals the rapid emergence of metabolic interdependence between these two nascent endosymbionts, their partner bacterial co-symbiont in whose cytoplasm they reside, and their insect host cell. Our work confirms that intracellular bacteria rapidly adapt to a host-restricted lifestyle through breakage or loss of redundant genes.

A Genomic Census of the Chicken Gut Microbiome using Metagenomics and Culture

Background. The chicken is the most abundant food animal in the world. However, despite its importance, the chicken gut microbiome remains largely undefined. Here, we exploit culture-independent and culture-dependent approaches to deliver a genomic census of this complex microbial community. Results. We performed metagenomic sequencing of fifty chicken faecal samples from two breeds and analysed these, alongside all (n=582) relevant publicly available chicken metagenomes, to cluster over 20 million non-redundant genes and to construct over 5,500 metagenome-assembled bacterial genomes. In addition, we recovered nearly 600 bacteriophage genomes This represents the most comprehensive view of the chicken gut associated microbiome to date, encompassing dozens of novel candidate bacterial genera and hundreds of novel candidate species. To provide a stable, clear and memorable nomenclature for novel species, we devised a scalable combinatorial system for the creation of hundreds of well-formed Latin binomials. We cultured and genome-sequenced bacterial isolates from faeces, documenting thirty novel species, together with three species from the genus Escherichia, including the newly named species Escherichia whittamii.Conclusions. Our metagenomic and culture-based analyses provide new insights into the bacterial, archaeal and bacteriophage components of the chicken gut microbiome. The resulting datasets expand the known diversity of the chicken gut microbiome and provides a key resource for future high-resolution taxonomic and functional studies on the chicken gut microbiome.

A Genomic Census of the Chicken Gut Microbiome using Metagenomics and Culture

Background. The chicken is the most abundant food animal in the world. However, despite its importance, the chicken gut microbiome remains largely undefined. Here, we exploit culture-independent and culture-dependent approaches to deliver a genomic blueprint of this complex microbial community.Results. We performed metagenomic sequencing of fifty chicken faecal samples from two breeds and analysed these, alongside all (n=582) relevant publicly available chicken metagenomes, to cluster over 20 million non-redundant genes and to construct over 5,500 metagenome-assembled bacterial genomes. In addition, we recovered nearly 600 bacteriophage genomes This represents the most comprehensive view of the chicken gut associated microbiome to date, encompassing dozens of novel candidate bacterial genera and hundreds of novel candidate species. Keen to provide a stable, clear and memorable nomenclature for novel species, we devised a scalable combinatorial system for the creation of hundreds of well-formed Latin binomials. We cultured and genome-sequenced bacterial isolates from faeces, documenting thirty novel species, together with three species from the genus Escherichia, including the newly named species Escherichia whittamii.Conclusions. Our metagenomic and culture-based analyses provide new insights into the bacterial, archaeal and bacteriophage components of the chicken gut microbiome. The resulting datasets expand the known diversity of the chicken gut microbiome and provides a key resource for future high-resolution taxonomic and functional studies on the chicken gut microbiome.

Uncovering the genomic potential of the Amazon River microbiome to degrade rainforest organic matter

Background The Amazon River is one of the largest in the world and receives huge amounts of terrestrial organic matter (TeOM) from the surrounding rainforest. Despite this TeOM is typically recalcitrant (i.e. resistant to degradation), only a small fraction of it reaches the ocean, pointing to a substantial TeOM degradation by the river microbiome. Yet, microbial genes involved in TeOM degradation in the Amazon River were barely known. Here, we examined the Amazon River microbiome by analysing 106 metagenomes from 30 sampling points distributed along the river. Results We constructed the Amazon River basin Microbial non-redundant Gene Catalogue (AMnrGC) that includes ~ 3.7 million non-redundant genes, affiliating mostly to bacteria. We found that the Amazon River microbiome contains a substantial gene-novelty compared to other relevant known environments (rivers and rainforest soil). Genes encoding for proteins potentially involved in lignin degradation pathways were correlated to tripartite tricarboxylates transporters and hemicellulose degradation machinery, pointing to a possible priming effect. Based on this, we propose a model on how the degradation of recalcitrant TeOM could be modulated by labile compounds in the Amazon River waters. Our results also suggest changes of the microbial community and its genomic potential along the river course. Conclusions Our work contributes to expand significantly our comprehension of the world’s largest river microbiome and its potential metabolism related to TeOM degradation. Furthermore, the produced gene catalogue (AMnrGC) represents an important resource for future research in tropical rivers.

An expanded gene catalog of the mouse gut metagenome

High-quality and comprehensive reference gene catalogs are essential for metagenomic research. The rather low diversity of samples used to construct existing catalogs of mouse gut metagenomes limits the size and numbers of identified genes in existing catalogs. We therefore established an expanded gene catalog of genes in the mouse gut metagenomes (EMGC) containing >5.8 million genes by integrating 88 newly sequenced samples, 86 mouse-gut-related bacterial genomes and 3 existing gene catalogs. EMGC increases the number on non-redundant genes by more than one million genes compared to the so far most extensive catalog. More than 50% of the genes in EMGC were taxonomically assigned and 30% were functionally annotated. 902 Metagenomic species (MGS) assigned to 122 taxa are identified based on the EMGC. The EMGC-based analysis of samples from groups of mice originating from different animal providers, housing laboratories and genetic strains substantiated that diet is a major contributor to differences in composition and functional potential of the gut microbiota irrespective of differences in environment and genetic background. We envisage that EMGC will serve as an efficient and resource-saving reference dataset for future metagenomic studies in mice.

A consensus multi-view multi-objective gene selection approach for improved sample classification

Background In the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different ‘omics’ resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency. Results In this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification. Conclusions The experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

A Genomic Blueprint of the Chicken Gut Microbiome

Background: The chicken is the most abundant food animal in the world. However, despite its importance, the chicken gut microbiome remains largely undefined. Here, we exploit culture-independent and culture-dependent approaches to deliver a genomic blueprint of this complex microbial community. Results: We performed metagenomic sequencing of fifty chicken faecal samples from two breeds and analysed these, alongside all (n=582) relevant publicly available chicken metagenomes, to cluster over 20 million non-redundant genes and to construct over 5,500 metagenome-assembled bacterial genomes. In addition, we recovered nearly 600 bacteriophage genomes This represents the most comprehensive view of the chicken gut associated microbiome to date, encompassing dozens of novel candidate bacterial genera and hundreds of novel candidate species. Keen to provide a stable, clear and memorable nomenclature for novel species, we devised a scalable combinatorial system for the creation of hundreds of well-formed Latin binomials. We cultured bacterial isolates from faeces to deliver 282 whole genome sequences, incorporating thirty novel species, together with three species from the genus Escherichia, including the newly named species Escherichia whittamii.Conclusions: Our metagenomic and culture-based analyses provide new insights into the bacterial, archaeal and bacteriophage components of the chicken gut microbiome. The resulting datasets expand the known diversity of the chicken gut microbiome and provides a key resource for future high-resolution taxonomic and functional studies on the chicken gut microbiome.