Annotation enrichment analysis: an alternative method for evaluating the functional properties of gene sets

Cell reprogramming design by transfer learning of functional transcriptional networks

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach's versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.

Cell reprogramming design by transfer learning of functional transcriptional networks

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cell behavior. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates to generate a model of the functional network dynamics that can be transferred to specific reprogramming goals. The approach additively combines transcriptional responses to gene perturbations (single-gene knockdowns and overexpressions) to minimize the transcriptional difference between a given pair of initial and target states. We demonstrate the flexibility of our approach by applying it to a microarray dataset comprising over 9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of over 10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an average AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases as the developmental relatedness decreases. We also show that fewer genes are needed to progress along developmental paths than to regress. Together, these findings establish a proof-of-concept for our approach to computationally design control strategies and demonstrate their ability to provide insights into the dynamics of gene regulatory networks.

Deconstructing Allograft Adipose and Fascia Matrix: Fascia Matrix Improves Angiogenesis, Volume Retention, and Adipogenesis in a Rodent Model

BACKGROUND

Autologous fat grafting is commonly used for soft-tissue repair (approximately 90,000 cases per year in the United States), but outcomes are limited by volume loss (20% to 80%) over time. Human allograft adipose matrix (AAM) stimulates de novo adipogenesis in vivo, but retention requires optimization. The extracellular matrix derived from superficial fascia, interstitial within the adipose layer, is typically removed during AAM processing. Thus, fascia, which contains numerous important proteins, might cooperate with AAM to stimulate de novo adipogenesis, improving long-term retention compared to AAM alone.

METHODS

Human AAM and fascia matrix proteins (back and upper leg regions) were identified by mass spectrometry and annotated by gene ontology. A three-dimensional in vitro angiogenesis assay was performed. Finally, AAM and/or fascia (1 mL) was implanted into 6- to 8-week-old male Fischer rats. After 8 weeks, the authors assessed graft retention by gas pycnometry and angiogenesis (CD31) and adipocyte counts (hematoxylin and eosin) histologically.

RESULTS

Gene ontology annotation revealed an angiogenic enrichment pattern unique to the fascia, including lactadherin, collagen alpha-3(V) chain, and tenascin-C. In vitro, AAM stimulated 1.0 ± 0.17 angiogenic sprouts per bead. The addition of fascia matrix increased sprouting by 88% (2.0 ± 0.12; P < 0.001). A similar angiogenic response (CD31) was observed in vivo. Graft retention volume was 25% (0.25 ± 0.13) for AAM, significantly increasing to 60% (0.60 ± 0.14) for AAM/fascia ( P < 0.05). De novo adipogenesis was 12% (12.4 ± 7.4) for AAM, significantly increasing to 51% (51.2 ± 8.0) for AAM/fascia ( P < 0.001) by means of adipocyte quantification.

CONCLUSIONS

Combining fascia matrix with AAM improves angiogenesis and adipogenesis compared to AAM alone in rats. These preliminary in vitro and pilot animal studies should be further validated before definitive clinical adoption.

CLINICAL RELEVANCE STATEMENT

When producing an off-the-shelf adipose inducing product by adding a connective tissue fascial component (that is normally discarded) to the mix of adipose matrix, vasculogenesis is increased and, thus, adipogenesis and graft survival is improved. This is a significant advance in this line of product.

Predicted mouse interactome and network-based interpretation of differentially expressed genes

The house mouse or Mus musculus has become a premier mammalian model for genetic research due to its genetic and physiological similarities to humans. It brought mechanistic insights into numerous human diseases and has been routinely used to assess drug efficiency and toxicity, as well as to predict patient responses. To facilitate molecular mechanism studies in mouse, we present the Mouse Interactome Database (MID, Version 1), which includes 155,887 putative functional associations between mouse protein-coding genes inferred from functional association evidence integrated from 9 public databases. These putative functional associations are expected to cover 19.32% of all mouse protein interactions, and 26.02% of these function associations may represent protein interactions. On top of MID, we developed a gene set linkage analysis (GSLA) web tool to annotate potential functional impacts from observed differentially expressed genes. Two case studies show that the MID/GSLA system provided precise and informative annotations that other widely used gene set annotation tools, such as PANTHER and DAVID, did not. Both MID and GSLA are accessible through the website http://mouse.biomedtzc.cn.

Wheat omics: Classical breeding to new breeding technologies

Wheat is an important cereal crop, and its significance is more due to compete for dietary products in the world. Many constraints facing by the wheat crop due to environmental hazardous, biotic, abiotic stress and heavy matters factors, as a result, decrease the yield. Understanding the molecular mechanism related to these factors is significant to figure out genes regulate under specific conditions. Classical breeding using hybridization has been used to increase the yield but not prospered at the desired level. With the development of newly emerging technologies in biological sciences i.e., marker assisted breeding (MAB), QTLs mapping, mutation breeding, proteomics, metabolomics, next-generation sequencing (NGS), RNA_sequencing, transcriptomics, differential expression genes (DEGs), computational resources and genome editing techniques i.e. (CRISPR cas9; Cas13) advances in the field of omics. Application of new breeding technologies develops huge data; considerable development is needed in bioinformatics science to interpret the data. However, combined omics application to address physiological questions linked with genetics is still a challenge. Moreover, viroid discovery opens the new direction for research, economics, and target specification. Comparative genomics important to figure gene of interest processes are further discussed about considering the identification of genes, genomic loci, and biochemical pathways linked with stress resilience in wheat. Furthermore, this review extensively discussed the omics approaches and their effective use. Integrated plant omics technologies have been used viroid genomes associated with CRISPR and CRISPR-associated Cas13a proteins system used for engineering of viroid interference along with high-performance multidimensional phenotyping as a significant limiting factor for increasing stress resistance in wheat.

Abattoir survey of bovine tuberculosis in tanta, centre of the Nile delta, with in silico analysis of gene mutations and protein-protein interactions of the involved mycobacteria

Bovine tuberculosis is a transboundary disease of high economic and public health burden worldwide. In this study, post-mortem examination of 750 cattle and buffalo in Tanta abattoir, Centre of the Nile Delta, revealed visible TB in 4% of animals and a true prevalence of 6.85% (95% CI: 5.3%-8.9%). Mycobacterial culture, histopathology and RT-PCR targeting all members of M. tuberculosis complex were performed, upon which 85%, 80% and 100% of each tested lesions were confirmed as TB, respectively. Mpb70-targeting PCR was conducted on ten RT-PCR positive samples for sequencing and identified nine Mycobacterium (M.) bovis strains and, interestingly, one M. tuberculosis (Mtb) strain from a buffalo. Bioinformatics tools were used for prediction of mutations, nucleotide polymorphisms, lineages, drug resistance and protein-protein interactions (PPI) of the sequenced strains. The Mtb strain was resistant to rifampicin, isoniazid and streptomycin, and to the best of our knowledge, this is the first report of multidrug resistant (MDR)-Mtb originating from buffaloes. Seven M. bovis strains were resistant to ethambutol and ethionamide. Such resistances were associated with KatG, rpoB, rpsL, embB and ethA genes mutations. Other mutations and nucleotide polymorphisms were also predicted, some are reported for the first time and require experimental work for validation. PPI revealed more interactions than what would be expected for a random set of proteins of similar size and had dense interactions between nodes that are biologically connected, as a group. Two M. bovis strains belonged to BOV AFRI lineage (Spoligotypes BOV 1; BOV 2) and eight strains belonged to East-Asian (Beijing) lineage. In conclusion, visible TB was prevalent in the study area, RT-PCR is the best to confirm the disease, MDR-Mtb is associated with buffalo TB, and mycobacteria of different lineages carry many resistance genes to chemotherapeutic agents used in treatment of human TB constituting a major public health risk.

Use of Network Pharmacology to Investigate the Mechanism by Which Allicin Ameliorates Lipid Metabolism Disorder in HepG2 Cells

Allicin has been well documented to exhibit a wide spectrum of biological activities, especially lipid-lowering activity, as a promising candidate for the management of nonalcoholic fatty liver disease (NALFD). However, the mechanisms underlying the therapeutic effects of allicin require further investigation. It is tempting to think of combining network pharmacology and experimental validation to investigate the mechanism by which allicin ameliorates lipid metabolism disorder in HepG2 cells. We established a cell model of hepatic steatosis induced by PA to investigate the antisteatotic effects of allicin. The studies showed that allicin reduced PA-induced lipid accumulation using Nile red staining and TC and TG assays. Then, 219 potential targets of allicin were successfully predicted by PharmMapper. According to Reactome Pathway Analysis, 44 potential targets related to lipid metabolism were screened out. Molecular signaling cascades mediated by allicin included PPARA, PPARG, FABP4, and FABP6 by cytoHubba and qPCR analysis. Results revealed that allicin activated the gene expression of PPARA and FABP6 and suppressed the gene expression of FABP4 and PPARG. Thus, the present study united the methods of network pharmacology and experimental validation to investigate the protein targets of allicin on PA-induced lipid metabolism disorders to supply a reference for related application for the first time.

Targeting redox heterogeneity to counteract drug tolerance in replicating Mycobacterium tuberculosis

The capacity of Mycobacterium tuberculosis (Mtb) to tolerate multiple antibiotics represents a major problem in tuberculosis (TB) management. Heterogeneity in Mtb populations is one of the factors that drives antibiotic tolerance during infection. However, the mechanisms underpinning this variation in bacterial population remain poorly understood. Here, we show that phagosomal acidification alters the redox physiology of Mtb to generate a population of replicating bacteria that display drug tolerance during infection. RNA sequencing of this redox-altered population revealed the involvement of iron-sulfur (Fe-S) cluster biogenesis, hydrogen sulfide (H₂S) gas, and drug efflux pumps in antibiotic tolerance. The fraction of the pH- and redox-dependent tolerant population increased when Mtb infected macrophages with actively replicating HIV-1, suggesting that redox heterogeneity could contribute to high rates of TB therapy failure during HIV-TB coinfection. Pharmacological inhibition of phagosomal acidification by the antimalarial drug chloroquine (CQ) eradicated drug-tolerant Mtb, ameliorated lung pathology, and reduced postchemotherapeutic relapse in in vivo models. The pharmacological profile of CQ (C _max and AUC_last) exhibited no major drug-drug interaction when coadministered with first line anti-TB drugs in mice. Our data establish a link between phagosomal pH, redox metabolism, and drug tolerance in replicating Mtb and suggest repositioning of CQ to shorten TB therapy and achieve a relapse-free cure.

Exploring Neuronal Vulnerability to Head Trauma Using a Whole Exome Approach

Brain injuries are associated with oxidative stress and a need to restore neuronal homeostasis. Mutations in ion channel genes, in particular CACNA1A, have been implicated in familial hemiplegic migraine (FHM) and in the development of concussion-related symptoms in response to trivial head trauma. The aim of this study was to explore the potential role of variants in other ion channel genes in the development of such responses. We conducted whole exome sequencing (WES) on16 individuals who developed a range of neurological and concussion-related symptoms following minor or trivial head injuries. All individuals were initially tested and shown to be negative for mutations in known FHM genes. Variants identified from the WES results were filtered to identify rare variants (minor allele frequency [MAF] <0.01) in genes related to neural processes as well as genes highly expressed in the brain using a combination of in silico prediction tools (SIFT, PolyPhen, PredictSNP, Mutation Taster, and Mutation Assessor). Rare (MAF <0.001) or novel heterozygous variants in 7 ion channel genes were identified in 37.5% (6/16) of the cases (CACNA1I, CACNA1C, ATP10A, ATP7B, KCNAB1, KCNJ10, and SLC26A4), rare variants in neurotransmitter genes were found in 2 cases (GABRG1 and GRIK1), and rare variants in 3 ubiquitin-related genes identified in 4 cases (SQSTM1, TRIM2, and HECTD1). In this study, the largest proportion of potentially pathogenic variants in individuals with severe responses to minor head trauma were identified in genes previously implicated in migraine and seizure-related autosomal recessive neurological disorders. Together with results implicating variants in the hemiplegic migraine genes, CACNA1A and ATP1A2, in severe head trauma response, our results support a role for heterozygous deleterious mutations in genes implicated in neurological dysfunction and potentially increasing the risk of poor response to trivial head trauma.

Contribution of brain pericytes in blood-brain barrier formation and maintenance: a transcriptomic study of cocultured human endothelial cells derived from hematopoietic stem cells

Formation, maintenance, and repair of the blood–brain barrier (BBB) are critical for central nervous system homeostasis. The interaction of endothelial cells (ECs) with brain pericytes is known to induce BBB characteristics in brain ECs during embryogenesis and can be used to differentiate human ECs from stem cell source in in vitro BBB models. However, the molecular events involved in BBB maturation are not fully understood. To this end, human ECs derived from hematopoietic stem cells were cultivated with either primary bovine or cell line-derived human brain pericytes to induce BBB formation. Subsequently, the transcriptomic profiles of solocultured vs. cocultured ECs were analysed over time by Massive Analysis of cDNA Ends (MACE) technology. This RNA sequencing method is a 3′-end targeted, tag-based, reduced representation transcriptome profiling technique, that can reliably quantify all polyadenylated transcripts including those with low expression. By analysing the generated transcriptomic profiles, we can explore the molecular processes responsible for the functional changes observed in ECs in coculture with brain pericytes (e.g. barrier tightening, changes in the expression of transporters and receptors). Our results identified several up- and downregulated genes and signaling pathways that provide a valuable data source to further delineate complex molecular processes that are involved in BBB formation and BBB maintenance. In addition, this data provides a source to identify novel targets for central nervous system drug delivery strategies.

Differential visible spectral influence on carbon metabolism in heterotrophic marine flavobacteria

The visible spectrum of solar radiation is known to stimulate photoheterotrophic bacterial carbon metabolism. However, its impact on 'strictly' heterotrophic bacteria remains less explored. Here, we show that heterotrophic flavobacteria exhibit enhanced uptake and mineralization of dissolved organic carbon with increasing wavelengths of visible light, without employing any 'known' light-harvesting mechanisms. RNA sequencing identified blue light as a major constraint in the extracellular enzymatic hydrolysis of polymeric carbohydrates and acquisition of sugars, despite acting as a stimulus for inorganic carbon sequestration. In contrast, green-red and continuous full-spectrum lights activated diverse hydrolytic enzymes and sugar transporters, but obstructed inorganic carbon fixation. This 'metabolic switching' was apparent through limited nutrient uptake, suppressed light-sensitivity, oxidative stress response and promotion of inorganic carbon sequestration pathways under blue light. The visible light impact on metabolism may be of significant ecological relevance as it appears to promote cell-mediated mineralization of organic carbon in 'green-colored' chlorophyll-rich copiotrophic coastal seawater and inorganic carbon sequestration in 'blue-colored' oligotrophic open ocean. Thus, a novel regulatory role played by light on heterotrophic metabolism and a hidden potential of flavobacteria to sense and respond differentially to monochromatic lights influencing marine carbon cycling were unraveled.

The draft genome of a new Verminephrobacter eiseniae strain: a nephridial symbiont of earthworms

Purpose

Verminephrobacter is a genus of symbiotic bacteria that live in the nephridia of earthworms. The bacteria are recruited during the embryonic stage of the worm and transferred from generation to generation in the same manner. The worm provides shelter and food for the bacteria. The bacteria deliver micronutrients to the worm. The present study reports the genome sequence assembly and annotation of a new strain of Verminephrobacter called Verminephrobacter eiseniae msu.

Methods

We separated the sequences of a new Verminephrobacter strain from the whole genome of Eisenia fetida using the sequence of V. eiseniae EF01-2, and the bacterial genome was assembled using the CLC Workbench. The de novo-assembled genome was annotated and analyzed for the protein domains, functions, and metabolic pathways. Besides, the multigenome comparison was performed to interpret the phylogenomic relationship of the strain with other proteobacteria.

Result

The FastqSifter sifted a total of 593,130 Verminephrobacter genomic reads. The de novo assembly of the reads generated 1832 contigs with a total genome size of 4.4 Mb. The Average Nucleotide Identity denoted the bacterium belongs to the species V. eiseniae, and the 16S rRNA analysis confirmed it as a new strain of V. eiseniae. The AUGUSTUS genome annotation predicted a total of 3809 protein-coding genes; of them, 3805 genes were identified from the homology search.

Conclusion

The bioinformatics analysis confirmed the bacterium is an isolate of V. eiseniae, and it was named Verminephrobacter eiseniae msu. The whole genome of the bacteria can be utilized as a useful resource to explore the area of symbiosis further.

netGO: R-Shiny package for network-integrated pathway enrichment analysis

Summary

We present an R-Shiny package, netGO, for novel network-integrated pathway enrichment analysis. The conventional Fisher’s exact test (FET) considers the extent of overlap between target genes and pathway gene-sets, while recent network-based analysis tools consider only network interactions between the two. netGO implements an intuitive framework to integrate both the overlap and networks into a single score, and adaptively resamples genes based on network degrees to assess the pathway enrichment. In benchmark tests for gene expression and genome-wide association study (GWAS) data, netGO captured the relevant gene-sets better than existing tools, especially when analyzing a small number of genes. Specifically, netGO provides user-interactive visualization of the target genes, enriched gene-set and their network interactions for both netGO and FET results for further analysis. For this visualization, we also developed a standalone R-Shiny package shinyCyJS to connect R-shiny and the JavaScript version of cytoscape.

Availability and implementation

netGO R-Shiny package is freely available from github, https://github.com/unistbig/netGO.

Supplementary information

Supplementary data are available at Bioinformatics online.

Predicting bacterial virulence factors - evaluation of machine learning and negative data strategies

Bacterial proteins dubbed virulence factors (VFs) are a highly diverse group of sequences, whose only obvious commonality is the very property of being, more or less directly, involved in virulence. It is therefore tempting to speculate whether their prediction, based on direct sequence similarity (seqsim) to known VFs, could be enhanced or even replaced by using machine-learning methods. Specifically, when trained on a large and diverse set of VFs, such may be able to detect putative, non-trivial characteristics shared by otherwise unrelated VF families and therefore better predict novel VFs with insignificant similarity to each individual family. We therefore first reassess the performance of dimer-based Support Vector Machines, as used in the widely used MP3 method, in light of seqsim-only and seqsim/dimer-hybrid classifiers. We then repeat the analysis with a novel, considerably more diverse data set, also addressing the important problem of negative data selection. Finally, we move on to the real-world use case of proteome-wide VF prediction, outlining different approaches to estimating specificity in this scenario. We find that direct seqsim is of unparalleled importance and therefore should always be exploited. Further, we observe strikingly low correlations between different feature and classifier types when ranking proteins by VF likeness. We therefore propose a 'best of each world' approach to prioritize proteins for experimental testing, focussing on the top predictions of each classifier. Further, classifiers for individual VF families should be developed.

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods

Studying sets of genomic features is increasingly popular in genomics, proteomics and metabolomics since analyzing at set level not only creates a natural connection to biological knowledge but also offers more statistical power. Currently, there are two gene-set testing approaches, self-contained and competitive, both of which have their advantages and disadvantages, but neither offers the final solution. We introduce simultaneous enrichment analysis (SEA), a new approach for analysis of feature sets in genomics and other omics based on a new unified null hypothesis, which includes the self-contained and competitive null hypotheses as special cases. We employ closed testing using Simes tests to test this new hypothesis. For every feature set, the proportion of active features is estimated, and a confidence bound is provided. Also, for every unified null hypotheses, a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$P$\end{document}-value is calculated, which is adjusted for family-wise error rate. SEA does not need to assume that the features are independent. Moreover, users are allowed to choose the feature set(s) of interest after observing the data. We develop a novel pipeline and apply it on RNA-seq data of dystrophin-deficient mdx mice, showcasing the flexibility of the method. Finally, the power properties of the method are evaluated through simulation studies.

Visualization and Analysis of Epiproteome Dynamics

The epiproteome describes the set of all post-translational modifications (PTMs) made to the proteins comprising a cell or organism. The extent of the epiproteome is still largely unknown; however, advances in experimental techniques are beginning to produce a deluge of data, tracking dynamic changes to the epiproteome in response to cellular stimuli. These data have potential to revolutionize our understanding of biology and disease. This review covers a range of recent visualization methods and tools developed specifically for dynamic epiproteome data sets. These methods have been designed primarily for data sets on phosphorylation, as this the most studied PTM; however, most of these methods are also applicable to other types of PTMs. Unfortunately, the currently available methods are often inadequate for existing data sets; thus, realizing the potential buried in epiproteome data sets will require new, tailored bioinformatics methods that will help researchers analyze, visualize, and interactively explore these complex data sets.

Analysis of the common bean (Phaseolus vulgaris L.) transcriptome regarding efficiency of phosphorus use

Common bean is a highly important food in tropical regions, where most production occurs on small farms with limited use of technology and, consequently, greater vulnerability to abiotic stresses such as nutritional stress. Usually phosphorus (P) is the most limiting nutrient for crop growth in these regions. The aim of this study was to characterize the gene expression profiles of the genotypes of common bean IAC Imperador (P-responsive) and DOR 364 (P-unresponsive) under different P concentrations using RNA-seq transcriptome sequencing technology. Plants were grown hydroponically, with application of two P concentrations (4.00 mg L^-1 restrictive level and 8.00 mg L^-1 control level). Differential expression analyses, annotation, and functional classification were performed comparing genotypes within each P rate administered and comparing each genotype response to the different P levels. Considering differential expression analyses within genotypes, IAC Imperador exhibited 1538 up-regulated genes under P restriction and 1679 up-regulated genes in the control, while DOR 364 exhibited 13 up-regulated genes in the control and only 2 up-regulated genes under P restriction, strongly corroborating P-unresponsiveness of this genotype. Genes related to phosphorus restriction were identified among the differentially expressed genes, including transcription factors such as WRKY, ERF, and MYB families, phosphatase related genes such as pyrophosphatase, acid phosphatase, and purple acid phosphatase, and phosphate transporters. The enrichment test for the P restriction treatment showed 123 enriched gene ontologies (GO) for IAC Imperador, while DOR 364 enriched only 24. Also, the enriched GO correlated with P metabolism, compound metabolic processes containing phosphate, nucleoside phosphate binding, phosphorylation, and also response to stresses. Thus, this study proved to be informative to phosphorus limitation in common bean showing global changes at transcript level.

Data on genome annotation and analysis of earthworm Eisenia fetida

The present article reports the complete draft genome annotation of earthworm Eisenia fetida, obtained from the manuscript entitled "Timing and Scope of Genomic Expansion within Annelida: Evidence from Homeoboxes in the Genome of the Earthworm E. fetida" (Zwarycz et al., 2015) and provides the data on the repetitive elements, protein coding genes and noncoding RNAs present in the genome dataset of the species. The E. fetida protein coding genes were predicted from AUGUSTUS gene prediction and subsequently annotated based on their sequence similarity, Gene Ontology (GO) functional terms, InterPro domains, Clusters of Orthologous Groups (COGs) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways information. The genome wide comparison of orthologous clusters and phylogenomic analysis of the core genes were performed to understand the events of genome evolution and genomic diversity between E. fetida and its related metazoans. In addition, the genome dataset was screened to identify the crucial stem cell markers, regeneration specific genes and immune-related genes and their functionally enriched GO terms were predicted from Fisher׳s enrichment analysis. The E. fetida genome annotation data containing the GFF (general feature format) annotation file, predicted coding gene sequences and translated protein sequences were deposited to the figshare repository under the DOI: https://doi.org/10.6084/m9.figshare.6142322.v1.

Using predictive specificity to determine when gene set analysis is biologically meaningful

Gene set analysis, which translates gene lists into enriched functions, is among the most common bioinformatic methods. Yet few would advocate taking the results at face value. Not only is there no agreement on the algorithms themselves, there is no agreement on how to benchmark them. In this paper, we evaluate the robustness and uniqueness of enrichment results as a means of assessing methods even where correctness is unknown. We show that heavily annotated (‘multifunctional’) genes are likely to appear in genomics study results and drive the generation of biologically non-specific enrichment results as well as highly fragile significances. By providing a means of determining where enrichment analyses report non-specific and non-robust findings, we are able to assess where we can be confident in their use. We find significant progress in recent bias correction methods for enrichment and provide our own software implementation. Our approach can be readily adapted to any pre-existing package.

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies

BACKGROUND

Biologists aim to understand the genetic background of diseases, metabolic disorders or any other genetic condition. Microarrays are one of the main high-throughput technologies for collecting information about the behaviour of genetic information on different conditions. In order to analyse this data, clustering arises as one of the main techniques used, and it aims at finding groups of genes that have some criterion in common, like similar expression profile. However, the problem of finding groups is normally multi dimensional, making necessary to approach the clustering as a multi-objective problem where various cluster validity indexes are simultaneously optimised. They are usually based on criteria like compactness and separation, which may not be sufficient since they can not guarantee the generation of clusters that have both similar expression patterns and biological coherence.

METHOD

We propose a Multi-Objective Clustering algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) to find clusters of genes with high levels of co-expression, biological coherence, and also good compactness and separation. Cluster quality indexes are used to optimise simultaneously gene relationships at expression level and biological functionality. Our proposal also includes intensification and diversification strategies to improve the search process.

RESULTS

The effectiveness of the proposed algorithm is demonstrated on four publicly available datasets. Comparative studies of the use of different objective functions and other widely used microarray clustering techniques are reported. Statistical, visual and biological significance tests are carried out to show the superiority of the proposed algorithm.

CONCLUSIONS

Integrating a-priori biological knowledge into a multi-objective approach and using intensification and diversification strategies allow the proposed algorithm to find solutions with higher quality than other microarray clustering techniques available in the literature in terms of co-expression, biological coherence, compactness and separation.

Molecular activities and ligand-binding specificities of StAR-related lipid transfer domains: exploring integrated in silico methods and ensemble-docking approaches

In this study, cholesterol biotransformation gene-set of human steroidogenic acute regulatory protein-related lipid transfer (START) domains were evaluated from high-throughput gene screening approaches. It was shown that STARD1, STARD3 and STARD4 proteins are better effective transporters of cholesterol than STARD5 and STARD6 domains. Docking studies show a strong agreement with gene ontology enrichment data. According to both complementary strategies, it was found that only STARD1, STARD3 and STARD4 are potentially involved in cholesterol biotransformation in mitochondria through Ω1-loop of C-terminal α4-helical domain. Ensemble docking assessment for a set of selected chemicals of protein-chemical networks has shown possible binding probabilities with START domains. Among those, reproductive toxicity evoked drugs (mifepristone), insecticides (rotenone), tobacco pulmonary carcinogens (benzo(a)pyrene) and endocrine disruptor chemicals (EDCs) including perfluorooctanesulfonic acid (PFOS) and aflatoxin B₁ (AFB₁) potentially bound with novel hotspot residues of the α4-helical domain. Compound representation space and clustering approaches reveal that the START proteins show more sensitivity with these lead scaffolds, so they could provide probable barrier assets in cholesterol and steroidogenic acute regulatory (StAR) binding and leads adverse consequences in steroidogenesis. These findings indicate potential START domains and their binding levels with toxic chemicals; sorted viewpoints could be useful as a promising way to identify chemicals with related steroidogenisis impacts on human health.

Analysis of Annotation and Differential Expression Methods used in RNA-seq Studies in Crustacean Systems

In the field of crustacean biology, usage of RNA-seq to study gene expression is rapidly growing. Major advances in sequencing technology have contributed to the ability to examine complex patterns of genome activity in a wide range of organisms that are extensively used for comparative physiology, ecology and evolution, environmental monitoring, and commercial aquaculture. Relative to insect and vertebrate model organisms, however, information on the organization of crustacean genomes is virtually nonexistent, making de novo transcriptome assembly, annotation and quantification problematic and challenging. We present here a summary of the methodologies and software analyses employed in 23 recent publications, which describe de novo transcriptome assembly, annotation, and differential gene expression in a variety of crustacean experimental systems. We focus on establishing a series of best practices that will allow for investigators to produce datasets that are understandable, reproducible, and of general utility for related analyses and cross-study comparisons.

Genomic and Genotypic Characterization of Cylindrospermopsis raciborskii: Toward an Intraspecific Phylogenetic Evaluation by Comparative Genomics

Cylindrospermopsis raciborskii is a freshwater cyanobacterial species with increasing bloom reports worldwide that are likely due to factors related to climate change. In addition to the deleterious effects of blooms on aquatic ecosystems, the majority of ecotypes can synthesize toxic secondary metabolites causing public health issues. To overcome the harmful effects of C. raciborskii blooms, it is important to advance knowledge of diversity, genetic variation, and evolutionary processes within populations. An efficient approach to exploring this diversity and understanding the evolution of C. raciborskii is to use comparative genomics. Here, we report two new draft genomes of C. raciborskii (strains CENA302 and CENA303) from Brazilian isolates of different origins and explore their molecular diversity, phylogeny, and evolutionary diversification by comparing their genomes with sequences from other strains available in public databases. The results obtained by comparing seven C. raciborskii and the Raphidiopsis brookii D9 genomes revealed a set of conserved core genes and a variable set of accessory genes, such as those involved in the biosynthesis of natural products, heterocyte glycolipid formation, and nitrogen fixation. Gene cluster arrangements related to the biosynthesis of the antifungal cyclic glycosylated lipopeptide hassallidin were identified in four C. raciborskii genomes, including the non-nitrogen fixing strain CENA303. Shifts in gene clusters involved in toxin production according to geographic origins were observed, as well as a lack of nitrogen fixation (nif) and heterocyte glycolipid (hgl) gene clusters in some strains. Single gene phylogeny (16S rRNA sequences) was congruent with phylogeny based on 31 concatenated housekeeping protein sequences, and both analyses have shown, with high support values, that the species C. raciborskii is monophyletic. This comparative genomics study allowed a species-wide view of the biological diversity of C. raciborskii and in some cases linked genome differences to phenotype.

SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data

Background

Protein or nucleic acid sequences contain a multitude of associated annotations representing continuous sequence elements (CSEs). Comparing these CSEs is needed, whenever we want to match identical annotations or integrate distinctive ones. Currently, there is no ready-to-use software available that provides comprehensive statistical readout for comparing two annotations of the same type with each other, which can be adapted to the application logic of the scientific question.

Results

We have developed a method, SLALOM (for StatisticaL Analysis of Locus Overlap Method), to perform comparative analysis of sequence annotations in a highly flexible way. SLALOM implements six major operation modes and a number of additional options that can answer a variety of statistical questions about a pair of input annotations of a given sequence collection. We demonstrate the results of SLALOM on three different examples from biology and economics and compare our method to already existing software. We discuss the importance of carefully choosing the application logic to address specific scientific questions.

Conclusion

SLALOM is a highly versatile, command-line based method for comparing annotations in a collection of sequences, with a statistical read-out for performance evaluation and benchmarking of predictors and gene annotation pipelines. Abstraction from sequence content even allows SLALOM to compare other kinds of positional data including, for example, data coming from time series.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2020-x) contains supplementary material, which is available to authorized users.

GSEH: A Novel Approach to Select Prostate Cancer-Associated Genes Using Gene Expression Heterogeneity

When a gene shows varying levels of expression among normal people but similar levels in disease patients or shows similar levels of expression among normal people but different levels in disease patients, we can assume that the gene is associated with the disease. By utilizing this gene expression heterogeneity, we can obtain additional information that abets discovery of disease-associated genes. In this study, we used collaborative filtering to calculate the degree of gene expression heterogeneity between classes and then scored the genes on the basis of the degree of gene expression heterogeneity to find "differentially predicted" genes. Through the proposed method, we discovered more prostate cancer-associated genes than 10 comparable methods. The genes prioritized by the proposed method are potentially significant to biological processes of a disease and can provide insight into them.

Annotation of nerve cord transcriptome in earthworm Eisenia fetida

In annelid worms, the nerve cord serves as a crucial organ to control the sensory and behavioral physiology. The inadequate genome resource of earthworms has prioritized the comprehensive analysis of their transcriptome dataset to monitor the genes express in the nerve cord and predict their role in the neurotransmission and sensory perception of the species. The present study focuses on identifying the potential transcripts and predicting their functional features by annotating the transcriptome dataset of nerve cord tissues prepared by Gong et al., 2010 from the earthworm Eisenia fetida. Totally 9762 transcripts were successfully annotated against the NCBI nr database using the BLASTX algorithm and among them 7680 transcripts were assigned to a total of 44,354 GO terms. The conserve domain analysis indicated the over representation of P-loop NTPase domain and calcium binding EF-hand domain. The COG functional annotation classified 5860 transcript sequences into 25 functional categories. Further, 4502 contig sequences were found to map with 124 KEGG pathways. The annotated contig dataset exhibited 22 crucial neuropeptides having considerable matches to the marine annelid Platynereis dumerilii, suggesting their possible role in neurotransmission and neuromodulation. In addition, 108 human stem cell marker homologs were identified including the crucial epigenetic regulators, transcriptional repressors and cell cycle regulators, which may contribute to the neuronal and segmental regeneration. The complete functional annotation of this nerve cord transcriptome can be further utilized to interpret genetic and molecular mechanisms associated with neuronal development, nervous system regeneration and nerve cord function.

Molecular dissection of transcriptional reprogramming of steviol glycosides synthesis in leaf tissue during developmental phase transitions in Stevia rebaudiana Bert

Stevia is a natural source of commercially important steviol glycosides (SGs), which share biosynthesis route with gibberellic acids (GAs) through plastidal MEP and cytosolic MVA pathways. Ontogeny-dependent deviation in SGs biosynthesis is one of the key factor for global cultivation of Stevia, has not been studied at transcriptional level. To dissect underlying molecular mechanism, we followed a global transcriptome sequencing approach and generated more than 100 million reads. Annotation of 41,262 de novo assembled transcripts identified all the genes required for SGs and GAs biosynthesis. Differential gene expression and quantitative analysis of important pathway genes (DXS, HMGR, KA13H) and gene regulators (WRKY, MYB, NAC TFs) indicated developmental phase dependent utilization of metabolic flux between SGs and GAs synthesis. Further, identification of 124 CYPs and 45 UGTs enrich the genomic resources, and their PPI network analysis with SGs/GAs biosynthesis proteins identifies putative candidates involved in metabolic changes, as supported by their developmental phase-dependent expression. These putative targets can expedite molecular breeding and genetic engineering efforts to enhance SGs content, biomass and yield. Futuristically, the generated dataset will be a useful resource for development of functional molecular markers for diversity characterization, genome mapping and evolutionary studies in Stevia.

Discovery of biomarkers in rare diseases: innovative approaches by predictive and personalized medicine

There are more than 8000 rare diseases (RDs) that affect >5 % of the world’s population. Many of the RDs have no effective treatment and lack of knowledge creates delayed diagnosis making management difficult. The emerging concept of the personalized medicine allows for early screening, diagnosis, and individualized treatment of human diseases. In this context, the discovery of biomarkers in RDs will be of prime importance to enable timely prevention and effective treatment. Since 80 % of RDs are of genetic origin, identification of new genes and causative mutations become valuable biomarkers. Furthermore, dynamic markers such as expressed genes, metabolites, and proteins are also very important to follow prognosis and response the therapy. Recent advances in omics technologies and their use in combination can define pathophysiological pathways that can be drug targets. Biomarker discovery and their use in diagnosis in RDs is a major pillar in RD research.

Experimental Evolution Reveals Interplay between Sch9 and Polyploid Stability in Yeast

Polyploidization has crucial impacts on the evolution of different eukaryotic lineages including fungi, plants and animals. Recent genome data suggest that, for many polyploidization events, all duplicated chromosomes are maintained and genome reorganizations occur much later during evolution. However, newly-formed polyploid genomes are intrinsically unstable and often quickly degenerate into aneuploidy or diploidy. The transition between these two states remains enigmatic. In this study, laboratory evolution experiments were conducted to investigate this phenomenon. We show that robust tetraploidy is achieved in evolved yeast cells by increasing the abundance of Sch9—a protein kinase activated by the TORC1 (Target of Rapamycin Complex 1) and other signaling pathways. Overexpressing SCH9, but not TOR1, allows newly-formed tetraploids to exhibit evolved phenotypes and knocking out SCH9 diminishes the evolved phenotypes. Furthermore, when cells were challenged with conditions causing ancestral cells to evolve aneuploidy, tetraploidy was maintained in the evolved lines. Our results reveal a determinant role for Sch9 during the early stage of polyploid evolution.

Polyploidy is frequently observed in eukaryotes, including in human liver cells and cancer. Evolutionary studies also suggest that polyploidy has contributed to species diversification and novel adaptation in fungi, plants and animals. However, artificially-constructed polyploids often display chromosome instability and quickly convert to aneuploids. This phenomenon conflicts with observations that many species derived from ancient genome duplications have maintained the extra number of chromosomes following polyploidization. What happened during the early stages of these polyploidy events that stabilized the duplicated genomes? We used laboratory evolution experiments to investigate this process. After being propagated in a rich medium at 23°C for 1000 generations, newly-constructed tetraploid yeast cells had evolved stable genomes. In addition, evolved cells acquired resistance to stresses specific to tetraploids and exhibited a more diploid-like transcriptome profile. Further analyses indicated that Sch9—the functional ortholog of mammalian S6 kinase involved in protein homeostasis, G1 progression, stress response and nutrient signaling—contributed to the evolved phenotypes. Evolved cells increased the protein abundance and stability of Sch9. Reconstitution experiments showed that overexpression of SCH9 enabled ancestral cells to display the evolved phenotypes and eliminating SCH9 diminished the evolved phenotypes. Finally, we show that evolved cells were able to maintain their genomes even under a condition that causes newly-formed tetraploids to evolve aneuploidy. Our results reveal that at the early stages after genome duplication, stable polyploidy can be achieved by fine-tuning a conserved key regulator coordinating multiple cellular processes.

A Comparison of Resources for the Annotation of a De Novo Assembled Transcriptome in the Molting Gland (Y-Organ) of the Blackback Land Crab, Gecarcinus lateralis

Next-generation sequencing technologies are revolutionizing crustacean biology. De novo assembly of RNA sequencing (RNA-seq) data allows researchers to catalog and quantify genes expressed in tissues of a species that lacks a complete genome sequence. RNA-seq has become an important tool for understanding phenotypic plasticity and the responses of organisms to environmental cues. However, there are challenges with identification of assembled contiguous sequences (contigs) without a reference genome. Thus, the selection of resources for annotating contigs is critical for the downstream analysis of gene functions. A de novo-assembled transcriptome of the Gecarcinus lateralis molting gland, or Y-organ (YO), was used to compare two functional annotation packages, Trinotate and Blast2GO. The assembled transcriptome contained 229,278 contigs derived from YOs from animals in intermolt, premolt (early, mid, and late), and postmolt stages. Gene identification using BLAST against four databases and functional annotation using Gene Ontologies were conducted. The analysis revealed two major limitations of de novo assembly: (1) assembly using Trinity generates redundant contigs and (2) transcripts that encode protein isoforms are not distinguished with current computational tools. It is recommended that the NCBI Non-Redundant, SwissProt, TrEMBL, and Uniref90 databases be used to maximize gene identification. Trinotate is preferred for assigning functions to identified genes, as the package uses multiple databases for annotation. The differences between packages to generate Gene Ontology (GO) terms are attributed to the databases used for inputs: Trinotate uses both Pfam and BLAST databases, while Blast2GO uses only the BLAST database. InterProScan was used to verify the GO terms assigned via BLAST. A comprehensive annotation of de novo assembled transcriptome is necessary for the downstream analysis of differentially expressed transcripts in the YO over the molt cycle.

Integrated analysis of proteome and transcriptome changes in the mucopolysaccharidosis type VII mouse hippocampus

Mucopolysaccharidosis type VII (MPS VII) is a lysosomal storage disease caused by the deficiency of β-glucuronidase. In this study, we compared the changes relative to normal littermates in the proteome and transcriptome of the hippocampus in the C57Bl/6 mouse model of MPS VII, which has well-documented histopathological and neurodegenerative changes. A completely different set of significant changes between normal and MPS VII littermates were found in each assay. Nevertheless, the functional annotation terms generated by the two methods showed agreement in many of the processes, which also corresponded to known pathology associated with the disease. Additionally, assay-specific changes were found, which in the proteomic analysis included mitochondria, energy generation, and cytoskeletal differences in the mutant, while the transcriptome differences included immune, vesicular, and extracellular matrix changes. In addition, the transcriptomic changes in the mutant hippocampus were concordant with those in a MPS VII mouse caused by the same mutation but on a different background inbred strain.

Finding New Order in Biological Functions from the Network Structure of Gene Annotations

The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to one another. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternative approach for grouping functional terms that captures intrinsic functional relationships that are not evident in the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our approach connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different sources. We show that grouping terms by this alternate scheme provides a new framework with which to describe and predict the functions of experimentally identified sets of genes.

Visual annotation display (VLAD): a tool for finding functional themes in lists of genes

Experiments that employ genome scale technology platforms frequently result in lists of tens to thousands of genes with potential significance to a specific biological process or disease. Searching for biologically relevant connections among the genes or gene products in these lists is a common data analysis task. We have implemented a software application for uncovering functional themes in sets of genes based on their annotations to bio-ontologies, such as the gene ontology and the mammalian phenotype ontology. The application, called VisuaL Annotation Display (VLAD), performs a statistical analysis to test for the enrichment of ontology terms in a set of genes submitted by a researcher. The results for each analysis using VLAD includes a table of ontology terms, sorted in decreasing order of significance. Each row contains the term, statistics such as the number of annotated terms, the p value, etc., and the symbols of annotated genes. An accompanying graphical display shows portions of the ontology hierarchy, where node sizes are scaled based on p values. Although numerous ontology term enrichment programs already exist, VLAD is unique in that it allows users to upload their own annotation files and ontologies for customized term enrichment analyses, supports the analysis of multiple gene sets at once, provides interfaces to customize graphical output, and is tightly integrated with functional and biological details about mouse genes in the Mouse Genome Informatics (MGI) database. VLAD is available as a web-based application from the MGI web site (http://proto.informatics.jax.org/prototypes/vlad/).

MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis

Background

In genome-wide studies, over-representation analysis (ORA) against a set of genes is an essential step for biological interpretation. Many gene annotation resources and software platforms for ORA have been proposed. Recently, Medical Subject Headings (MeSH) terms, which are annotations of PubMed documents, have been used for ORA. MeSH enables the extraction of broader meaning from the gene lists and is expected to become an exhaustive annotation resource for ORA. However, the existing MeSH ORA software platforms are still not sufficient for several reasons.

Results

In this work, we developed an original MeSH ORA framework composed of six types of R packages, including MeSH.db, MeSH.AOR.db, MeSH.PCR.db, the org.MeSH.XXX.db-type packages, MeSHDbi, and meshr.

Conclusions

Using our framework, users can easily conduct MeSH ORA. By utilizing the enriched MeSH terms, related PubMed documents can be retrieved and saved on local machines within this framework.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0453-z) contains supplementary material, which is available to authorized users.

Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity

BACKGROUND

Communalities between large sets of genes obtained from high-throughput experiments are often identified by searching for enrichments of genes with the same Gene Ontology (GO) annotations. The GO analysis tools used for these enrichment analyses assume that GO terms are independent and the semantic distances between all parent-child terms are identical, which is not true in a biological sense. In addition these tools output lists of often redundant or too specific GO terms, which are difficult to interpret in the context of the biological question investigated by the user. Therefore, there is a demand for a robust and reliable method for gene categorization and enrichment analysis.

RESULTS

We have developed Categorizer, a tool that classifies genes into user-defined groups (categories) and calculates p-values for the enrichment of the categories. Categorizer identifies the biologically best-fit category for each gene by taking advantage of a specialized semantic similarity measure for GO terms. We demonstrate that Categorizer provides improved categorization and enrichment results of genetic modifiers of Huntington's disease compared to a classical GO Slim-based approach or categorizations using other semantic similarity measures.

CONCLUSION

Categorizer enables more accurate categorizations of genes than currently available methods. This new tool will help experimental and computational biologists analyzing genomic and proteomic data according to their specific needs in a more reliable manner.

Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS

Background

The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms.

Results

In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants.

Conclusion

We conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome sequences, providing initial clues about regulatory and metabolic network topology, and revealing relationships that are not immediately obvious from traditional homology-based annotations.