Use and misuse of the gene ontology annotations

Proceedings of the National Academy of Sciences, 2014

Proteomics techniques generate an avalanche of data and promise to satisfy biologists' long-held desire to measure absolute protein abundances on a genome-wide scale. However, can this knowledge be translated into a clearer picture of how cells invest their protein resources? This article aims to give a broad perspective on the composition of proteomes as gleaned from recent quantitative proteomics studies. We describe proteomaps, an approach for visualizing the composition of proteomes with a focus on protein abundances and functions. In proteomaps, each protein is shown as a polygon-shaped tile, with an area representing protein abundance. Functionally related proteins appear in adjacent regions. General trends in proteomes, such as the dominance of metabolism and protein production, become easily visible. We make interactive visualizations of published proteome datasets accessible at www.proteomaps.net. We suggest that evaluating the way protein resources are allocated by various organisms and cell types in different conditions will sharpen our understanding of how and why cells regulate the composition of their proteomes.

BMC Genomics, 2010

Background: The European sea bass (Dicentrarchus labrax) is a marine fish of great importance for fisheries and aquaculture. Functional genomics offers the possibility to discover the molecular mechanisms underlying productive traits in farmed fish, and a step towards the application of marker assisted selection methods in this species. To this end, we report here on the development of an oligo DNA microarray for D. labrax.

PLoS ONE, 2012

Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a 'Cancer Genes Network', a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.

PLoS ONE, 2011

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call 'trait prioritized sub-networks.' As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn's disease. NIMMI prioritized sub-networks involved in B-and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn's disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.

Frontiers in Plant Science, 2015

ME (2015) Identifying the ionically bound cell wall and intracellular glycoside hydrolases in late growth stage Arabidopsis stems: implications for the genetic engineering of bioenergy crops. Front. Plant Sci. 6:315.

PLoS Computational Biology, 2011

The genetic dissection of the phenotypes associated with Williams-Beuren Syndrome (WBS) is advancing thanks to the study of individuals carrying typical or atypical structural rearrangements, as well as in vitro and animal studies. However, little is known about the global dysregulations caused by the WBS deletion. We profiled the transcriptomes of skin fibroblasts from WBS patients and compared them to matched controls. We identified 868 differentially expressed genes that were significantly enriched in extracellular matrix genes, major histocompatibility complex (MHC) genes, as well as genes in which the products localize to the postsynaptic membrane. We then used public expression datasets from human fibroblasts to establish transcription modules, sets of genes coexpressed in this cell type. We identified those sets in which the average gene expression was altered in WBS samples. Dysregulated modules are often interconnected and share multiple common genes, suggesting that intricate regulatory networks connected by a few central genes are disturbed in WBS. This modular approach increases the power to identify pathways dysregulated in WBS patients, thus providing a testable set of additional candidates for genes and their interactions that modulate the WBS phenotypes.

PLoS Computational Biology, 2009

The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content.

BMC Bioinformatics, 2011

Background: A major challenge in genomic research is identifying significant biological processes and generating new hypotheses from large gene sets. Gene sets often consist of multiple separate biological pathways, controlled by distinct regulatory mechanisms. Many of these pathways and the associated regulatory mechanisms might be obscured by a large number of other significant processes and thus not identified as significant by standard gene set enrichment analysis tools. Results: We present a novel method called Independent Enrichment Analysis (IEA) and software TAFFEL that eases the task by clustering genes to subgroups using Gene Ontology categories and transcription regulators. IEA indicates transcriptional regulators putatively controlling biological functions in studied condition.

International journal of molecular sciences, 2011

Since the onset of antiviral therapy, viral resistance has compromised the clinical value of small-molecule drugs targeting pathogen components. As intracellular parasites, viruses complete their life cycle by hijacking a multitude of host-factors. Aiming at the latter rather than the pathogen directly, host-directed antiviral therapy has emerged as a concept to counteract evolution of viral resistance and develop broad-spectrum drug classes. This approach is propelled by bioinformatics analysis of genome-wide screens that greatly enhance insights into the complex network of host-pathogen interactions and generate a shortlist of potential gene targets from a multitude of candidates, thus setting the stage for a new era of rational identification of drug targets for host-directed antiviral therapies. With particular emphasis on human immunodeficiency virus and influenza virus, two major human pathogens, we review screens employed to elucidate host-pathogen interactions and discuss th...

Journal of bioinformatics and computational biology, 2013

There is a prominent trend to augment and improve the formality of biomedical ontologies. For example, this is shown by the current effort on adding description logic axioms, such as disjointness. One of the key ontology applications that can take advantage of this effort is the conceptual (functional) similarity measurement. The presence of description logic axioms in biomedical ontologies make the current structural or extensional approaches weaker and further away from providing sound semantics-based similarity measures. Although beneficial in small ontologies, the exploration of description logic axioms by semantics-based similarity measures is computational expensive. This limitation is critical for biomedical ontologies that normally contain thousands of concepts. Thus in the process of gaining their rightful place, biomedical functional similarity measures have to take the journey of finding how this rich and powerful knowledge can be fully explored while keeping feasible com...

Journal of Biomedical Semantics, 2012

Background: With the development of high throughput methods of gene analyses, there is a growing need for mining tools to retrieve relevant articles in PubMed. As PubMed grows, literature searches become more complex and time-consuming. Automated search tools with good precision and recall are necessary. We developed GO2PUB to automatically enrich PubMed queries with gene names, symbols and synonyms annotated by a GO term of interest or one of its descendants. Results: GO2PUB enriches PubMed queries based on selected GO terms and keywords. It processes the result and displays the PMID, title, authors, abstract and bibliographic references of the articles. Gene names, symbols and synonyms that have been generated as extra keywords from the GO terms are also highlighted. GO2PUB is based on a semantic expansion of PubMed queries using the semantic inheritance between terms through the GO graph. Two experts manually assessed the relevance of GO2PUB, GoPubMed and PubMed on three queries about lipid metabolism. Experts' agreement was high (kappa=0.88). GO2PUB returned 69% of the relevant articles, GoPubMed: 40% and PubMed: 29%. GO2PUB and GoPubMed have 17% of their results in common, corresponding to 24% of the total number of relevant results. 70% of the articles returned by more than one tool were relevant. 36% of the relevant articles were returned only by GO2PUB, 17% only by GoPubMed and 14% only by PubMed. For determining whether these results can be generalized, we generated twenty queries based on random GO terms with a granularity similar to those of the first three queries and compared the proportions of GO2PUB and GoPubMed results. These were respectively of 77% and 40% for the first queries, and of 70% and 38% for the random queries. The two experts also assessed the relevance of seven of the twenty queries (the three related to lipid metabolism and four related to other domains). Expert agreement was high (0.93 and 0.8). GO2PUB and GoPubMed performances were similar to those of the first queries. Conclusions: We demonstrated that the use of genes annotated by either GO terms of interest or a descendant of these GO terms yields some relevant articles ignored by other tools. The comparison of GO2PUB, based on semantic expansion, with GoPubMed, based on text mining techniques, showed that both tools are complementary. The analysis of the randomly-generated queries suggests that the results obtained about lipid metabolism can be generalized to other biological processes. GO2PUB is available at http://go2pub.genouest.org.

PLoS ONE, 2014

Background: Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity.

BMC Bioinformatics, 2012

Background: Most major genome projects and sequence databases provide a GO annotation of their data, either automatically or through human annotators, creating a large corpus of data written in the language of GO. Texts written in natural language show a statistical power law behaviour, Zipf's law, the exponent of which can provide useful information on the nature of the language being used. We have therefore explored the hypothesis that collections of GO annotations will show similar statistical behaviours to natural language.

Scientific reports, 2015

Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets ...

Genes

The ectoparasite protozoan Amyloodinium ocellatum (AO) is the etiological agent of amyloodiniosis in European seabass (Dicentrarchus labrax) (ESB). There is a lack of information about basic molecular data on AO biology and its interaction with the host. Therefore, de novo transcriptome sequencing of AO tomonts was performed. AO trophonts were detached from infested ESB gills, and quickly becoming early tomonts were purified by Percoll® density gradient. Tomont total RNA was processed and quality was assessed immediately. cDNA libraries were constructed using TruSeq® Stranded mRNA kit and sequenced using Illumina sequencer. CLC assembly was used to generate the Transcriptome assembly of AO tomonts. Out of 48,188 contigs, 56.12% belong to dinophyceae wherein Symbiodinium microadriaticum had 94.61% similarity among dinophyceae. Functional annotations of contigs indicated that 12,677 had associated GO term, 9005 with KEGG term. The contigs belonging to dinophyceae resulted in the detec...

Nucleic acids research, 2017

ANISEED (www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-bi...

Frontiers in microbiology, 2018

While in eukaryotes acetylation/deacetylation regulation exerts multiple pleiotropic effects, in it seems to be more limited and less known. Hence, we aimed to progress in the characterization of this regulation by dealing with three convergent aspects: the effector enzymes involved, the master regulator CRP, and the dependence on glucose availability. The transcriptional response of BW25113 was analyzed across 14 relevant scenarios. These conditions arise when the wild type and four isogenic mutants (defective in deacetylase CobB, defective in N(ε)-lysine acetyl transferase PatZ, Q- and R-type mutants of protein CRP) are studied under three levels of glucose availability (glucose-limited chemostat and glucose-excess or glucose-exhausted in batch culture). The Q-type emulates a permanent stage of CRP, whereas the R-type emulates a permanent stage of CRP. The data were analyzed by an optimized factorial microarray method (Q-GDEMAR). (a) By analyzing one mutant against the other, we w...

Frontiers in physiology, 2015

Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms. Despite their wide employment, pathway analysis foundations and overall background may not be fully understood, leading to misinterpretation of analysis results. This review attempts to comprise the fundamental knowledge to take into consideration when using pathway analysis as a hypothesis generation tool. We discuss the key elements that are part of these methodologies, their capabilities and current deficiencies. We also present an overview of current and all-time popular methods, highlighting different classes across them. In doing so, we show the exploding diversity of methods that pathway analysis encompasses, point out commonly overlooked caveats, and direct attention t...

PloS one, 2016

Iron (Fe) is an essential mineral for metabolism and plays a central role in a range of biochemical processes. Therefore, this study aimed to identify differentially expressed (DE) genes and metabolic pathways in Longissimus dorsi (LD) muscle from cattle with divergent iron content, as well as to investigate the likely role of these DE genes in biological processes underlying beef quality parameters. Samples for RNA extraction for sequencing and iron, copper, manganese, and zinc determination were collected from LD muscles at slaughter. Eight Nelore steers, with extreme genomic estimated breeding values for iron content (Fe-GEBV), were selected from a reference population of 373 animals. From the 49 annotated DE genes (FDR<0.05) found between the two groups, 18 were up-regulated and 31 down-regulated for the animals in the low Fe-GEBV group. The functional enrichment analyses identified several biological processes, such as lipid transport and metabolism, and cell growth. Lipid m...

BMC systems biology, 2018

Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. This framework will help health researchers plan and perform mu...

Crop Science

Meeting the challenge of food and nutritional security requires ongoing innovation, particularly in managing dietary nutritional information for pre-breeding analysis, selection, and cultivation of specific food crops and cultivars. At present, the ability to compare the relative nutritional value of crops is limited, with data management systems for most crops often inconsistent and poorly integrated. Here, we review generic efforts to standardize the description and management of crop trait data and discuss several issues currently constraining their exchange and comparison, with a focus on knowledge representation related to dietary nutrition. These issues include lack of consistency within or between crop specific databases, as well as limited data standardization and interoperability. At present, the use of common descriptors or controlled vocabularies between crops is fragmentary, with only partial implementation or uptake of formal ontologies, particularly for dietary nutritional composition. Although development of the existing Crop Ontology (CO) system has improved data sharing and reuse, it represents only a limited set of trait classes and crops. We identify the need for more robust and generic ontologies, particularly those that may address crop contributions to human dietary nutrition. We propose development of a Crop Dietary Nutrition Ontology (CDNO) as a robust structured controlled vocabulary for dietary nutritional composition and function, and provide examples of specific use cases and different end users who would benefit from using CDNO terms in their database searches. This development is likely to transform the way in which crops may be compared in terms of optimal dietary nutritional values.

BMC Bioinformatics, 2013

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Frontiers in Physiology, 2016

Human Gingival Mesenchymal Stem Cells (hGMSCs) are multipotential cells that can expand and differentiate in culture under specific and standardized conditions. In the present study, we have investigated whether in vitro pre-treatment of hGMSCs with Cannabidiol (CBD) can influence their expression profile, improving the therapeutic potential of this cell culture. Following CBD treatment (5 µM) for 24 h, gene expression analysis through Next Generation Sequencing (NGS) has revealed several genes differentially expressed between CBD-treated hGMSCs (CBD-hGMSCs) and control cells (CTR-hGMSCs) that were linked to inflammation and apoptosis. In particular, we have demonstrated that CBD treatment in hGMSCs prevented the activation of the NALP3-inflammasome pathway by suppressing the levels of NALP3, CASP1, and IL18, and in parallel, inhibited apoptosis, as demonstrated by the suppression of Bax. CBD treatment was also able to modulate the expression of the well-known mesenchymal stem cell markers (CD13, CD29, CD73, CD44, CD90, and CD166), and other surface antigens. Specifically, CBD led to the downregulation of genes codifying for antigens involved in the activation of the immune system (CD109, CD151, CD40, CD46, CD59, CD68, CD81, CD82, CD99), while it led to the upregulation of those implicated in the inhibition of the immune responses (CD47, CD55, CD276). In conclusion, the present study will provide a new simple and reproducible method for preconditioning hGMSCs with CBD, before transplantation, as an interesting strategy for improving the hGMSCs molecular phenotype, reducing the risk of immune or inflammatory reactions in the host, and in parallel, for increasing their survival and thus, their long-term therapeutic efficacy.

NPJ systems biology and applications, 2018

Molecular interaction networks are routinely used to map the organization of cellular function. Edges represent interactions between genes, proteins, or metabolites. However, in living cells, molecular interactions are dynamic, necessitating context-dependent models. Contextual information can be integrated into molecular interaction networks through the inclusion of additional molecular data, but there are concerns about completeness and relevance of this data. We developed an approach for representing the organization of human cellular processes using pathways as the nodes in a network. Pathways represent spatial and temporal sets of context-dependent interactions, generating a high-level network when linked together, which incorporates contextual information without the need for molecular interaction data. Analysis of the pathway network revealed linked communities representing functional relationships, comparable to those found in molecular networks, including metabolism, signal...

2011

In recent work, stable evolutionary signal induced by orthologous proteins has been observed in a Yeast protein-protein interaction (PPI) network. This finding suggests more connected subgraphs of a PPI network to be potential mediators of evolutionary information. Because protein complexes are also likely to be present in such subgraphs, it is interesting to characterize the bias of the orthology signal on the detection of putative protein complexes. To this aim, we propose a novel methodology for quantifying the functionality of the orthology signal in a PPI network at a protein complex level. The methodology performs a differential analysis between the functions of those complexes detected by clustering a PPI network using only proteins with orthologs in another given species, and the functions of complexes detected using the entire network or sub-networks generated by random sampling of proteins. We applied the proposed methodology to a Yeast PPI network using orthology information from a number of different organisms. The results indicated that the proposed method is capable to isolate functional categories that can be clearly attributed to the presence of an evolutionary (orthology) signal and quantify their distribution at a fine-grained protein level.

Current protocols in bioinformatics, 2018

Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology-based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is prese...

The Gene Ontology (GO) is one of the most widely used resources in molecular and cellular biology, largely through the use of “enrichment analysis”. To facilitate informed use of GO, we present GOTrack (https://gotrack.msl.ubc.ca), which provides access to historical records and trends in the Gene Ontology and GO annotations (GOA). GOTrack gives users access to gene- and term-level information on annotations for nine model organisms as well as an interactive tool that measures the stability of enrichment results over time for user-provided “hit lists” of genes. To document the effects of GO evolution on enrichment, we analyzed over 2500 published hit lists of human genes (most over 9 years old). 53% of hit lists were considered to yield significantly stable enrichment results. Because stability is far from assured for any individual hit list, GOTrack can lead to more informed and cautious application of GO to genomics research.

Scientific Reports

The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automated GO-CC annotation of proteins suffer from the inconsistency of individual GO-CC term predictions. Here, we present FGGA-CC + , a class of hierarchical graph-based classifiers for the consistent GO-CC annotation of protein coding genes at the subcellular compartment or macromolecular complex levels. Aiming to boost the accuracy of GO-CC predictions, we make use of the protein localization knowledge in the GO-Biological Process (GO-BP) annotations to boost the accuracy of GO-CC prediction. As a result, FGGA-CC + classifiers are built from annotation data in both the GO-CC and GO-BP ontologies. Due to their graph-based design, FGGA-CC + classifiers are fully interpretable and their predictions amenable to expert analysis. Promising results on protein annotation data from five model organisms were obtained. Additionally, successful validation results in the annotation of a challenging subset of tandem duplicated genes in the tomato non-model organism were accomplished. Overall, these results suggest that FGGA-CC + classifiers can indeed be useful for satisfying the huge demand of GO-CC annotation arising from ubiquitous high throughout sequencing and proteomic projects.

2019 IEEE International Conference On Artificial Intelligence Testing (AITest), 2019

Proteins are the workhorses of life and gaining insight on their functions is of paramount importance for applications such as drug design. However, the experimental validation of functions of proteins is highly-resource consuming. Therefore, recently, automated protein function prediction (AFP) using machine learning has gained significant interest. Many of these AFP tools are based on supervised learning models trained using existing gold-standard functional annotations, which are known to be incomplete. The main challenge associated with conducting systematic testing on AFP software is the lack of a test oracle, which determines passing or failing of a test case; unfortunately, due to the incompleteness of gold-standard data, the exact expected outcomes are not well defined for the AFP task. Thus, AFP tools face the oracle problem. In this work, we use metamorphic testing (MT) to test nine state-of-the-art AFP tools by defining a set of metamorphic relations (MRs) that apply input transformations to protein sequences. According to our results, we observe that several AFP tools fail all the test cases causing concerns over the quality of their predictions.

2019

The main aspect of bioinformatics is to make an understanding between microarray data with biological processes as much as possible to ensure the development and application of data mining techniques. Microarray dataset is high voluminous containing huge genes, most of these are irrelevant regarding cancer classification. These irrelevant genes should be filtered out from the dataset before applying it in cancer classification system. In this paper, a clustering algorithm is used to group the genes whose similar expressions suggest that they may be co-regulated. Once the clusters are obtained, the biological knowledge is investigated for the genes associated with the clusters. A quality-based partition is determined by the co-expressed genes that have been incorporated with similar biological knowledge. Gene Ontology (GO) annotations are used to link the clusters to identify the biologically meaningful genes within the clusters. In the next phase, the fold-change method is used to p...

PloS one, 2016

There are insufficient system-wide transcriptomic (or other) data that help explain the observed inter-individual variability in antibody titers after measles vaccination in otherwise healthy individuals. We performed a transcriptome(mRNA-Seq)-profiling study after in vitro viral stimulation of PBMCs from 30 measles vaccine recipients, selected from a cohort of 764 schoolchildren, based on the highest and lowest antibody titers. We used regression and network biology modeling to define markers associated with neutralizing antibody response. We identified 39 differentially expressed genes that demonstrate significant differences between the high and low antibody responder groups (p-value≤0.0002, q-value≤0.092), including the top gene CD93 (p<1.0E-13, q<1.0E-09), encoding a receptor required for antigen-driven B-cell differentiation, maintenance of immunoglobulin production and preservation of plasma cells in the bone marrow. Network biology modeling highlighted plasma cell surv...

2022

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and often leads to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis method that summarises redundancies into biological themes and provides various analytical modules to characterise and visualise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE’s versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance al...

Genetics Selection Evolution, 2016

Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene's network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide 'omics' data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.

International Journal of Molecular Sciences, 2016

Aegista chejuensis and Aegista quelpartensis (Family-Bradybaenidae) are endemic to Korea, and are considered vulnerable due to declines in their population. The limited genetic resources for these species restricts the ability to prioritize conservation efforts. We sequenced the transcriptomes of these species using Illumina paired-end technology. Approximately 257 and 240 million reads were obtained and assembled into 198,531 and 230,497 unigenes for A. chejuensis and A. quelpartensis, respectively. The average and N50 unigene lengths were 735.4 and 1073 bp, respectively, for A. chejuensis, and 705.6 and 1001 bp, respectively, for A. quelpartensis. In total, 68,484 (34.5%) and 77,745 (33.73%) unigenes for A. chejuensis and A. quelpartensis, respectively, were annotated to databases. Gene Ontology terms were assigned to 23,778 (11.98%) and 26,396 (11.45) unigenes, for A. chejuensis and A. quelpartensis, respectively, while 5050 and 5838 unigenes were mapped to 117 and 124 pathways in the Kyoto Encyclopedia of Genes and Genomes database. In addition, we identified and annotated 9542 and 10,395 putative simple sequence repeats (SSRs) in unigenes from A. chejuensis and A. quelpartensis, respectively. We designed a list of PCR primers flanking the putative SSR regions. These microsatellites may be utilized for future phylogenetics and conservation initiatives.

The FASEB Journal, 2019

This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Journal of Cerebral Blood Flow & Metabolism, 2016

Prolonged translation arrest correlates with delayed neuronal death of hippocampal CA1 neurons following global cerebral ischemia and reperfusion. Many previous studies investigated ribosome molecular biology, but mRNA regulatory mechanisms after brain ischemia have been less studied. Here we investigated the embryonic lethal abnormal vision/Hu isoforms HuR, HuB, HuC, and HuD, as well as expression of mRNAs containing adenine and rich uridine elements following global ischemia in rat brain. Proteomics of embryonic lethal abnormal vision immunoprecipitations or polysomes isolated from rat hippocampal CA1 and CA3 from controls or following 10 min ischemia plus 8 h of reperfusion showed distinct sets of mRNA-binding proteins, suggesting differential mRNA regulation in each condition. Notably, HuB, HuC, and HuD were undetectable in NIC CA1. At 8 h reperfusion, polysome-associated mRNAs contained 46.1% of ischemia-upregulated mRNAs containing adenine and rich uridine elements in CA3, but...

Genes, 2016

An aquatic gastropod belonging to the family Neritidae, Clithon retropictus is listed as an endangered class II species in South Korea. The lack of information on its genomic background limits the ability to obtain functional data resources and inhibits informed conservation planning for this species. In the present study, the transcriptomic sequencing and de novo assembly of C. retropictus generated a total of 241,696,750 high-quality reads. These assembled to 282,838 unigenes with mean and N50 lengths of 736.9 and 1201 base pairs, respectively. Of these, 125,616 unigenes were subjected to annotation analysis with known proteins in Protostome DB, COG, GO, and KEGG protein databases (BLASTX; E ≤ 0.00001) and with known nucleotides in the Unigene database (BLASTN; E ≤ 0.00001). The GO analysis indicated that cellular process, cell, and catalytic activity are the predominant GO terms in the biological process, cellular component, and molecular function categories, respectively. In add...

Entomological Research, 2016

Grassland butterflies are considered representative indicators of biodiversity and ecosystem health. Their dramatic decline caused by habitat destruction, intensifying agriculture and global warming has prompted concern for conservation. While ecological indices of butterflies have been documented, there is a lack of genomic resources relative to the biological importance of this group. Here, we report, first whole-transcriptomic resource for Fabriciana nerippe, a brush-footed butterfly member that is endangered in Korea. Approximately, 241.3 million clean reads were obtained from paired-end Illumina sequencing of adult whole-body RNA. The de novo assembly resulted in 114 405 unigenes with length ranging from 133 to 33 218 bp. We found 41 868 assembled unigenes homologous to sequences in locally curated PANM-DB (Protostome DB). We assigned gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology terms to 18 085 and 5779 unigenes, respectively. InterProScan predicted 6691 protein domains for the assembled unigene sequences, with the reverse transcriptase and zinc finger domains found to be the most predominant. A total of 315 282 simple sequence repeats (SSRs) were identified from the assembled unigenes, when considering dinucleotide to octanucleotide repeats with a minimum of three repeat units. AT/AT, AAT/ATT, and AAAT/ATTT represented the most prominent SSR repeat types in the transcriptome. The unigene annotation profile and microsatellites generated in the study can serve as a reference resource for closely related species and gene functional analysis studies. Our data can be utilized to study ecological drift and loss of the species, consequently leading to better protection of the population.

Molecular genetics and genomics : MGG, 2016

The Korean endemic land snail Koreanohadra kurodana (Gastropoda: Bradybaenidae) found in humid areas of broadleaf forests and shrubs have been considered vulnerable as the number of individuals are declining in recent years. The species is poorly characterized at the genomic level that limits the understanding of functions at the molecular and genetics level. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive transcript dataset of visceral mass tissue of K. kurodana by the Illumina paired-end sequencing technology. Over 234 million quality reads were assembled to a total of 315,924 contigs and 191,071 unigenes, with an average and N50 length of 585.6 and 715 bp and 678 and 927 bp, respectively. Overall, 36.32 % of the unigenes found matches to known protein/nucleotide sequences in the public databases. The direction of the unigenes to functional categories was determined using COG, GO, KEGG, and InterProScan protein domain search. The GO a...

Scientific reports, 2018

The availability of powerful high-throughput genomic tools, combined with genome scans, has helped identifying genes and genetic changes responsible for environmental adaptation in many organisms, including the honeybee. Here, we resequenced 87 whole genomes of the honeybee native to Iberia and used conceptually different selection methods (Samβada, LFMM, PCAdapt, iHs) together with in sillico protein modelling to search for selection footprints along environmental gradients. We found 670 outlier SNPs, most of which associated with precipitation, longitude and latitude. Over 88.7% SNPs laid outside exons and there was a significant enrichment in regions adjacent to exons and UTRs. Enrichment was also detected in exonic regions. Furthermore, in silico protein modelling suggests that several non-synonymous SNPs are likely direct targets of selection, as they lead to amino acid replacements in functionally important sites of proteins. We identified genomic signatures of local adaptatio...

Genome Biology and Evolution, 2020

Historical specimens in museum collections provide opportunities to gain insights into the genomic past. For the Western honey bee, Apis mellifera L., this is particularly important because its populations are currently under threat worldwide and have experienced many changes in management and environment over the last century. Using Swiss Apis mellifera mellifera as a case study, our research provides important insights into the genetic diversity of native honey bees prior to the industrial-scale introductions and trade of non-native stocks during the 20th century—the onset of intensive commercial breeding and the decline of wild honey bees following the arrival of Varroa destructor. We sequenced whole-genomes of 22 honey bees from the Natural History Museum in Bern collected in Switzerland, including the oldest A. mellifera sample ever sequenced. We identify both, a historic and a recent migrant, natural or human-mediated, which corroborates with the population history of honey be...

BMC Genomics, 2017

Background: The use of swine in biomedical research has increased dramatically in the last decade. Diverse genomic-and proteomic databases have been developed to facilitate research using human and rodent models. Current porcine gene databases, however, lack the robust annotation to study pig models that are relevant to human studies and for comparative evaluation with rodent models. Furthermore, they contain a significant number of errors due to their primary reliance on machine-based annotation. To address these deficiencies, a comprehensive literature-based survey was conducted to identify certain selected genes that have demonstrated function in humans, mice or pigs. Results: The process identified 13,054 candidate human, bovine, mouse or rat genes/proteins used to select potential porcine homologs by searching multiple online sources of porcine gene information. The data in the Porcine Translational Research Database ((http://www.ars.usda.gov/Services/docs.htm?docid=6065) is supported by >5800 references, and contains 65 data fields for each entry, including >9700 full length (5′ and 3′) unambiguous pig sequences, >2400 real time PCR assays and reactivity information on >1700 antibodies. It also contains gene and/or protein expression data for >2200 genes and identifies and corrects 8187 errors (gene duplications artifacts, mis-assemblies, mis-annotations, and incorrect species assignments) for 5337 porcine genes. Conclusions: This database is the largest manually curated database for any single veterinary species and is unique among porcine gene databases in regard to linking gene expression to gene function, identifying related gene pathways, and connecting data with other porcine gene databases. This database provides the first comprehensive description of three major Super-families or functionally related groups of proteins (Cluster of Differentiation (CD) Marker genes, Solute Carrier Superfamily, ATP binding Cassette Superfamily), and a comparative description of porcine microRNAs.

Archives of Biochemistry and Biophysics, 2020

Rett syndrome (RTT) is a progressive neurodevelopmental disorder caused by mutations in the X-linked MECP2 gene. RTT patients show multisystem disturbances associated with perturbed redox homeostasis and inflammation, which appear as possible key factors in RTT pathogenesis. In this study, using primary dermal fibroblasts from control and RTT subjects, we performed a proteomic analysis that, together with data mining approaches, allowed us to carry out a comprehensive characterization of RTT cellular proteome. Functional and pathway enrichment analyses showed that differentially expressed proteins in RTT were mainly enriched in biological processes related to immune/inflammatory responses. Overall, by using proteomic data mining as supportive approach, our results provide a detailed insight into the molecular pathways involved in RTT immune dysfunction that, causing tissue and organ damage, can increase the vulnerability of affected patients to unknown endogenous factors or infections.

PLOS ONE, 2020

Extracellular vesicles (EVs), are important for intercellular communication in both physiological and pathological processes. To explore the potential of cancer derived EVs as disease biomarkers for diagnosis, monitoring, and treatment decision, it is necessary to thoroughly characterize their biomolecular content. The aim of the study was to characterize and compare the protein content of EVs derived from three different cancer cell lines in search of a specific molecular signature, with emphasis on proteins related to the carcinogenic process. Oral squamous cell carcinoma (OSCC), pancreatic ductal adenocarcinoma (PDAC) and melanoma brain metastasis cell lines were cultured in CELLine AD1000 flasks. EVs were isolated by ultrafiltration and size-exclusion chromatography and characterized. Next, the isolated EVs underwent liquid chromatography-mass spectrometry (LC-MS) analysis for protein identification. Functional enrichment analysis was performed for a more general overview of the biological processes involved. More than 600 different proteins were identified in EVs from each particular cell line. Here, 14%, 10%, and 24% of the identified proteins were unique in OSCC, PDAC, and melanoma vesicles, respectively. A specific protein profile was discovered for each cell line, e.g., EGFR in OSCC, Muc5AC in PDAC, and FN1 in melanoma vesicles. Nevertheless, 25% of all the identified proteins were common to all cell lines. Functional enrichment analysis linked the proteins in each data set to biological processes such as "biological adhesion", "cell motility", and "cellular component biogenesis". EV proteomics discovered cancer-specific protein profiles, with proteins involved in processes promoting tumor progression. In addition, the biological processes associated to the melanoma-derived EVs were distinct from the ones linked to the EVs isolated from OSCC and PDAC. The malignancy specific biomolecular cues in EVs may have potential applications as diagnostic biomarkers and in therapy.

F1000Research

As the global population is surging, the agricultural industry is required to meet the food demand while simultaneously providing eco-friendly sustainable crops that can withstand numerous abiotic and biotic stresses. The current era requires high-throughput biotechnology approaches to alleviate the current plant production and protection crisis. Omics approaches are regarded as a collection of high throughput technologies ending with “omics” such as genomics, proteomics, transcriptomics, metabolomics, phenomics and epigenomics. Furthermore, omics provide the best tactic to increase high quality crop production yield. A body of evidence has shown that microbial diversity, abundance, composition, functional gene patterns, and metabolic pathways at the genome level could also assist in understanding the contributions of the microbial community towards plant growth and protection. In addition, the link between plant genomes and phenotypes under physiological and environmental settings ...

Maize Iranian mosaic virus (MIMV, family Rhabdoviridae) is one of the factors limiting cereal production in Iran. MIMV can affect maize transcriptome, alternative splicing, circular RNAs and miRNA profiles during infection and active biological pathways in the maize cells. In the present study, we sought to find critical genes and key pathways involved in MIMV infection and analyzed gene networks, pathways and promoters using transcriptome data. We determined the hub genes involved in pathways related to the proteasome and ubiquitin. These hub genes played a key role in the response to MIMV infection. Gene ontology (GO) and pathway analysis showed that metabolic processes are most affected in MIMV infection. In addition, the results showed the important role of the cellular endoplasmic reticulum in MIMV infection. Network cluster analysis confirmed the result of GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Potential miRNAs were identified as target hub genes using...

BMC bioinformatics, 2016

Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of indi...

Proceedings of the National Academy of Sciences, 2018

Significance Despite the fundamental importance of the surfaceome as a signaling gateway to the cellular microenvironment, it remains difficult to determine which proteoforms reside in the plasma membrane and how they interact to enable context-dependent signaling functions. We applied a machine-learning approach utilizing domain-specific features to develop the accurate surfaceome predictor SURFY and used it to define the human in silico surfaceome of 2,886 proteins. The in silico surfaceome is a public resource which can be used to filter multiomics data to uncover cellular phenotypes and surfaceome markers. By our domain-specific feature machine-learning approach, we show indirectly that the environment (extracellular, cytoplasm, or vesicle) is reflected in the biochemical properties of protein domains reaching into that environment.

Expert Review of Vaccines, 2018

Introduction: Repeated measles outbreaks in countries with relatively high vaccine coverage are mainly due to failure to vaccinate and importation; however, cases in immunized individuals exist raising questions about suboptimal measles vaccine-induced humoral immunity and/or waning immunity in a low measles-exposure environment. Areas covered: The plaque reduction neutralization measurement of functional measles-specific antibodies correlates with protection is the gold standard in measles serology, but it does not assess cellular-immune or other parameters that may be associated with durable and/or protective immunity after vaccination. Additional correlates of protection and long-term immunity and new determinants/ signatures of vaccine responsiveness such as specific CD46 and IFI44L genetic variants associated with neutralizing antibody titers after measles vaccination are under investigation. Current and future systems biology studies, coupled with new technology/assays and analytical approaches, will lead to an increasingly sophisticated understanding of measles vaccine-induced humoral immunity and will identify 'signatures' of protective and durable immune responses. Expert opinion: This will translate into the development of highly predictive assays of measles vaccine efficacy, effectiveness, and durability for prospective identification of potential low/non-responders and susceptible individuals who require additional vaccine doses. Such new advances may drive insights into the development of new/improved vaccine formulations and delivery systems.

bioRxiv (Cold Spring Harbor Laboratory), 2019

The biology of how faces are built and come to differ from one another is complex. Discovering the genes that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL's peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic. The mammalian face is a complex structure serving many functions. We studied the genetic basis for facial skeletal diversity in a large sample of mice from an experimental population designed for the study of complex traits. We quantified the contribution of genetic variation to variation in three-dimensional facial shape across more than 55,000 genetic markers spread throughout the mouse genome. We found 37 genetic regions which are very likely to contribute to differences in facial shape. We then conducted a more detailed analysis of the genetic regions associated with the most variable aspects of facial shape. For these regions, a disproportionately large number of genes are known to be important to growth and to skeletal and facial development. The magnitude of these genetic contributions to differences in facial shape are consistently small. Our results therefore support the notion that facial skeletal diversity is influenced by many genes of small effect, but that some of these small effects may be related to genes that are fundamental to skeletal and facial development.

Genes and Immunity, 2016

This study aimed to identify gene expression markers shared between both influenza hemagglutination inhibition (HAI) and virusneutralization antibody (VNA) responses. We enrolled 158 older subjects who received the 2010-2011 trivalent inactivated influenza vaccine. Influenza-specific HAI and VNA titers and mRNA-sequencing were performed using blood samples obtained at Days 0, 3 and 28 post vaccination. For antibody response at Day 28 versus Day 0, several gene sets were identified as significant in predictive models for HAI (n = 7) and VNA (n = 35) responses. Five gene sets (comprising the genes MAZ, TTF, GSTM, RABGGTA, SMS, CA, IFNG and DOPEY) were in common for both HAI and VNA. For response at Day 28 versus Day 3, many gene sets were identified in predictive models for HAI (n = 13) and VNA (n = 41). Ten gene sets (comprising biologically related genes, such as MAN1B1, POLL, CEBPG, FOXP3, IL12A, TLR3, TLR7 and others) were shared between HAI and VNA. These identified gene sets demonstrated a high degree of network interactions and likelihood for functional relationships. Influenza-specific HAI and VNA responses demonstrated a remarkable degree of similarity. Although unique gene set signatures were identified for each humoral outcome, several gene sets were determined to be in common with both HAI and VNA response to influenza vaccine.

PLOS ONE, 2020

The biology of how faces are built and come to differ from one another is complex. Discovering normal variants that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and Xchromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal and facial development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL's peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.

bioRxiv (Cold Spring Harbor Laboratory), 2023

Ongoing ocean warming due to climate change poses new challenges for marine life and its exploitation. We have used transcriptomics to find genetically-based responses to increased temperature in natural populations of the marine clam Ruditapes decussatus living along parallel thermal gradients in southern Europe. Clams of the Atlantic and West Mediterranean races were collected in a northern (cool) and a southern (warm) localities. The animals were kept in running seawater in the warm, Atlantic southern locality for a 15-week period, during which water temperature raised to typical southern European summer values. After this period, the expression profile was obtained for a total of 34 clams and 11,025 probes by means of an oligonucleotide microarray. We found distinct transcriptional patterns for each population based on a total of 553 differentially expressed genes (DEG), indicating innate differences which probably have a genetic basis. Race and latitude contributed significantly to gene expression differences, with very different sets of DEG. A gene ontology analysis showed that races differed mainly at genes involved in ribosomal function and protein biosynthesis, while genes related to glutathione metabolism and ATP synthesis in the mitochondria were the most outstanding as to north/south transcriptional differences.

2021

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are two most prevalent age-related dementias that severely affect a large number of elderly people around the globe. Poor understanding of pathogenesis of these neurological diseases imposes challenge to discover therapeutic measures and effective diagnosis methods. In this study, a network-based approach was utilized to identify potential common molecular signatures and therapeutic agents for AD and PD. Protein-protein interaction analysis revealed NCK1, UBC, CDH1, CDC20, ACTB, PSMA7, PRPF8, RPL7, XRCC6 and HSP90AB1 as the best proteome signatures. Different regulatory transcriptional signatures i.e., YY1, NFKB1, BRCA1, TP53, GATA2, SREBF2, E2F1, FOXC1, RELA and NFIC and post-transcriptional signatures i.e., hsa-mir-186-5p, hsamir-92a-3p, hsa-mir-615-3p, hsa-let-7c-5p, hsa-mir-100-5p, hsa-mir-93-3p, hsa-mir-5681a, hsamir-484, hsa-mir-193b-3p and hsa-mir-16p-5p were identified from other interaction network. Drug-gene interaction...

Genes, 2016

The tadpole shrimp (Triops longicaudatus) is an aquatic crustacean that helps control pest populations. It inhabits freshwater ponds and pools and has been described as a living fossil. T. longicaudatus was officially declared an endangered species South Korea in 2005; however, through subsequent protection and conservation management, it was removed from the endangered species list in 2012. The limited number of available genetic resources on T. longicaudatus makes it difficult to obtain valuable genetic information for marker-aided selection programs. In this study, whole-transcriptome sequencing of T. longicaudatus generated 39.74 GB of clean data and a total of 269,822 contigs using the Illumina HiSeq 2500 platform. After clustering, a total of 208,813 unigenes with an N 50 length of 1089 bp were generated. A total of 95,105 unigenes were successfully annotated against Protostome (PANM), Unigene, Eukaryotic Orthologous Groups (KOG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases using BLASTX with a cutoff of 1E−5. A total of 57,731 unigenes were assigned to GO terms, and 7247 unigenes were mapped to 129 KEGG pathways. Furthermore, 1595 simple sequence repeats (SSRs) were detected

bioRxiv (Cold Spring Harbor Laboratory), 2022

COVID-19 develops certain neurological symptoms, the molecular pathophysiology of which is obscure. In the present study, two networks were constructed and their hubbottleneck and driver nodes were evaluated to consider them as 'target genes' followed by identifying 'candidate genes' and their associations with neurological phenotypes of COVID-19. A tripartite network was first constructed using literature-based neurological symptoms of COVID-19 as input. The target genes evaluated therefrom were then used as query genes to identify the co-expressed genes from the RNA-sequence data of the frontal cortex of COVID-19 patients using pair-wise mutual information to genes. A 'combined gene network' (CGN) was constructed with 189 genes selected from TN and 225 genes co-expressed in COVID-19. Total 44 'target genes' evaluated from both networks and their connecting genes in respective networks were analyzed functionally by measuring pair-wise 'semantic similarity scores' (SSS) and finding Enrichr annotation terms against a set of genes. A new integrated 'weighted harmonic mean score' was formulated using SSS and STRING-based 'combined score' to select 21 gene-pairs among 'target genes' that provided 21 'candidate genes' with their properties as 'indispensable driver nodes' of CGN. Finally, six pairs providing seven prevalent candidate genes (ADAM10,

Nature communications, 2015

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach th...