scholarly journals Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i516-i524
Author(s):  
Midori Iida ◽  
Michio Iwata ◽  
Yoshihiro Yamanishi
Keyword(s):  
Large Scale ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Molecular Networks ◽  
Supplementary Information ◽  
New Associations ◽  
Disease States ◽  
Molecular Features ◽  
Novel Approach

Abstract Motivation Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease–disease relationships remain uncharacterized. Results In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Shedding new light on opsin evolution

2011 ◽  
Vol 279 (1726) ◽  
pp. 3-14 ◽  
Author(s):  
Megan L. Porter ◽  
Joseph R. Blasic ◽  
Michael J. Bok ◽  
Evan G. Cameron ◽  
Thomas Pringle ◽  
...  
Keyword(s):  
Evolutionary History ◽  
Large Scale ◽  
Current Model ◽  
Expression Patterns ◽  
Model Organisms ◽  
Biological Functions ◽  
Site Specific ◽  
History Of ◽  
Functional Features

Opsin proteins are essential molecules in mediating the ability of animals to detect and use light for diverse biological functions. Therefore, understanding the evolutionary history of opsins is key to understanding the evolution of light detection and photoreception in animals. As genomic data have appeared and rapidly expanded in quantity, it has become possible to analyse opsins that functionally and histologically are less well characterized, and thus to examine opsin evolution strictly from a genetic perspective. We have incorporated these new data into a large-scale, genome-based analysis of opsin evolution. We use an extensive phylogeny of currently known opsin sequence diversity as a foundation for examining the evolutionary distributions of key functional features within the opsin clade. This new analysis illustrates the lability of opsin protein-expression patterns, site-specific functionality (i.e. counterion position) and G-protein binding interactions. Further, it demonstrates the limitations of current model organisms, and highlights the need for further characterization of many of the opsin sequence groups with unknown function.

scholarly journals SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

2020 ◽  
Author(s):  
Stefan Schulze ◽  
Anne Oltmanns ◽  
Christian Fufezan ◽  
Julia Krägenbring ◽  
Michael Mormann ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Glycosylation ◽  
Supplementary Information ◽  
Cyanidioschyzon Merolae ◽  
Human Breast Milk ◽  
The Novel ◽  
Post Translational Modification ◽  
Cellular Functions ◽  
Domains Of Life

AbstractMotivationProtein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes.ResultsTherefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.AvailabilityThe source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating [email protected] and [email protected] informationSupplementary data are available online.

scholarly journals Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i417-i426
Author(s):  
Assya Trofimov ◽  
Joseph Paul Cohen ◽  
Yoshua Bengio ◽  
Claude Perreault ◽  
Sébastien Lemieux
Keyword(s):  
Large Scale ◽  
Learning Algorithm ◽  
Single Gene ◽  
Expression Patterns ◽  
Tensor Decomposition ◽  
Supplementary Information ◽  
Fe Model ◽  
Tensor Factorization ◽  
Rna Seq ◽  
Sample Representation

Abstract Motivation The recent development of sequencing technologies revolutionized our understanding of the inner workings of the cell as well as the way disease is treated. A single RNA sequencing (RNA-Seq) experiment, however, measures tens of thousands of parameters simultaneously. While the results are information rich, data analysis provides a challenge. Dimensionality reduction methods help with this task by extracting patterns from the data by compressing it into compact vector representations. Results We present the factorized embeddings (FE) model, a self-supervised deep learning algorithm that learns simultaneously, by tensor factorization, gene and sample representation spaces. We ran the model on RNA-Seq data from two large-scale cohorts and observed that the sample representation captures information on single gene and global gene expression patterns. Moreover, we found that the gene representation space was organized such that tissue-specific genes, highly correlated genes as well as genes participating in the same GO terms were grouped. Finally, we compared the vector representation of samples learned by the FE model to other similar models on 49 regression tasks. We report that the representations trained with FE rank first or second in all of the tasks, surpassing, sometimes by a considerable margin, other representations. Availability and implementation A toy example in the form of a Jupyter Notebook as well as the code and trained embeddings for this project can be found at: https://github.com/TrofimovAssya/FactorizedEmbeddings. Supplementary information Supplementary data are available at Bioinformatics online.

Molecular and cytological characterization of genomic variability in hexaploid wheat 'Lindström'

Genome ◽  
2005 ◽  
Vol 48 (5) ◽  
pp. 895-904
Author(s):  
Pedro Costa-Nunes ◽  
Teresa Ribeiro ◽  
Margarida Delgado ◽  
Leonor Morais-Cecílio ◽  
Neil Jones ◽  
...  
Keyword(s):  
Molecular Weight ◽  
High Molecular Weight ◽  
Large Scale ◽  
Expression Patterns ◽  
5S Rdna ◽  
Rdna Locus ◽  
B Chromosomes ◽  
Addition Line ◽  
Rdna Loci

'Lindström' wheat (AABBDD + rye B chromosomes) was used to study the effects of alien chromatin introgressed into a wheat genetic background, subjecting the wheat genome to a new and transient allopolyploidisation episode. Using this experimental material, we have previously demonstrated that no large-scale chromosomal translocations occurred as a result of the genomic constitution of the addition line. However, we have shown that the presence of a number of rye B chromosomes is associated with changes in the interphase organization and expression patterns of wheat rDNA loci. We have now extended our studies to focus on a further characterization of 'Lindström' 5S rDNA loci and also on high molecular weight glutenin subunit (HMW-GS) patterns. In the process, we have uncovered an unusually large variant of the 5S rDNA locus on wheat chromosome 1B (not to be confused with rye B chromosomes) and 2 novel HMW glutenin y-type alleles. These changes are not directly related to variation in rye B chromosome number in the present material, but the fact that a new, and still segregating, 1Dy HMW-GS gene was identified indicates a recent timescale for its origin. Strikingly, the 'Lindström' 5S rDNA 1B locus integrates a unit sharing 94% homology with a rye 5S rDNA sequence, suggesting the possibility that the wheat locus was colonized by highly homologous rye sequences during the breeding of 'Lindström', when the rye and wheat genomes were together, albeit briefly, in the same nucleus.Key words: Triticum aestivum 'Lindström', allopolyploidisation, 5S rDNA, NTS, high molecular weight glutenin (HMW-GS).

scholarly journals An entropic characterization of protein interaction networks and cellular robustness

2006 ◽  
Vol 3 (11) ◽  
pp. 843-850 ◽  
Author(s):  
Thomas Manke ◽  
Lloyd Demetrius ◽  
Martin Vingron
Keyword(s):  
Protein Interaction ◽  
Large Scale ◽  
Global Network ◽  
Molecular Networks ◽  
Local Network ◽  
Large Contribution ◽  
Fundamental Relation ◽  
Cellular Behaviour ◽  
Network Entropy

The structure of molecular networks is believed to determine important aspects of their cellular function, such as the organismal resilience against random perturbations. Ultimately, however, cellular behaviour is determined by the dynamical processes, which are constrained by network topology. The present work is based on a fundamental relation from dynamical systems theory, which states that the macroscopic resilience of a steady state is correlated with the uncertainty in the underlying microscopic processes, a property that can be measured by entropy. Here, we use recent network data from large-scale protein interaction screens to characterize the diversity of possible pathways in terms of network entropy. This measure has its origin in statistical mechanics and amounts to a global characterization of both structural and dynamical resilience in terms of microscopic elements. We demonstrate how this approach can be used to rank network elements according to their contribution to network entropy and also investigate how this suggested ranking reflects on the functional data provided by gene knockouts and RNAi experiments in yeast and Caenorhabditis elegans . Our analysis shows that knockouts of proteins with large contribution to network entropy are preferentially lethal. This observation is robust with respect to several possible errors and biases in the experimental data. It underscores the significance of entropy as a fundamental invariant of the dynamical system, and as a measure of structural and dynamical properties of networks. Our analytical approach goes beyond the phenomenological studies of cellular robustness based on local network observables, such as connectivity. One of its principal achievements is to provide a rationale to study proxies of cellular resilience and rank proteins according to their importance within the global network context.

scholarly journals Noncoding-RNA-Mediated Regulation in Response to Macronutrient Stress in Plants

2021 ◽  
Vol 22 (20) ◽  
pp. 11205
Author(s):  
Ziwei Li ◽  
Peng Tian ◽  
Tengbo Huang ◽  
Jianzi Huang
Keyword(s):  
Noncoding Rna ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Theoretical Study ◽  
Functional Characterization ◽  
Molecular Networks ◽  
Plant Responses ◽  
Crop Breeding ◽  
Target Module

Macronutrient elements including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S) are required in relatively large and steady amounts for plant growth and development. Deficient or excessive supply of macronutrients from external environments may trigger a series of plant responses at phenotypic and molecular levels during the entire life cycle. Among the intertwined molecular networks underlying plant responses to macronutrient stress, noncoding RNAs (ncRNAs), mainly microRNAs (miRNAs) and long ncRNAs (lncRNAs), may serve as pivotal regulators for the coordination between nutrient supply and plant demand, while the responsive ncRNA-target module and the interactive mechanism vary among elements and species. Towards a comprehensive identification and functional characterization of nutrient-responsive ncRNAs and their downstream molecules, high-throughput sequencing has produced massive omics data for comparative expression profiling as a first step. In this review, we highlight the recent findings of ncRNA-mediated regulation in response to macronutrient stress, with special emphasis on the large-scale sequencing efforts for screening out candidate nutrient-responsive ncRNAs in plants, and discuss potential improvements in theoretical study to provide better guidance for crop breeding practices.

scholarly journals MSF: Modulated Sub-graph Finder

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1346
Author(s):  
Mariam R. Farman ◽  
Ivo L. Hofacker ◽  
Fabian Amman
Keyword(s):  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Data Interpretation ◽  
Differentially Expressed ◽  
Novel Approach ◽  
Differential Gene ◽  
External Stimuli ◽  
Flow Of Information

High throughput techniques such as RNA-seq or microarray analysis have proven tobe invaluable for the characterization of global transcriptional gene activity changesdue to external stimuli or diseases. Differential gene expression analysis (DGEA) is the first step in the course of data interpretation, typically producing lists of dozens to thousands of differentially expressed genes. To further guide the interpretation of these lists, different pathway analysis approaches have been developed. These tools typically rely on the classification of genes into sets of genes, such as pathways, based on the interactions between the genes and their function in a common biological process. Regardless of technical differences, these methods do not properly account for cross talk between different pathways and rely on binary separation into differentially expressed gene and unaffected genes based on an arbitrarily set p-value cut-off. To overcome this limitation, we developed a novel approach to identify concertedly modulated sub-graphs in the global cell signaling network, based on the DGEA results of all genes tested. To this end, expression patterns of genes are integrated according to the topology of their interactions and allow potentially to read the flow of information and identify the effectors. The described software, named Modulated Sub-graph Finder (MSF) is freely available at https://github.com/Modulated-Subgraph-Finder/MSF.

scholarly journals A large-scale systematic survey of SARS-CoV-2 antibodies reveals recurring molecular features

2021 ◽  
Author(s):  
Yiquan Wang ◽  
Meng Yuan ◽  
Jian Peng ◽  
Ian A. Wilson ◽  
Nicholas C. Wu
Keyword(s):  
Large Scale ◽  
Somatic Hypermutation ◽  
Antigen Specificity ◽  
Human Antibodies ◽  
The Past ◽  
Influenza Hemagglutinin ◽  
Molecular Features ◽  
Isolation And Characterization ◽  
Single Antigen

In the past two years, the global research in combating COVID-19 pandemic has led to isolation and characterization of numerous human antibodies to the SARS-CoV-2 spike. This enormous collection of antibodies provides an unprecedented opportunity to study the antibody response to a single antigen. Using information derived from 88 research publications and 13 patents, we have assembled a dataset of ~8,000 human antibodies to the SARS-CoV-2 spike from >200 donors. Analysis of antibodies that target different domains of the spike protein reveals a number of common (public) responses to SARS-CoV-2, exemplified via recurring IGHV/IGK(L)V pairs, CDR H3 sequences, IGHD usage, and somatic hypermutation. We further present a proof-of-concept for predicting antigen specificity by using deep learning to differentiate sequences of antibodies to SARS-CoV-2 spike and to influenza hemagglutinin. Overall, this study not only provides an informative resource for antibody research, but fundamentally advances our molecular understanding of public antibody responses.

Large-scale identification and characterization of genes with asymmetric expression patterns in the developing chick retina

2004 ◽  
Vol 59 (1) ◽  
pp. 34-47 ◽  
Author(s):  
Takafumi Shintani ◽  
Akira Kato ◽  
Junichi Yuasa-Kawada ◽  
Hiraki Sakuta ◽  
Masakazu Takahashi ◽  
...  
Keyword(s):  
Large Scale ◽  
Expression Patterns ◽  
Chick Retina ◽  
Identification And Characterization ◽  
Asymmetric Expression
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