scholarly journals Listeriomics: an Interactive Web Platform for Systems Biology of Listeria

mSystems ◽  
2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Christophe Bécavin ◽  
Mikael Koutero ◽  
Nicolas Tchitchek ◽  
Franck Cerutti ◽  
Pierre Lechat ◽  
...  
Keyword(s):  
Systems Biology ◽  
Noncoding Rna ◽  
Model Organism ◽  
Regulation Mechanism ◽  
Data Sets ◽  
Omics Data ◽  
Sources Of Information ◽  
Proteomics Data ◽  
Rna Regulation ◽  
Web Platform

ABSTRACT In the last decades, Listeria has become a key model organism for the study of host-pathogen interactions, noncoding RNA regulation, and bacterial adaptation to stress. To study these mechanisms, several genomics, transcriptomics, and proteomics data sets have been produced. We have developed Listeriomics, an interactive web platform to browse and correlate these heterogeneous sources of information. Our website will allow listeriologists and microbiologists to decipher key regulation mechanism by using a systems biology approach. As for many model organisms, the amount of Listeria omics data produced has recently increased exponentially. There are now >80 published complete Listeria genomes, around 350 different transcriptomic data sets, and 25 proteomic data sets available. The analysis of these data sets through a systems biology approach and the generation of tools for biologists to browse these various data are a challenge for bioinformaticians. We have developed a web-based platform, named Listeriomics, that integrates different tools for omics data analyses, i.e., (i) an interactive genome viewer to display gene expression arrays, tiling arrays, and sequencing data sets along with proteomics and genomics data sets; (ii) an expression and protein atlas that connects every gene, small RNA, antisense RNA, or protein with the most relevant omics data; (iii) a specific tool for exploring protein conservation through the Listeria phylogenomic tree; and (iv) a coexpression network tool for the discovery of potential new regulations. Our platform integrates all the complete Listeria species genomes, transcriptomes, and proteomes published to date. This website allows navigation among all these data sets with enriched metadata in a user-friendly format and can be used as a central database for systems biology analysis. IMPORTANCE In the last decades, Listeria has become a key model organism for the study of host-pathogen interactions, noncoding RNA regulation, and bacterial adaptation to stress. To study these mechanisms, several genomics, transcriptomics, and proteomics data sets have been produced. We have developed Listeriomics, an interactive web platform to browse and correlate these heterogeneous sources of information. Our website will allow listeriologists and microbiologists to decipher key regulation mechanism by using a systems biology approach.

scholarly journals Quickomics: exploring omics data in an intuitive, interactive and informative manner

2021 ◽  
Author(s):  
Benbo Gao ◽  
Jing Zhu ◽  
Soumya Negi ◽  
Xinmin Zhang ◽  
Stefka Gyoneva ◽  
...  
Keyword(s):  
Modular Design ◽  
Functional Module ◽  
Supplementary Information ◽  
Data Sets ◽  
Omics Data ◽  
Proteomics Data ◽  
Primary Analysis ◽  
Link Type ◽  
R Shiny ◽  
Advanced Analysis

AbstractSummaryWe developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics data and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customized configurations and generates both interactive and publication-ready high-resolution plots to uncover biological insights from data. The modular design makes the tool extensible with ease.AvailabilityResearchers can experience the functionalities with their own data or demo RNA-Seq and proteomics data sets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/[email protected], [email protected] informationSupplementary materials are available at https://bit.ly/37HP17g.

scholarly journals Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 76 ◽  
Author(s):  
Farhana R. Pinu ◽  
David J. Beale ◽  
Amy M. Paten ◽  
Konstantinos Kouremenos ◽  
Sanjay Swarup ◽  
...  
Keyword(s):  
New Zealand ◽  
Systems Biology ◽  
Life Science ◽  
Biological Systems ◽  
Research Community ◽  
Data Sets ◽  
Omics Data ◽  
Omics Integration ◽  
Traditional Approaches ◽  
Multiple Interactions

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

The model organism as a system: integrating 'omics' data sets

2006 ◽  
Vol 7 (3) ◽  
pp. 198-210 ◽  
Author(s):  
Andrew R. Joyce ◽  
Bernhard Ø. Palsson
Keyword(s):  
Model Organism ◽  
Data Sets ◽  
Omics Data

scholarly journals piNET: a versatile web platform for downstream analysis and visualization of proteomics data

2020 ◽  
Vol 48 (W1) ◽  
pp. W85-W93 ◽  
Author(s):  
Behrouz Shamsaei ◽  
Szymon Chojnacki ◽  
Marcin Pilarczyk ◽  
Mehdi Najafabadi ◽  
Wen Niu ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Large Scale ◽  
Model Organism ◽  
Proteomics Data ◽  
Primary Input ◽  
Network Analyses ◽  
Enzyme Substrate ◽  
Chemical Inhibitors ◽  
Downstream Analysis ◽  
Web Platform

Abstract Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

scholarly journals Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Chaowang Lan ◽  
Hui Peng ◽  
Gyorgy Hutvagner ◽  
Jinyan Li
Keyword(s):  
Breast Cancer ◽  
Mutual Information ◽  
Noncoding Rna ◽  
Regulation Mechanism ◽  
Data Sets ◽  
Rna Seq ◽  
Competing Endogenous Rna ◽  
Data Set ◽  
Cerna Network ◽  
Pointwise Mutual Information

Abstract Background A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs. Results We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks. Conclusion Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding.

scholarly journals Cloud Computing Enabled Big Multi-Omics Data Analytics

2021 ◽  
Vol 15 ◽  
pp. 117793222110359
Author(s):  
Saraswati Koppad ◽  
Annappa B ◽  
Georgios V Gkoutos ◽  
Animesh Acharjee
Keyword(s):  
Cloud Computing ◽  
Data Analytics ◽  
Large Scale ◽  
Low Cost ◽  
Data Sets ◽  
Omics Data ◽  
Proteomics Data ◽  
Phenotypic Data ◽  
Multifactorial Diseases ◽  
Big Data Technologies

High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications.

scholarly journals AlzGPS: A Genome-wide Positioning Systems Platform to Catalyze Multi-omics for Alzheimer’s Therapeutic Discovery

2020 ◽  
Author(s):  
Yadi Zhou ◽  
Jiansong Fang ◽  
Lynn Bekris ◽  
Young Heon Kim ◽  
Andrew A. Pieper ◽  
...  
Keyword(s):  
Systems Biology ◽  
Network Visualization ◽  
Data Sets ◽  
Omics Data ◽  
Effective Prevention ◽  
Positioning Systems ◽  
Network Analyses ◽  
Clinical Databases ◽  
A Genome ◽  
Disease Modules

AbstractBackgroundOver15 million family members and caregivers have expended $220 billion for care of patients with AD and other dementias, and the attrition rate for AD clinical trials (2002-2012) is estimated at 99.6%. While recent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, no effective disease-modifying or preventive therapies, for AD have emerged in the past two decades. A new approach to integration of the genome, transcriptome, proteome, and human interactome in the drug discovery and development process is essential for this endeavor.MethodsIn this study, we developed AlzGPS (Genome-wide Positioning Systems platform for Alzheimer’s Therapeutic Discovery, https://alzgps.lerner.ccf.org), a comprehensive systems biology tool to enable searching, visualizing, and analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and effective prevention and treatment of AD.ResultsVia AlzGPS: (1) we curated more than 100 AD multi-omics data sets capturing DNA, RNA, protein, and small molecules’ profiles underlying AD pathogenesis (e.g., early vs. late stage and tau vs. amyloid endophenotype); (2) we constructed endophenotype disease modules by incorporating multi-omics findings and human protein-protein interactome networks; (3) we identified repurposable drugs from ∼3,000 FDA approved/investigational drugs for AD using state-of-the-art network proximity analyses; (4) we curated 300 literature references for highly repurposable drugs; (5) we included information from over 200 ongoing AD clinicals noting drug mechanisms and primary drug targets, and linking them to our integrated multi-omics view for targets and network analyses results for the drugs; (6) we implemented a highly interactive web-interface for database browsing and network visualization.ConclusionsNetwork visualization enabled by the AlzGPS includes brain-specific neighborhood networks for genes-of-interest, endophenotype disease module networks for data sets-of-interest, and mechanism-of-action networks for drugs targeting disease modules. By virtue of combining systems pharmacology and network-based integrative analysis of multi-omics data, the AlzGPS offers actionable systems biology tools for accelerating therapeutic development in AD.

scholarly journals Computational proteogenomic identification and functional interpretation of translated fusions and micro structural variations in cancer

2017 ◽  
Author(s):  
Yen Yi Lin ◽  
Alexander Gawronski ◽  
Faraz Hach ◽  
Sujun Li ◽  
Ibrahim Numanagić ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tissue Sample ◽  
The Cancer Genome Atlas ◽  
Molecular Profile ◽  
Data Sets ◽  
Omics Data ◽  
Proteomics Data ◽  
Structural Variations ◽  
Functional Interpretation ◽  
Proteomic Data

MotivationRapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. In this paper we introduce a novel computational framework which can integratively analyze all three types of omics data to obtain a complete molecular profile of a tissue sample, in normal and disease conditions. Our framework includes MiStrVar, an algorithmic method we developed to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can provide an accurate profile of structurally aberrant transcripts in cancer samples. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures in the respective proteomics data sets. Our framework's ability to observe structural aberrations at three levels of omics data provides means of validating their presence.ResultsWe have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq data sets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides [1]. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations (in particular, fusions) were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Moreover, the most significantly enriched genes involved in translated fusions are cancer-related. Furthermore a number of the somatic, translated microSVs are observed in tumor suppressor [email protected]

scholarly journals An RNA-centric view on gut Bacteroidetes

2020 ◽  
Vol 402 (1) ◽  
pp. 55-72
Author(s):  
Daniel Ryan ◽  
Gianluca Prezza ◽  
Alexander J. Westermann
Keyword(s):  
Noncoding Rna ◽  
Case Reports ◽  
Model Organism ◽  
Global Gene Expression ◽  
Host Cells ◽  
Human Gut ◽  
Open Questions ◽  
Obligate Anaerobic ◽  
Bacterial Rna ◽  
Rna Biology

AbstractBacteria employ noncoding RNAs to maintain cellular physiology, adapt global gene expression to fluctuating environments, sense nutrients, coordinate their interaction with companion microbes and host cells, and protect themselves against bacteriophages. While bacterial RNA research has made fundamental contributions to biomedicine and biotechnology, the bulk of our knowledge of RNA biology stems from the study of a handful of aerobic model species. In comparison, RNA research is lagging in many medically relevant obligate anaerobic species, in particular the numerous commensal bacteria comprising our gut microbiota. This review presents a guide to RNA-based regulatory mechanisms in the phylum Bacteroidetes, focusing on the most abundant bacterial genus in the human gut, Bacteroides spp. This includes recent case reports on riboswitches, an mRNA leader, cis- and trans-encoded small RNAs (sRNAs) in Bacteroides spp., and a survey of CRISPR-Cas systems across Bacteroidetes. Recent work from our laboratory now suggests the existence of hundreds of noncoding RNA candidates in Bacteroides thetaiotaomicron, the emerging model organism for functional microbiota research. Based on these collective observations, we predict mechanistic and functional commonalities and differences between Bacteroides sRNAs and those of other model bacteria, and outline open questions and tools needed to boost Bacteroidetes RNA research.

scholarly journals Future of Personalized Medicine in Oncology: A Systems Biology Approach

2010 ◽  
Vol 28 (16) ◽  
pp. 2777-2783 ◽  
Author(s):  
Ana Maria Gonzalez-Angulo ◽  
Bryan T.J. Hennessy ◽  
Gordon B. Mills
Keyword(s):  
Systems Biology ◽  
Patient Outcomes ◽  
Cost Effective ◽  
Molecular Medicine ◽  
Multidimensional Data ◽  
Data Sets ◽  
Therapeutic Modalities ◽  
Molecular Tests ◽  
Therapeutic Decision Making ◽  
The Masses

The development of cost-effective technologies able to comprehensively assess DNA, RNA, protein, and metabolites in patient tumors has fueled efforts to tailor medical care. Indeed validated molecular tests assessing tumor tissue or patient germline DNA already drive therapeutic decision making. However, many theoretical and regulatory challenges must still be overcome before fully realizing the promise of personalized molecular medicine. The masses of data generated by high-throughput technologies are challenging to manage, visualize, and convert to the knowledge required to improve patient outcomes. Systems biology integrates engineering, physics, and mathematical approaches with biologic and medical insights in an iterative process to visualize the interconnected events within a cell that determine how inputs from the environment and the network rewiring that occurs due to the genomic aberrations acquired by patient tumors determines cellular behavior and patient outcomes. A cross-disciplinary systems biology effort will be necessary to convert the information contained in multidimensional data sets into useful biomarkers that can classify patient tumors by prognosis and response to therapeutic modalities and to identify the drivers of tumor behavior that are optimal targets for therapy. An understanding of the effects of targeted therapeutics on signaling networks and homeostatic regulatory loops will be necessary to prevent inadvertent effects as well as to develop rational combinatorial therapies. Systems biology approaches identifying molecular drivers and biomarkers will lead to the implementation of smaller, shorter, cheaper, and individualized clinical trials that will increase the success rate and hasten the implementation of effective therapies into the clinical armamentarium.

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