(w)HOL(e)ISTIC gene ontology and pathway analysis of data using open source web tools

2020 ◽  
Author(s):  
Damarius S. Fleming ◽  
Laura Miller
Keyword(s):  
Gene Ontology ◽  
Open Source ◽  
Biological Networks ◽  
Genomic Databases ◽  
Network Analyses ◽  
Go Enrichment ◽  
Downstream Analysis ◽  
Species Specific ◽  
Stage 1 ◽  
Next Generation Sequencing Ngs

Abstract Objective Downstream analysis of next generation sequencing (NGS) experiments provides researchers a means of deciphering their results. These downstream analyses elucidate clusters of genes or networks of biological interest under the conditions being studied. One convention for examining gene interactions is to conduct downstream investigations based on gene ontology (GO), pathway, and network analyses of statistically significant genes of interest. Unfortunately, the software available for these types of analyses is expensive, not species specific, and subject to gaps in annotation. These difficulties can cause studies to omit this downstream step, limiting the utility of the data. In order to facilitate pathway and network analyses of candidate gene lists from NGS studies, a workflow was constructed based on the use of open-sourced freely available software and genomic databases termed the “(w)HOL(e)ISTIC GO enrichment” approach.Results Overlap of multiple open-source software was used to annotate, analyze, and visualize biological networks. It is a 3-stage process in which stage 1 (Annotation) is the generation of alias identifiers. Stage 2 (Analysis) is a two-part process generating ontologies and networks with statistical inferences. Stage 2 relies on information from databases such as Reactome, KEGG, and InterPro. Stage 3 (Visualization) allows for figure creation.

Mechanistic Investigation of Glycyrrhiza uralensis Effects against Respiratory Ailments: Application of Network Pharmacology and Molecular Docking Approaches

2021 ◽  
Vol 18 ◽  
Author(s):  
Munazza Ijaz ◽  
Xianju Huang ◽  
Manal Buabeid ◽  
Tahir Ali Chohan ◽  
Ghulam Murtaza ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Molecular Docking ◽  
Herbal Remedy ◽  
Database Search ◽  
Glycyrrhiza Uralensis ◽  
Network Pharmacology ◽  
Careful Study ◽  
Protein Targets ◽  
Go Enrichment ◽  
Mechanistic Investigation

Background: Glycyrrhiza uralensis, also known as liquorice, is a herbal remedy that is traditionally used worldwide for treating respiratory ailments and ameliorating breathing. Objective: The objective of this systematic study was to investigate active ingredients of Glycyrrhiza uralensis and determine its mode of action in silico against severe and acute respiratory complications of respiratory ailments through network pharmacology and molecular docking studies. Methods: TCMSP database search helped retrieve the compounds of Glycyrrhiza uralensis and their protein targets, especially related to respiratory ailments. Subsequently, the protein-protein association was attained as a network by using the STITCH database. Cytoscape and its ClueGO plugin were used to study gene ontology (GO) enrichment. In addition, seven natural compounds were docked in the active site of four different molecular targets; JUN-FOS, COX2, MAPK14 and IL-6, to identify the binding mechanism of ligands under study. Results: TCMSP database search resulted in the retrieval of 280 compounds of Glycyrrhiza uralensis (including formononetin, naringenin, sitosterol, isorhamnetin, kaempferol, quercetin and Glycyrrhizin) and 135 protein targets. A careful study of targets showed that 26 prospective targets (including JUN, FOS, IL6, MAPK14 and PTGS2) related to respiratory ailments were identified. Gene ontology (GO) enrichment analysis resulted in the retrieval of 176 GO terms, which were associated with respiratory ailments. This study proposed that Glycyrrhiza uralensis acts against respiratory ailments through various proteins, such as JUN, FOS, IL6, MAPK14 and PTGS2. Docking results revealed that among all studied ligands, the flavonoid-based compounds isorhamnetin and kaempferol form stronger complexes with JUN-FOS-DNA, MAPK-14, and IL-6 proteins (Cscore=6.81, 4.27, and 4.77, respectively) and the saponin based compound glycyrrhizin (Cscore=13.07) demonstrated stronger binding affinity towards COX2 enzyme. Conclusion: Conclusively, isorhamnetin, kaempferol and glycyrrhizin in Glycyrrhiza uralensis may regulate several signaling pathways through JUN-FOS-DNA, MAPK-14, and IL-6, which might play a therapeutic role against respiratory ailments.

Transcriptome Signatures Reveal Candidate Key Genes in the Peripheral Blood Mononuclear Cells of Patients With Coronary Artery Disease

2020 ◽  
Author(s):  
Vijayakrishna Kolur ◽  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti ◽  
Anandkumar Tengli
Keyword(s):  
Coronary Artery Disease ◽  
Gene Ontology ◽  
Coronary Artery ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Go Enrichment ◽  
Artery Disease ◽  
Go Enrichment Analysis

Abstract BackgroundCoronary artery disease (CAD) is one of the most common disorders in the cardiovascular system. This study aims to explore potential signaling pathways and important biomarkers that drive CAD development. MethodsThe CAD GEO Dataset GSE113079 was featured to screen differentially expressed genes (DEGs). The pathway and Gene Ontology (GO) enrichment analysis of DEGs were analyzed using the ToppGene. We screened hub and target genes from protein-protein interaction (PPI) networks, target gene - miRNA regulatory network and target gene - TF regulatory network, and Cytoscape software. Validations of hub genes were performed to evaluate their potential prognostic and diagnostic value for CAD. Results1,036 DEGs were captured according to screening criteria (525upregulated genes and 511downregulated genes). Pathway and Gene Ontology (GO) enrichment analysis of DEGs revealed that these up and down regulated genes are mainly enriched in thyronamine and iodothyronamine metabolism, cytokine-cytokine receptor interaction, nervous system process, cell cycle and nuclear membrane. Hub genes were validated to find out potential prognostic biomarkers, diagnostic biomarkers and novel therapeutic target for CAD. ConclusionsIn summary, our findings discovered pivotal gene expression signatures and signaling pathways in the progression of CAD. CAPN13, ACTBL2, ERBB3, GATA4, GNB4, NOTCH2, EXOSC10, RNF2, PSMA1 and PRKAA1 might contribute to the progression of CAD, which could have potential as biomarkers or therapeutic targets for CAD.

scholarly journals Offline Next Generation Metagenomics Sequence Analysis Using MinION Detection Software (MINDS)

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 578 ◽  
Author(s):  
Deshpande ◽  
Reed ◽  
Sullivan ◽  
Kerkhof ◽  
Beigel ◽  
...  
Keyword(s):  
Open Source ◽  
Cost Effective ◽  
Ease Of Use ◽  
Remote Locations ◽  
Resource Limited ◽  
Field Samples ◽  
Sample Characterization ◽  
Internet Availability ◽  
Detection Software ◽  
Downstream Analysis

Field laboratories interested in using the MinION often need the internet to perform sample analysis. Thus, the lack of internet connectivity in resource-limited or remote locations renders downstream analysis problematic, resulting in a lack of sample identification in the field. Due to this dependency, field samples are generally transported back to the lab for analysis where internet availability for downstream analysis is available. These logistics problems and the time lost in sample characterization and identification, pose a significant problem for field scientists. To address this limitation, we have developed a stand-alone data analysis packet using open source tools developed by the Nanopore community that does not depend on internet availability. Like Oxford Nanopore Technologies’ (ONT) cloud-based What’s In My Pot (WIMP) software, we developed the offline MinION Detection Software (MINDS) based on the Centrifuge classification engine for rapid species identification. Several online bioinformatics applications have been developed surrounding ONT’s framework for analysis of long reads. We have developed and evaluated an offline real time classification application pipeline using open source tools developed by the Nanopore community that does not depend on internet availability. Our application has been tested on ATCC’s 20 strain even mix whole cell (ATCC MSA-2002) sample. Using the Rapid Sequencing Kit (SQK-RAD004), we were able to identify all 20 organisms at species level. The analysis was performed in 15 min using a Dell Precision 7720 laptop. Our offline downstream bioinformatics application provides a cost-effective option as well as quick turn-around time when analyzing samples in the field, thus enabling researchers to fully utilize ONT’s MinION portability, ease-of-use, and identification capability in remote locations.

scholarly journals The CELLmicrocosmos Tools: A Small History of Java-Based Cell and Membrane Modelling Open Source Software Development

2019 ◽  
Vol 16 (3) ◽  
Author(s):  
Bjorn Sommer
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Biological Networks ◽  
Special Focus ◽  
Database Integration ◽  
Software Suite ◽  
Software Frameworks ◽  
Cell Components ◽  
History Of ◽  
Student Projects

AbstractFor more than one decade, CELLmicrocosmos tools are being developed. Here, we discus some of the technical and administrative hurdles to keep a software suite running so many years. The tools were being developed during a number of student projects and theses, whereas main developers refactored and maintained the code over the years. The focus of this publication is laid on two Java-based Open Source Software frameworks. Firstly, the CellExplorer with the PathwayIntegration combines the mesoscopic and the functional level by mapping biological networks onto cell components using database integration. Secondly, the MembraneEditor enables users to generate membranes of different lipid and protein compositions using the PDB format. Technicalities will be discussed as well as the historical development of these tools with a special focus on group-based development. In this way, university-associated developers of Integrative Bioinformatics applications should be inspired to go similar ways. All tools discussed in this publication can be downloaded and installed from https://www.CELLmicrocosmos.org.

scholarly journals DNA Rchitect: an R based visualizer for network analysis of chromatin interaction data

2019 ◽  
Author(s):  
R N Ramirez ◽  
K Bedirian ◽  
S M Gray ◽  
A Diallo
Keyword(s):  
Open Source ◽  
Web Application ◽  
Genomic Data ◽  
Chromatin Interaction ◽  
Chromatin Conformation ◽  
Interaction Data ◽  
Data Types ◽  
Network Analyses ◽  
Interactive Network ◽  
Network Functionality

Abstract Motivation Visualization of multiple genomic data generally requires the use of public or commercially hosted browsers. Flexible visualization of chromatin interaction data as genomic features and network components offer informative insights to gene expression. An open source application for visualizing HiC and chromatin conformation-based data as 2D-arcs accompanied by interactive network analyses is valuable. Results DNA Rchitect is a new tool created to visualize HiC and chromatin conformation-based contacts at high (Kb) and low (Mb) genomic resolutions. The user can upload their pre-filtered HiC experiment in bedpe format to the DNA Rchitect web app that we have hosted or to a version they themselves have deployed. Using DNA Rchitect, the uploaded data allows the user to visualize different interactions of their sample, perform simple network analyses, while also offering visualization of other genomic data types. The user can then download their results for additional network functionality offered in network based programs such as Cytoscape. Availability and implementation DNA Rchitect is freely available both as a web application written primarily in R available at http://shiny.immgen.org/DNARchitect/ and as an open source released under an MIT license at: https://github.com/alosdiallo/DNA_Rchitect.

scholarly journals Genomic Approach to Understand the Association of DNA Repair with Longevity and Healthy Aging Using Genomic Databases of Oldest-Old Population

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yeo Jin Kim ◽  
Hyun Soo Kim ◽  
Young Rok Seo
Keyword(s):  
Dna Repair ◽  
Biological Networks ◽  
Genetic Variants ◽  
Healthy Aging ◽  
Oldest Old ◽  
Genomic Research ◽  
Human Longevity ◽  
Genomic Databases ◽  
Aged Population ◽  
Genomic Study

Aged population is increasing worldwide due to the aging process that is inevitable. Accordingly, longevity and healthy aging have been spotlighted to promote social contribution of aged population. Many studies in the past few decades have reported the process of aging and longevity, emphasizing the importance of maintaining genomic stability in exceptionally long-lived population. Underlying reason of longevity remains unclear due to its complexity involving multiple factors. With advances in sequencing technology and human genome-associated approaches, studies based on population-based genomic studies are increasing. In this review, we summarize recent longevity and healthy aging studies of human population focusing on DNA repair as a major factor in maintaining genome integrity. To keep pace with recent growth in genomic research, aging- and longevity-associated genomic databases are also briefly introduced. To suggest novel approaches to investigate longevity-associated genetic variants related to DNA repair using genomic databases, gene set analysis was conducted, focusing on DNA repair- and longevity-associated genes. Their biological networks were additionally analyzed to grasp major factors containing genetic variants of human longevity and healthy aging in DNA repair mechanisms. In summary, this review emphasizes DNA repair activity in human longevity and suggests approach to conduct DNA repair-associated genomic study on human healthy aging.

scholarly journals Mining Gene Ontology Data with AGENDA

2012 ◽  
Vol 6 ◽  
pp. BBI.S9101
Author(s):  
Guvanch Ovezmyradov ◽  
Qianhao Lu ◽  
Martin C. Göpfert
Keyword(s):  
Gene Ontology ◽  
Open Source ◽  
Genetic Information ◽  
Collaborative Effort ◽  
Controlled Vocabularies ◽  
Web Based ◽  
Commercial Use ◽  
Diverse Species ◽  
Go Terms ◽  
The Web

The Gene Ontology (GO) initiative is a collaborative effort that uses controlled vocabularies for annotating genetic information. We here present AGENDA (Application for mining Gene Ontology Data), a novel web-based tool for accessing the GO database. AGENDA allows the user to simultaneously retrieve and compare gene lists linked to different GO terms in diverse species using batch queries, facilitating comparative approaches to genetic information. The web-based application offers diverse search options and allows the user to bookmark, visualize, and download the results. AGENDA is an open source web-based application that is freely available for non-commercial use at the project homepage. URL: http://sourceforge.net/projects/bioagenda .

scholarly journals The DOMINO web-server for active module identification analysis

2021 ◽  
Author(s):  
Hagai Levi ◽  
Nima Rahmanian ◽  
Ran Elkon ◽  
Ron Shamir
Keyword(s):  
Gene Network ◽  
Enrichment Analysis ◽  
Molecular Processes ◽  
Activity Profile ◽  
Module Identification ◽  
Go Enrichment ◽  
Downstream Analysis ◽  
User Friendly ◽  
Identification Analysis ◽  
Go Enrichment Analysis

Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"). Such modules can point out key molecular processes in the analyzed biological conditions. We recently introduced a novel AMI algorithm called DOMINO, and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations, and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms. The server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.

scholarly journals Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis)

2019 ◽  
Vol 27 (2) ◽  
pp. 59-66
Author(s):  
Sándor Zsebők ◽  
Máté Ferenc Nagy-Egri ◽  
Gergely Gábor Barnaföldi ◽  
Miklós Laczi ◽  
Gergely Nagy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Detection System ◽  
Automatic Process ◽  
Ficedula Albicollis ◽  
Discrete Elements ◽  
Acoustic Data ◽  
Collared Flycatcher ◽  
Order Of Magnitude ◽  
Species Specific

Abstract The bioacoustic analyses of animal sounds result in an enormous amount of digitized acoustic data, and we need effective automatic processing to extract the information content of the recordings. Our research focuses on the song of Collared Flycatcher (Ficedula albicollis) and we are interested in the evolution of acoustic signals. During the last 20 years, we obtained hundreds of hours of recordings of bird songs collected in natural environment, and there is a permanent need for the automatic process of recordings. In this study, we chose an open-source, deep-learning image detection system to (1) find the species-specific songs of the Collared Flycatcher on the recordings and (2) to detect the small, discrete elements so-called syllables within the song. For these tasks, we first transformed the acoustic data into spectrogram images, then we trained two deep-learning models separately on our manually segmented database. The resulted models detect the songs with an intersection of union higher than 0.8 and the syllables higher than 0.7. This technique anticipates an order of magnitude less human effort in the acoustic processing than the manual method used before. Thanks to the new technique, we are able to address new biological questions that need large amount of acoustic data.

scholarly journals Organisation of the transcriptional regulation of genes involved in protein transactions in yeast

2017 ◽  
Author(s):  
Duygu Dikicioglu ◽  
Daniel J H Nightingale ◽  
Valerie Wood ◽  
Kathryn S Lilley ◽  
Stephen G Oliver
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Topological Analysis ◽  
Network Size ◽  
Transcriptional Level ◽  
Transcriptional Regulatory Networks ◽  
Network Function ◽  
Network Analyses ◽  
Transcriptional Regulatory

AbstractThe topological analyses of many large-scale molecular interaction networks often provide only limited insights into network function or evolution. In this paper, we argue that the functional heterogeneity of network components, rather than network size, is the main factor limiting the utility of topological analysis of large cellular networks. We have analysed large epistatic, functional, and transcriptional regulatory networks of genes that were attributed to the following biological process groupings: protein transactions, gene expression, cell cycle, and small molecule metabolism. Control analyses were performed on networks of randomly selected genes. We identified novel biological features emerging from the analysis of functionally homogenous biological networks irrespective of their size. In particular, direct regulation by transcription as an underrepresented feature of protein transactions. The analysis also demonstrated that the regulation of the genes involved in protein transactions at the transcriptional level was orchestrated by only a small number of regulators. Quantitative proteomic analysis of nuclear- and chromatin-enriched sub-cellular fractions of yeast provided supportive evidence for the conclusions generated by network analyses.

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