Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS

2020 ◽  
Author(s):  
Zhixu Qiu ◽  
Siyuan Chen ◽  
Yuhong Qi ◽  
Chunni Liu ◽  
Jingjing Zhai ◽  
...  
Keyword(s):  
High Throughput ◽  
Stress Responses ◽  
Large Scale ◽  
Model Organisms ◽  
Rna Seq ◽  
Research Groups ◽  
Web Based ◽  
Rich Functionality ◽  
Transcriptional Switch ◽  
User Friendly

Abstract Transcriptional switch (TS) is a widely observed phenomenon caused by changes in the relative expression of transcripts from the same gene, in spatial, temporal or other dimensions. TS has been associated with human diseases, plant development and stress responses. Its investigation is often hampered by a lack of suitable tools allowing comprehensive and flexible TS analysis for high-throughput RNA sequencing (RNA-Seq) data. Here, we present deepTS, a user-friendly web-based implementation that enables a fully interactive, multifunctional identification, visualization and analysis of TS events for large-scale RNA-Seq datasets from pairwise, temporal and population experiments. deepTS offers rich functionality to streamline RNA-Seq-based TS analysis for both model and non-model organisms and for those with or without reference transcriptome. The presented case studies highlight the capabilities of deepTS and demonstrate its potential for the transcriptome-wide TS analysis of pairwise, temporal and population RNA-Seq data. We believe deepTS will help research groups, regardless of their informatics expertise, perform accessible, reproducible and collaborative TS analyses of large-scale RNA-Seq data.

scholarly journals TACITuS: transcriptomic data collector, integrator, and selector on big data platform

2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Salvatore Alaimo ◽  
Antonio Di Maria ◽  
Dennis Shasha ◽  
Alfredo Ferro ◽  
Alfredo Pulvirenti
Keyword(s):  
High Throughput ◽  
Rna Seq ◽  
Web Based ◽  
Large Bandwidth ◽  
Specific Analysis ◽  
Galaxy Environment ◽  
Public Repositories ◽  
Microarray Datasets ◽  
User Friendly ◽  
Ngs Data

Abstract Background Several large public repositories of microarray datasets and RNA-seq data are available. Two prominent examples include ArrayExpress and NCBI GEO. Unfortunately, there is no easy way to import and manipulate data from such resources, because the data is stored in large files, requiring large bandwidth to download and special purpose data manipulation tools to extract subsets relevant for the specific analysis. Results TACITuS is a web-based system that supports rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in an easy and efficient way. The system also supports the ability to import data into Galaxy for further analysis. Conclusions TACITuS automates most of the pre-processing needed to analyze high-throughput microarray and NGS data from large publicly-available repositories. The system implements several modules to manage large files in an easy and efficient way. Furthermore, it is capable deal with Galaxy environment allowing users to analyze data through a user-friendly interface.

scholarly journals CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk

2017 ◽  
Author(s):  
Venkata Manem ◽  
George Adam ◽  
Tina Gruosso ◽  
Mathieu Gigoux ◽  
Nicholas Bertos ◽  
...  
Keyword(s):  
Stromal Cells ◽  
Large Scale ◽  
Network Visualization ◽  
Visualization Tool ◽  
Biological Processes ◽  
Web Based ◽  
Link Type ◽  
A Cell ◽  
Large Scale Networks ◽  
User Friendly

ABSTRACTBackground:Over the last several years, we have witnessed the metamorphosis of network biology from being a mere representation of molecular interactions to models enabling inference of complex biological processes. Networks provide promising tools to elucidate intercellular interactions that contribute to the functioning of key biological pathways in a cell. However, the exploration of these large-scale networks remains a challenge due to their high-dimensionality.Results:CrosstalkNet is a user friendly, web-based network visualization tool to retrieve and mine interactions in large-scale bipartite co-expression networks. In this study, we discuss the use of gene co-expression networks to explore the rewiring of interactions between tumor epithelial and stromal cells. We show how CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks representing the crosstalk occurring between the tumour and its microenvironment.Conclusion:CrosstalkNet serves as a tool to assist biologists and clinicians in exploring complex, large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. A comprehensive tutorial along with case studies are provided with the application.Availability:The web-based application is available at the following location: http://epistroma.pmgenomics.ca/app/. The code is open-source and freely available from http://github.com/bhklab/EpiStroma-webapp.Contact:[email protected]

scholarly journals Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Andreas Friedrich ◽  
Erhan Kenar ◽  
Oliver Kohlbacher ◽  
Sven Nahnsen
Keyword(s):  
Big Data ◽  
Experimental Design ◽  
High Throughput ◽  
Large Scale ◽  
Integrated Design ◽  
Added Value ◽  
Biomedical Data ◽  
Data Generation ◽  
Web Based ◽  
Data Annotation

Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.

scholarly journals A Universal, Genomewide GuideFinder for CRISPR/Cas9 Targeting in Microbial Genomes

mSphere ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Michelle Spoto ◽  
Changhui Guan ◽  
Elizabeth Fleming ◽  
Julia Oh
Keyword(s):  
Gene Function ◽  
Large Scale ◽  
Essential Gene ◽  
Bacterial Species ◽  
Bacterial Genome ◽  
Model Organisms ◽  
Design Parameters ◽  
Bacterial Genomes ◽  
Wide Range ◽  
User Friendly

ABSTRACT The CRISPR/Cas system has significant potential to facilitate gene editing in a variety of bacterial species. CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) represent modifications of the CRISPR/Cas9 system utilizing a catalytically inactive Cas9 protein for transcription repression and activation, respectively. While CRISPRi and CRISPRa have tremendous potential to systematically investigate gene function in bacteria, few programs are specifically tailored to identify guides in draft bacterial genomes genomewide. Furthermore, few programs offer open-source code with flexible design parameters for bacterial targeting. To address these limitations, we created GuideFinder, a customizable, user-friendly program that can design guides for any annotated bacterial genome. GuideFinder designs guides from NGG protospacer-adjacent motif (PAM) sites for any number of genes by the use of an annotated genome and FASTA file input by the user. Guides are filtered according to user-defined design parameters and removed if they contain any off-target matches. Iteration with lowered parameter thresholds allows the program to design guides for genes that did not produce guides with the more stringent parameters, one of several features unique to GuideFinder. GuideFinder can also identify paired guides for targeting multiplicity, whose validity we tested experimentally. GuideFinder has been tested on a variety of diverse bacterial genomes, finding guides for 95% of genes on average. Moreover, guides designed by the program are functionally useful—focusing on CRISPRi as a potential application—as demonstrated by essential gene knockdown in two staphylococcal species. Through the large-scale generation of guides, this open-access software will improve accessibility to CRISPR/Cas studies of a variety of bacterial species. IMPORTANCE With the explosion in our understanding of human and environmental microbial diversity, corresponding efforts to understand gene function in these organisms are strongly needed. CRISPR/Cas9 technology has revolutionized interrogation of gene function in a wide variety of model organisms. Efficient CRISPR guide design is required for systematic gene targeting. However, existing tools are not adapted for the broad needs of microbial targeting, which include extraordinary species and subspecies genetic diversity, the overwhelming majority of which is characterized by draft genomes. In addition, flexibility in guide design parameters is important to consider the wide range of factors that can affect guide efficacy, many of which can be species and strain specific. We designed GuideFinder, a customizable, user-friendly program that addresses the limitations of existing software and that can design guides for any annotated bacterial genome with numerous features that facilitate guide design in a wide variety of microorganisms.

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics

2016 ◽  
Vol 14 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Ruifeng Hu ◽  
Xiaobo Sun
Keyword(s):  
Free Energy ◽  
Nucleic Acid ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binding Free Energy ◽  
Target Prediction ◽  
Easy Access ◽  
Web Based ◽  
Size Limitation ◽  
User Friendly

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

scholarly journals RNA-seq based analysis of population structure within the maize inbred B73

2016 ◽  
Author(s):  
Zhikai Liang ◽  
James C Schnable
Keyword(s):  
Large Scale ◽  
Reference Genome ◽  
Data Sets ◽  
Rna Seq ◽  
Research Groups ◽  
Independent Research ◽  
Sequencing Project ◽  
Maize Genetics ◽  
Outcrossing Species ◽  
The Relationship

B73 is a variety of maize (Zea mays ssp. mays) widely used in genetic, genomic, and phenotypic research around the world. B73 was also served as the reference genotype for the original maize genome sequencing project. The advent of large-scale RNA-sequencing as a method of measuring gene expression presents a unique opportunity to assess the level of relatedness among individuals identified as variety B73. The level of haplotype conservation and divergence across the genome were assessed using 27 RNA-seq data sets from 20 independent research groups in three countries. Several clearly distinct clades were identified among putatively B73 samples. A number of these blocks were defined by the presence of clearly defined genomic blocks containing a haplotype which did not match the published B73 reference genome. In a number of cases the relationship among B73 samples generated by different research groups recapitulated mentor/mentee relationships within the maize genetics community. A number of regions with distinct, dissimilar, haplotypes were identified in our study. However, when considering the age of the B73 accession -- greater than 40 years -- and the challenges of maintaining isogenic lines of a naturally outcrossing species, a strikingly high overall level of conservation was exhibited among B73 samples from around the globe.

scholarly journals NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization

2019 ◽  
Author(s):  
Ayman Yousif ◽  
Nizar Drou ◽  
Jillian Rowe ◽  
Mohammed Khalfan ◽  
Kristin C Gunsalus
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Web Applications ◽  
Rna Seq ◽  
Sequencing Data ◽  
Web Based ◽  
Link Type

AbstractBackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

scholarly journals scDIOR: single cell RNA-seq data IO software

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Huijian Feng ◽  
Lihui Lin ◽  
Jiekai Chen
Keyword(s):  
Single Cell ◽  
Programming Languages ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Rapid Development ◽  
Data Transformation ◽  
Rna Seq ◽  
Data Types ◽  
User Friendly ◽  
Cell Data

Abstract Background Single-cell RNA sequencing is becoming a powerful tool to identify cell states, reconstruct developmental trajectories, and deconvolute spatial expression. The rapid development of computational methods promotes the insight of heterogeneous single-cell data. An increasing number of tools have been provided for biological analysts, of which two programming languages- R and Python are widely used among researchers. R and Python are complementary, as many methods are implemented specifically in R or Python. However, the different platforms immediately caused the data sharing and transformation problem, especially for Scanpy, Seurat, and SingleCellExperiemnt. Currently, there is no efficient and user-friendly software to perform data transformation of single-cell omics between platforms, which makes users spend unbearable time on data Input and Output (IO), significantly reducing the efficiency of data analysis. Results We developed scDIOR for single-cell data transformation between platforms of R and Python based on Hierarchical Data Format Version 5 (HDF5). We have created a data IO ecosystem between three R packages (Seurat, SingleCellExperiment, Monocle) and a Python package (Scanpy). Importantly, scDIOR accommodates a variety of data types across programming languages and platforms in an ultrafast way, including single-cell RNA-seq and spatial resolved transcriptomics data, using only a few codes in IDE or command line interface. For large scale datasets, users can partially load the needed information, e.g., cell annotation without the gene expression matrices. scDIOR connects the analytical tasks of different platforms, which makes it easy to compare the performance of algorithms between them. Conclusions scDIOR contains two modules, dior in R and diopy in Python. scDIOR is a versatile and user-friendly tool that implements single-cell data transformation between R and Python rapidly and stably. The software is freely accessible at https://github.com/JiekaiLab/scDIOR.

scholarly journals The Framework for Web-Based Automated Online Question Paper Generator through JEE

2018 ◽  
Vol 7 (3) ◽  
pp. 1415
Author(s):  
Vinayak Hegde ◽  
Lavanya V Rao ◽  
Shivali B S
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Educational Institution ◽  
Faculty Members ◽  
Educational Institutions ◽  
Small Scale ◽  
Web Based ◽  
Educational Institute ◽  
User Friendly ◽  
Word Format

Examinations are an indispensable part of a student’s life. In the conventional mechanism, the question paper generation is time-consuming work for the faculty members of the educational institution. Every educational institute mandatorily expects exam setters to follow its own typesetting format. We have designed the automated question paper setting software to be user-friendly so that, paper setters can overcome from the typographic problem. Presently in most of the educational institutions question papers are set manually. It is time-consuming work and there may be chances of repetition of the same questions. So, in order to make the question paper generation more convenient to use, the web application is developed using Java Enterprise Edition (JEE) that can be accessed from LAN/Intranet.The application comes with the Admin Module and Teachers Module. The Admin grants access to the users by registering them. The faculty can access the system once they are registered. The faculty can enter questions in the database daily as per their free time. In this way, the question pool can be generated. The questions are approved by the chairperson and substandard questions are discarded. The question paper is then generated by selected course experts. The Fisher-Yates Shuffling algorithm used to choose questions randomly from the pool of questions from the database. Text Mining Algorithm aids in duplicity removal from the paper.  The generated question paper will be in Word Format. In our application, we assure better security, removal of duplicity, cost-effectiveness, and human intervention avoidance. It can be used by small-scale and large-scale institutions.  

scholarly journals BtToxin_Digger: a comprehensive and high-throughput pipeline for mining toxin protein genes from Bacillus thuringiensis

2020 ◽  
Author(s):  
Hualin Liu ◽  
Jinshui Zheng ◽  
Dexin Bo ◽  
Yun Yu ◽  
Weixing Ye ◽  
...  
Keyword(s):  
Bacillus Thuringiensis ◽  
High Throughput ◽  
Large Scale ◽  
Web Based ◽  
Bt Toxin ◽  
Link Type ◽  
Toxin Genes ◽  
Toxin Protein ◽  
Mining Tool ◽  
Downstream Analysis

SummaryBacillus thuringiensis (Bt) which is a spore-forming gram-positive bacterium, has been used as the most successful microbial pesticide for decades. Its toxin genes (cry) have been successfully used for the development of GM crops against pests. We have previously developed a web-based insecticidal gene mining tool BtToxin_scanner, which has been proved to be the most important method for mining cry genes from Bt genome sequences. To facilitate efficiently mining major toxin genes and novel virulence factors from large-scale Bt genomic data, we re-design this tool with a new workflow. Here we present BtToxin_Digger, a comprehensive, high-throughput, and easy-to-use Bt toxin mining tool. It runs fast and can get rich, accurate, and useful results for downstream analysis and experiment designs. Moreover, it can also be used to mine other targeting genes from large-scale genome and metagenome data with the addition of other query sequences.Availability and ImplementationThe BtToxin_Digger codes and instructions are freely available at https://github.com/BMBGenomics/BtToxin_Digger. A web server of BtToxin_Digger can be found at http://bcam.hzau.edu.cn/[email protected]; [email protected].

Sign in / Sign up

Export Citation Format

Share Document