scholarly journals OmicsX: a web server for integrated OMICS analysis

2019 ◽  
Author(s):  
Jianbo Pan ◽  
Jiang Qian ◽  
Hui Zhang
Keyword(s):  
Web Server ◽  
Biological Molecules ◽  
Data Sets ◽  
Omics Data ◽  
Annotation Information ◽  
Integrated Omics ◽  
User Friendly ◽  
Deep Exploration ◽  
Modified Forms ◽  
Omic Data

ABSTRACTHigh-throughput omic data sets like genomics, transcriptomics, proteomics, glycomics, lipitomics, metabolomics, and modified forms of the biological molecules have been generated to investigate biological mechanisms. Accordingly, bioinformatic methods and tools have been developed and applied to analyze the omics data for different purposes. However, a lack of comparison and integration tools impedes the deep exploration of multi-omics data. OmicsX is a user-friendly web server for integration and comparison of different omic datasets with optional sample annotation information. The tool includes modules for gene-wise correlation, sample-wise correlation, subtype clustering, and differential expression. OmicsX provides researchers the analysis results with visualization, searching, and data downloading, and help to suggest the biological indications from the comparison of different omic data.Availability and implementationhttp://bioinfo.wilmer.jhu.edu/OmicsX/

scholarly journals IOBR: Multi-omics Immuno-Oncology Biological Research to decode tumor microenvironment and signatures

2020 ◽  
Author(s):  
Dongqiang Zeng ◽  
Zilan Ye ◽  
Guangchuang Yu ◽  
Jiani Wu ◽  
Yi Xiong ◽  
...  
Keyword(s):  
Clinical Success ◽  
Immune Checkpoint Blockade ◽  
Biological Research ◽  
Omics Data ◽  
Sequencing Data ◽  
Intrinsic Mechanism ◽  
Integrated Omics ◽  
Cancer Settings ◽  
User Friendly ◽  
Generation Sequencing

Motivation: Recent advance in next generation sequencing has triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to exploring robust signatures for clinical translation is increasingly highlighted, attributed to the clinical success of immune checkpoint blockade in diverse malignancies. However, effective tools to comprehensively interpret multi-omics data is still warranted to provide increased granularity into intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Results: We developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing comprehensive investigation of estimation of reported or user-built signatures, TME deconvolution and signature construction base on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, lncRNA profiling, genomic characteristics and signatures generated from single-cell RNA sequencing data in different cancer settings. Additionally, IOBR also integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool, to leverage multi-omics data to facilitate immuno-oncology exploration and unveiling of tumor-immune interactions and accelerating precision immunotherapy.

scholarly journals BatchServer: a web server for batch effect evaluation, visualization and correction

2020 ◽  
Author(s):  
Tiansheng Zhu ◽  
Guo-Bo Chen ◽  
Chunhui Yuan ◽  
Rui Sun ◽  
Fangfei Zhang ◽  
...  
Keyword(s):  
Web Server ◽  
Batch Effect ◽  
Variance Component Analysis ◽  
Data Sets ◽  
Batch Effects ◽  
Source Codes ◽  
Link Type ◽  
R Shiny ◽  
User Friendly ◽  
Web Platform

AbstractBatch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects are necessary for omics data analysis. In order to facilitate the evaluation and correction of batch effects, here we present BatchSever, an open-source R/Shiny based user-friendly interactive graphical web platform for batch effects analysis. In BatchServer we introduced autoComBat, a modified version of ComBat, which is the most widely adopted tool for batch effect correction. BatchServer uses PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) for evaluation and visualizion of batch effects. We demonstate its application in multiple proteomics and transcriptomic data sets. BatchServer is provided at https://lifeinfo.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/guomics-lab/batch_server.

scholarly journals AGP: A Multimethods Web Server for Alignment-Free Genome Phylogeny

2013 ◽  
Vol 30 (5) ◽  
pp. 1032-1037 ◽  
Author(s):  
Jinkui Cheng ◽  
Fuliang Cao ◽  
Zhihua Liu
Keyword(s):  
Web Server ◽  
Data Sets ◽  
Whole Genome ◽  
Alignment Method ◽  
Genome Sequences ◽  
Alignment Free ◽  
Tree Construction ◽  
Comparison Functions ◽  
Genome Phylogeny ◽  
User Friendly

Abstract Phylogenetic analysis based on alignment method meets huge challenges when dealing with whole-genome sequences, for example, recombination, shuffling, and rearrangement of sequences. Thus, various alignment-free methods for phylogeny construction have been proposed. However, most of these methods have not been implemented as tools or web servers. Researchers cannot use these methods easily with their data sets. To facilitate the usage of various alignment-free methods, we implemented most of the popular alignment-free methods and constructed a user-friendly web server for alignment-free genome phylogeny (AGP). AGP integrated the phylogenetic tree construction, visualization, and comparison functions together. Both AGP and all source code of the methods are available at http://www.herbbol.org:8000/agp (last accessed February 26, 2013). AGP will facilitate research in the field of whole-genome phylogeny and comparison.

scholarly journals State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing

2020 ◽  
Vol 11 ◽  
Author(s):  
Michal Krassowski ◽  
Vivek Das ◽  
Sangram K. Sahu ◽  
Biswapriya B. Misra
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Best Practices ◽  
Biological Process ◽  
Data Sets ◽  
Omics Data ◽  
Practical Advice ◽  
Current State ◽  
Integrated Omics ◽  
State Of The Field

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods’ limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.

scholarly journals mCSM-membrane: predicting the effects of mutations on transmembrane proteins

2020 ◽  
Vol 48 (W1) ◽  
pp. W147-W153 ◽  
Author(s):  
Douglas E V Pires ◽  
Carlos H M Rodrigues ◽  
David B Ascher
Keyword(s):  
Membrane Proteins ◽  
Single Point ◽  
Point Mutations ◽  
Web Server ◽  
Protein Coding ◽  
Pathogenic Variants ◽  
Model Protein ◽  
Matthew’S Correlation Coefficient ◽  
Membrane Protein Stability ◽  
User Friendly

Abstract Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.

Applied plant science experimental design and statistical analysis using the SAS® OnDemand for Academics

2021 ◽  
Keyword(s):  
Statistical Analysis ◽  
Agricultural Production ◽  
Science Research ◽  
Plant Science ◽  
Data Sets ◽  
Statistical Analysis System ◽  
Key Topics ◽  
Analysis System ◽  
User Friendly ◽  
Enormous Number

Abstract The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide. The enormous number of design and analysis options available for correctly implementing, analyzing and interpreting research can be overwhelming. Statistical Analysis System (SAS®) is the most widely used statistical software in the world and SAS® OnDemand for Academics is now freely available for academic insttutions. This is a user-friendly guide to statistics using SAS® OnDemand for Academics, ideal for facilitating the design and analysis of plant science experiments. It presents the most frequently used statistical methods in an easy-to-follow and non-intimidating fashion, and teaches the appropriate use of SAS® within the context of plant science research. This book contains 21 chapters that covers experimental designs and data analysis protocols; is presented as a how-to guide with many examples; includes freely downloadable data sets; and examines key topics such as ANOVA, mean separation, non-parametric analysis and linear regression.

scholarly journals Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Nanomaterials ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2385
Author(s):  
Tae Hwan Shin ◽  
Saraswathy Nithiyanandam ◽  
Da Yeon Lee ◽  
Do Hyeon Kwon ◽  
Ji Su Hwang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Biomedical Applications ◽  
Traction Force ◽  
Molecular Profiling ◽  
Omics Data ◽  
Network Integration ◽  
Rigidity Sensing ◽  
Integrated Omics ◽  
Biophysical Changes ◽  
Integration Analysis

Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

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 Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Natalie M. Clark ◽  
Trevor M. Nolan ◽  
Ping Wang ◽  
Gaoyuan Song ◽  
Christian Montes ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Cell Division ◽  
Network Inference ◽  
Phosphorylation Sites ◽  
Molecular Signaling ◽  
Signaling Events ◽  
Spatiotemporal Clustering ◽  
Integrated Omics ◽  
Omic Data ◽  
Integrative Network

AbstractBrassinosteroids (BRs) are plant steroid hormones that regulate cell division and stress response. Here we use a systems biology approach to integrate multi-omic datasets and unravel the molecular signaling events of BR response in Arabidopsis. We profile the levels of 26,669 transcripts, 9,533 protein groups, and 26,617 phosphorylation sites from Arabidopsis seedlings treated with brassinolide (BL) for six different lengths of time. We then construct a network inference pipeline called Spatiotemporal Clustering and Inference of Omics Networks (SC-ION) to integrate these data. We use our network predictions to identify putative phosphorylation sites on BES1 and experimentally validate their importance. Additionally, we identify BRONTOSAURUS (BRON) as a transcription factor that regulates cell division, and we show that BRON expression is modulated by BR-responsive kinases and transcription factors. This work demonstrates the power of integrative network analysis applied to multi-omic data and provides fundamental insights into the molecular signaling events occurring during BR response.

scholarly journals SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

2020 ◽  
Author(s):  
Anna M. Sozanska ◽  
Charles Fletcher ◽  
Dóra Bihary ◽  
Shamith A. Samarajiwa
Keyword(s):  
High Throughput ◽  
R Package ◽  
Data Reuse ◽  
Massively Parallel ◽  
Data Sets ◽  
Similar Data ◽  
Data Generation ◽  
Data Repositories ◽  
Public Data ◽  
Omic Data

AbstractMore than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets.Availabilityhttps://github.com/ss-lab-cancerunit/SpiderSeqR

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