scholarly journals PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data

2018 ◽  
Author(s):  
Rafael Hernández-de-Diego ◽  
Sonia Tarazona ◽  
Carlos Martínez-Mira ◽  
Leandro Balzano-Nogueira ◽  
Pedro Furió-Tarí ◽  
...  

ABSTRACTThe increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental to understand interconnections across molecular layers and to fully leverage the biology discovery power offered by the multi-omics approach.We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information.Unlike other visualization tools, PaintOmics 3 covers a complete pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, etc. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at http://bioinfo.cipf.es/paintomics/.

2021 ◽  
Author(s):  
Thomas Naake ◽  
Wolfgang Huber

Motivation: First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics, metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs x samples). Efficient and standardized data-quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality data sets and subsequent biological question-driven inference. Results: We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R's shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs x samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows. Availability: MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license.


2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Benjamin Ulfenborg

Abstract Background Studies on multiple modalities of omics data such as transcriptomics, genomics and proteomics are growing in popularity, since they allow us to investigate complex mechanisms across molecular layers. It is widely recognized that integrative omics analysis holds the promise to unlock novel and actionable biological insights into health and disease. Integration of multi-omics data remains challenging, however, and requires combination of several software tools and extensive technical expertise to account for the properties of heterogeneous data. Results This paper presents the miodin R package, which provides a streamlined workflow-based syntax for multi-omics data analysis. The package allows users to perform analysis of omics data either across experiments on the same samples (vertical integration), or across studies on the same variables (horizontal integration). Workflows have been designed to promote transparent data analysis and reduce the technical expertise required to perform low-level data import and processing. Conclusions The miodin package is implemented in R and is freely available for use and extension under the GPL-3 license. Package source, reference documentation and user manual are available at https://gitlab.com/algoromics/miodin.


2018 ◽  
Vol 46 (W1) ◽  
pp. W503-W509 ◽  
Author(s):  
Rafael Hernández-de-Diego ◽  
Sonia Tarazona ◽  
Carlos Martínez-Mira ◽  
Leandro Balzano-Nogueira ◽  
Pedro Furió-Tarí ◽  
...  

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuning Hou ◽  
Qingling Hao ◽  
Zhiwei Zhu ◽  
Dongmei Xu ◽  
Wenzhong Liu ◽  
...  

Abstract Background In previous study, we performed next-gene sequencing to investigate the differentially expressed transcripts of bovine follicular granulosa cells (GCs) at dominant follicle (DF) and subordinate follicle (SF) stages during first follicular wave. Present study is designed to further identify the key regulatory proteins and signaling pathways associated with follicular development using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multi-omics data analysis approach. Methods DF and SF from three cattle were collected by daily ultrasonography. The GCs were isolated from each follicle, total proteins were digested by trypsin, and then proteomic analyzed via LC-MS/MS, respectively. Proteins identified were retrieved from Uniprot-COW fasta database, and differentially expressed proteins were used to functional enrichment and KEGG pathway analysis. Proteome data and transcriptome data obtained from previous studies were integrated. Results Total 3409 proteins were identified from 30,321 peptides (FDR ≤0.01) obtained from LC-MS/MS analysis and 259 of them were found to be differentially expressed at different stage of follicular development (fold Change > 2, P < 0.05). KEGG pathway analysis of proteome data revealed important signaling pathways associated with follicular development, multi-omics data analysis results showed 13 proteins were identified as being differentially expressed in DF versus SF. Conclusions This study represents the first investigation of transcriptome and proteome of bovine follicles and offers essential information for future investigation of DF and SF in cattle. It also will enrich the theory of animal follicular development.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jordi Martorell-Marugán ◽  
Raúl López-Domínguez ◽  
Adrián García-Moreno ◽  
Daniel Toro-Domínguez ◽  
Juan Antonio Villatoro-García ◽  
...  

Abstract Background Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Results Here, we present Autoimmune Diseases Explorer (https://adex.genyo.es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. Conclusions This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.


2017 ◽  
Author(s):  
◽  
Siva Ratna Kumari Narisetti

Multi-level 'OMICS' data integration for multiple organisms has been one of the major challenges in the era of advanced next generation sequencing and high performance technologies. Biological data has been producing tremendously fast with the availability of these high throughput sequencing technologies at low price and high speed. However, these data are often stored individually across different web resources based on data type and organism, making it difficult to find and integrate them. There are many websites available which store data from different data types and display that data in pie charts or plain text format but limit their data to only one fixed organism. These web-based multi-omics analysis is an efficient and easy way of analyzing the data but it would be difficult for other researchers working with other organisms and with complex data. The complex multi-omics data requires extensive data management, exhaustive computational analysis, and effective integration to have a one-stop interactive, web-based portal to browse, access, analyze, integrate and share knowledge about genomics and molecular mechanisms, with ultimate links to phenotypes and traits for many different organisms. To achieve this, we have developed Knowledge Base Commons (KBCommons), a platform that automates the process of establishing the database and making the tools available for organisms via a dedicated web resource. KBCommons is currently supporting four different categories including Plants and Crops; Animals and Pets; Humans and Diseases; Microbes and Viruses. It has four main functionalities including Browse KBCommons, Contribute to KB, Add version to KB, and Create a new KB. Using KBCommons, researchers from different groups with different organisms' data can be shared and accessed among all. KBCommons is an automatic framework which uses famous and widely used Laravel PHP framework. This is very efficient to deal with complex and diverse biological datasets. In the Browse KBCommons section, all existing organisms will be displayed under each category and it also shows organisms which can be used as model organisms. KBCommons also displays the logo of each organism along with existing versions, in this way it will give a detailed information on all existing organisms. The user can browse existing data of each organism using various tools including Blast, Multiple Sequence Alignment, Motif Sampler, etc., by going to that particular page. Users can also visualize gene expression and differential expression data via pie charts and plain text. Add version to KB and Create a new KB are related because of their similar steps in the process, users must bring corresponding data in each section. When a particular organism of interest is not existing then the user can create a new Knowledge Base for that new organism with 6 essential files of Genome Sequence, protein coding sequence for Amino acid, gene coding sequence for Nucleotide and Spliced mRNA transcripts, mRNA sequences in GFF3, and a functional annotation file. In Add version to KB, if an organism is already existing then the user can add a new version to the existing KB with these 6 essential files for the new version. In Contribute to KB, user can upload multi-omics data including Transcriptomics -- RNA-Seq and Microarray; Proteomics -- Mass Spectrometry and 2DGel; Epigenomics -- Bisulphite Sequencing, Methylation Array, and MBD-Seq Array. We support both gene expression/ protein expression/ or methylation data and differential expression comparison for each data type. We also support different entities including miRNA/sRNA, Metabolite, SNP/GWAS, Plant introduction lines/ Animal strains, and Phenotype/ TRAIT/Diseases.


2018 ◽  
Author(s):  
Benjamin Ulfenborg

AbstractBackgroundStudies on multiple modalities of omics data such as transcriptomics, genomics and proteomics are growing in popularity, since they allow us to investigate complex mechanisms across molecular layers. It is widely recognized that integrative omics analysis holds the promise to unlock novel and actionable biological insights to health and disease. Integration of multi-omics data remains challenging, however, and requires combination of several software tools and extensive technical expertise to account for the properties of heterogeneous data.ResultsThis paper presents the miodin R package, which provides a streamlined workflow-based syntax for multi-omics data analysis. The package allows users to perform analysis and integration of omics data either across experiments on the same samples, or across studies on the same variables. Workflows have been designed to promote transparent data analysis and reduce the technical expertise required to perform low-level data import and processing.ConclusionsThe miodin package is implemented in R and is freely available for use and extension under the GPL-3 license. Package source, reference documentation and user manual are available at https://gitlab.com/algoromics/miodin.


2020 ◽  
Author(s):  
Jordi Martorell-Marugán ◽  
Raúl López-Domínguez ◽  
Adrián García-Moreno ◽  
Daniel Toro-Domínguez ◽  
Juan Antonio Villatoro-García ◽  
...  

SummaryAutoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Here, we present ADEx (https://adex.genyo.es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.


2020 ◽  
Author(s):  
Dongdong Lin ◽  
Hima Yalamanchili ◽  
Xinmin Zhang ◽  
Nathan E. Lewis ◽  
Christina S. Alves ◽  
...  

ABSTRACTChinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. High-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an interactive visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multilayer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and interactively. The tool was also designed with the flexibility to allow other omics data input and thereby enabling multi-omics comparison and visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, visualization and management with expectations to promote the broader use of omics in CHO cell research. The open-source tool is freely available at http://www.chomics.org.


Metabolites ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 237 ◽  
Author(s):  
Sara Cardoso ◽  
Telma Afonso ◽  
Marcelo Maraschin ◽  
Miguel Rocha

Metabolomics data analysis is an important task in biomedical research. The available tools do not provide a wide variety of methods and data types, nor ways to store and share data and results generated. Thus, we have developed WebSpecmine to overcome the aforementioned limitations. WebSpecmine is a web-based application designed to perform the analysis of metabolomics data based on spectroscopic and chromatographic techniques (NMR, Infrared, UV-visible, and Raman, and LC/GC-MS) and compound concentrations. Users, even those not possessing programming skills, can access several analysis methods including univariate, unsupervised and supervised multivariate statistical analysis, as well as metabolite identification and pathway analysis, also being able to create accounts to store their data and results, either privately or publicly. The tool’s implementation is based in the R project, including its shiny web-based framework. Webspecmine is freely available, supporting all major browsers. We provide abundant documentation, including tutorials and a user guide with case studies.


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