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

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


2020 ◽  
Vol 8 (2) ◽  
pp. 130-142
Author(s):  
Henry Linder ◽  
Yuping Zhang

BMC Genomics ◽  
2019 ◽  
Vol 20 (S11) ◽  
Author(s):  
Shuai Zeng ◽  
Zhen Lyu ◽  
Siva Ratna Kumari Narisetti ◽  
Dong Xu ◽  
Trupti Joshi

Abstract Background Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms’ genomics and integrative omics data. KBCommons is designed and developed to integrate diverse multi-level omics data and to support biological discoveries for all species via a common platform. Methods KBCommons has four modules including data storage, data processing, data accessing, and web interface for data management and retrieval. It provides a comprehensive framework for new plant-specific, animal-specific, virus-specific, bacteria-specific or human disease-specific knowledge base (KB) creation, for adding new genome versions and additional multi-omics data to existing KBs, and for exploring existing datasets within current KBs. Results KBCommons has an array of tools for data visualization and data analytics such as multiple gene/metabolite search, gene family/Pfam/Panther function annotation search, miRNA/metabolite/trait/SNP search, differential gene expression analysis, and bulk data download capacity. It contains a highly reliable data privilege management system to make users’ data publicly available easily and to share private or pre-publication data with members in their collaborative groups safely and securely. It allows users to conduct data analysis using our in-house developed workflow functionalities that are linked to XSEDE high performance computing resources. Using KBCommons’ intuitive web interface, users can easily retrieve genomic data, multi-omics data and analysis results from workflow according to their requirements and interests. Conclusions KBCommons addresses the needs of many diverse research communities to have a comprehensive multi-level OMICS web resource for data retrieval, sharing, analysis and visualization. KBCommons can be publicly accessed through a dedicated link for all organisms at http://kbcommons.org/.


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.


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.


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