skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: CPA: a web-based platform for consensus pathway analysis and interactive visualization
Abstract In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.  more » « less
Award ID(s):
2001385 2019609
PAR ID:
10244184
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Nucleic Acids Research
ISSN:
0305-1048
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, Current Protocols)
    Abstract Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command‐line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open‐source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology‐based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta‐analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA‐Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA‐Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology‐based (TB) pathway analysis Basic Protocol 7: Data integration and visualization 
    more » « less
  2. ; ; ; ; ; (, Briefings in Bioinformatics)
    Abstract This manuscript describes the development of a resource module that is part of a learning platform named ‘NIGMS Sandbox for Cloud-based Learning’ (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. 
    more » « less
  3. ; (, Scientific Reports)
    Abstract Rapid development of transcriptome sequencing technologies has resulted in a data revolution and emergence of new approaches to study transcriptomic regulation such as alternative splicing, alternative polyadenylation, CRISPR knockout screening in addition to the regular gene expression. A full characterization of the transcriptional landscape of different groups of cells or tissues holds enormous potential for both basic science as well as clinical applications. Although many methods have been developed in the realm of differential gene expression analysis, they all geared towards a particular type of sequencing data and failed to perform well when applied in different types of transcriptomic data. To fill this gap, we offer a negative beta binomial t-test (NBBt-test). NBBt-test provides multiple functions to perform differential analyses of alternative splicing, polyadenylation, CRISPR knockout screening, and gene expression datasets. Both real and large-scale simulation data show superior performance of NBBt-test with higher efficiency, and lower type I error rate and FDR to identify differential isoforms and differentially expressed genes and differential CRISPR knockout screening genes with different sample sizes when compared against the current very popular statistical methods. An R-package implementing NBBt-test is available for downloading from CRAN ( https://CRAN.R-project.org/package=NBBttest ). 
    more » « less
  4. ; ; ; (, mSystems)
    Whiteson, Katrine (Ed.)
    ABSTRACT A comprehensive pangenomic approach was employed to analyze the genomes of 75 type II methylotrophs spanning various genera. Our investigation revealed 256 exact core gene families shared by all 75 organisms, emphasizing their crucial role in the survival and adaptability of these organisms. Additionally, we predicted the functionality of 12 hypothetical proteins. The analysis unveiled a diverse array of genes associated with key metabolic pathways, including methane, serine, glyoxylate, and ethylmalonyl-CoA (EMC) metabolic pathways. While all selected organisms possessed essential genes for the serine pathway,Methylooceanibacter marginalislacked serine hydroxymethyltransferase (SHMT), andMethylobacterium variabileexhibited both isozymes of SHMT, suggesting its potential to utilize a broader range of carbon sources. Notably,Methylobrevissp. displayed a unique serine-glyoxylate transaminase isozyme not found in other organisms. Only nine organisms featured anaplerotic enzymes (isocitrate lyase and malate synthase) for the glyoxylate pathway, with the rest following the EMC pathway.Methylovirgulasp. 4MZ18 stood out by acquiring genes from both glyoxylate and EMC pathways, andMethylocapsasp. S129 featured an A-form malate synthase, unlike the G-form found in the remaining organisms. Our findings also revealed distinct phylogenetic relationships and clustering patterns among type II methylotrophs, leading to the proposal of a separate genus forMethylovirgulasp. 4M-Z18 andMethylocapsasp. S129. This pangenomic study unveils remarkable metabolic diversity, unique gene characteristics, and distinct clustering patterns of type II methylotrophs, providing valuable insights for future carbon sequestration and biotechnological applications. IMPORTANCEMethylotrophs have played a significant role in methane-based product production for many years. However, a comprehensive investigation into the diverse genetic architectures across different genera of methylotrophs has been lacking. This study fills this knowledge gap by enhancing our understanding of core hypothetical proteins and unique enzymes involved in methane oxidation, serine, glyoxylate, and ethylmalonyl-CoA pathways. These findings provide a valuable reference for researchers working with other methylotrophic species. Furthermore, this study not only unveils distinctive gene characteristics and phylogenetic relationships but also suggests a reclassification forMethylovirgulasp. 4M-Z18 andMethylocapsasp. S129 into separate genera due to their unique attributes within their respective genus. Leveraging the synergies among various methylotrophic organisms, the scientific community can potentially optimize metabolite production, increasing the yield of desired end products and overall productivity. 
    more » « less
  5. ; ; ; ; (, Angewandte Chemie International Edition)
    Abstract This work demonstrates the dominance of a Ni(0/II/III) cycle for Ni‐photoredox amide arylation, which contrasts with other Ni‐photoredox C‐heteroatom couplings that operate via Ni(I/III) self‐sustained cycles. The kinetic data gathered when using different Ni precatalysts supports an initial Ni(0)‐mediated oxidative addition into the aryl bromide. Using NiCl2as the precatalyst resulted in an observable induction period, which was found to arise from a photochemical activation event to generate Ni(0) and to be prolonged by unproductive comproportionation between the Ni(II) precatalyst and the in situ generated Ni(0) active species. Ligand exchange after oxidative addition yields a Ni(II) aryl amido complex, which was identified as the catalyst resting state for the reaction. Stoichiometric experiments showed that oxidation of this Ni(II) aryl amido intermediate was required to yield functionalized amide products. The kinetic data presented supports a rate‐limiting photochemically‐mediated Ni(II/III) oxidation to enable C−N reductive elimination. An alternative Ni(I/III) self‐sustained manifold was discarded based on EPR and kinetic measurements. The mechanistic insights uncovered herein will inform the community on how subtle changes in Ni‐photoredox reaction conditions may impact the reaction pathway, and have enabled us to include aryl chlorides as coupling partners and to reduce the Ni loading by 20‐fold without any reactivity loss. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email