Graphical Interpretation and Analysis of Proteins and their Ontologies (GiaPronto): A One-Click Graph Visualization Software for Proteomics Data Sets

Weiner, Amber K.; Diskin, Sharon J.; García, Benjamin A.

doi:10.1074/mcp.tir117.000438

Cited by 14 publications

(11 citation statements)

References 9 publications

(1 reference statement)

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“…The results of all samples were combined to a total label-free intensity analysis for each sample to get LFQ values (see details in Supplementary Table S1 ). Further normalization by subtracting the average of the raw LFQ values and log2 transformation was performed by using the GiaPronto software [ 69 ]. Proteins present in all samples were further analyzed.…”

Section: Methodsmentioning

confidence: 99%

“…To identify differentially expressed proteins, normalized LFQ values from the two treatments based on their log2 ration and associated p -values were plotted in a Volcano plot using GiaPronto and a limit for fold change as Log2ratio > 0.58, and p (−log) >5. Proteins that were both abundant and upregulated in one of the treatments were assigned after multiplying the log2 ratio with the abundance as biomarkers [ 69 ].…”

Section: Methodsmentioning

confidence: 99%

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Early Changes in Crayfish Hemocyte Proteins after Injection with a β-1,3-glucan, Compared to Saline Injected and Naive Animals

Ekblom

Söderhäll

2021

IJMS

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Early changes in hemocyte proteins in freshwater crayfish Pacifastacus leniusculus, in response to an injection with the fungal pattern recognition protein β-1,3-glucan (laminarin) were investigated, as well as changes after saline (vehicle) injection and in naïve animals. Injection of saline resulted in rapid recruitment of granular hemocytes from surrounding tissues, whereas laminarin injection on the other hand induced an initial dramatic drop of hemocytes. At six hours after injection, the hemocyte populations therefore were of different composition. The results show that mature granular hemocytes increase in number after saline injection as indicated by the high abundance of proteins present in granular cell vesicles, such as a vitelline membrane outer layer protein 1 homolog, mannose-binding lectin, masquerade, crustin 1 and serine protease homolog 1. After injection with the β-1,3-glucan, only three proteins were enhanced in expression, in comparison with saline-injected animals and uninjected controls. All of them may be associated with immune responses, such as a new and previously undescribed Kazal proteinase inhibitor. One interesting observation was that the clotting protein was increased dramatically in most of the animals injected with laminarin. The number of significantly affected proteins was very few after a laminarin injection when compared to uninjected and saline-injected crayfish. This finding may demonstrate some problematic issues with gene and protein expression studies from other crustaceans receiving injections with pathogens or pattern recognition proteins. If no uninjected controls are included and no information about hemocyte count (total or differential) is given, expressions data for proteins or mRNAs are very difficult to properly interpret.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Early Changes in Crayfish Hemocyte Proteins after Injection with a β-1,3-glucan, Compared to Saline Injected and Naive Animals

Ekblom

Söderhäll

2021

IJMS

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“…The MixProTool is compatible with identification and quantification outputs from other programs, such as MaxQuant and MASCOT, where results may be visualized as vector graphs and tables for further analysis, in contrast to existing softwares, such as GiaPronto. 255 According to the authors, the web tool can be conveniently operated, even by users without bioinformatics expertise, and it is beneficial for mining the most relevant features among different samples. 24…”

Section: Bioinformatic Tools and Databases For Plant Ampsmentioning

confidence: 99%

Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era

Santos-Silva

Zupin

Oliveira-Lima

et al. 2020

Bioinform Biol Insights

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Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated.

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“…Several research teams and laboratories dedicated in proteomics and transcriptomic data analysis, have all the appropriate equipment and tools to perform such analyses, while eminent tools such as Perseus [29] , GiaPronto [30] , LFQ-Analyst [31] , Cytoscape StringApp [31] , NetGestalt [32] and many other, can successfully perform individual analyses like some of the ones performed by ProTExA [2] . However, offering web-based services for omics data analysis, is an important forward technological step that aims to eliminate the expertise and the local resources required at a single computer level, as well as package compatibility issues, especially for stringent installations.…”

Section: Novelty and Applicationsmentioning

confidence: 99%

ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks and functional analytics

Minadakis

Sokratous

2020

Computational and Structural Biotechnology Journal

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ProTExA is a web-tool that provides a post-processing workflow for the analysis of protein and gene expression datasets. Using network-based bioinformatics approaches, ProTExA facilitates differential expression analysis and co-expression network analysis as well as pathway and post-pathway analysis. Specifically, for a given set of protein-gene expression data across samples, ProTExA: (1) performs statistical analysis and filtering to highlight the differentially expressed proteins-genes, (2) performs enrichment analysis to identify top-scored pathways, (3) generates pathway-to-pathway and pathway-to-gene networks (4) generates protein and gene co-expression networks using a variety of methodologies, and (5) applies clustering methodologies to identify sub-networks of co-expressed proteins-genes. The proposed web-tool is a simple yet informative tool, towards understanding and exploitation of protein and gene expression datasets, especially for those that do not have the expertise and local resources to replicate specific analyses in the context of collaborative and scientific data exchanging.

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Graphical Interpretation and Analysis of Proteins and their Ontologies (GiaPronto): A One-Click Graph Visualization Software for Proteomics Data Sets

Cited by 14 publications

References 9 publications

Early Changes in Crayfish Hemocyte Proteins after Injection with a β-1,3-glucan, Compared to Saline Injected and Naive Animals

Early Changes in Crayfish Hemocyte Proteins after Injection with a β-1,3-glucan, Compared to Saline Injected and Naive Animals

Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era

ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks and functional analytics

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