Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities

Li, Heng; Shi, Minjun; Ren, Keyi; Zhang, Lei; Ye, Weidong; Zhang, Wanting; Cheng, Yingyin; Xia, Xiao-Qin

doi:10.1093/bioinformatics/btac777

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…An explorative multivariate (Principal Component Analysis, PCA) analysis was applied to ASDs vs. CTRLs, whilst a partial least square-discriminant analysis (PLS-DA) ( Bylesjö et al, 2007 ) was exploited ( Li et al, 2023 ) by mixOmics R package to identify differential VOCs profiles. Results were verified by univariate analysis with Mann–Whitney test and p values adjusted by FDR ( Benjamini and Hochberg, 1995 ).…”

Section: Methodsmentioning

confidence: 99%

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

Vernocchi,

Marangelo,

Guerrera

et al. 2023

Front. Microbiol.

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BackgroundAutism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder. Major interplays between the gastrointestinal (GI) tract and the central nervous system (CNS) seem to be driven by gut microbiota (GM). Herein, we provide a GM functional characterization, based on GM metabolomics, mapping of bacterial biochemical pathways, and anamnestic, clinical, and nutritional patient metadata.MethodsFecal samples collected from children with ASD and neurotypical children were analyzed by gas-chromatography mass spectrometry coupled with solid phase microextraction (GC–MS/SPME) to determine volatile organic compounds (VOCs) associated with the metataxonomic approach by 16S rRNA gene sequencing. Multivariate and univariate statistical analyses assessed differential VOC profiles and relationships with ASD anamnestic and clinical features for biomarker discovery. Multiple web-based and machine learning (ML) models identified metabolic predictors of disease and network analyses correlated GM ecological and metabolic patterns.ResultsThe GM core volatilome for all ASD patients was characterized by a high concentration of 1-pentanol, 1-butanol, phenyl ethyl alcohol; benzeneacetaldehyde, octadecanal, tetradecanal; methyl isobutyl ketone, 2-hexanone, acetone; acetic, propanoic, 3-methyl-butanoic and 2-methyl-propanoic acids; indole and skatole; and o-cymene. Patients were stratified based on age, GI symptoms, and ASD severity symptoms. Disease risk prediction allowed us to associate butanoic acid with subjects older than 5 years, indole with the absence of GI symptoms and low disease severity, propanoic acid with the ASD risk group, and p-cymene with ASD symptoms, all based on the predictive CBCL-EXT scale. The HistGradientBoostingClassifier model classified ASD patients vs. CTRLs by an accuracy of 89%, based on methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, ethanol, butanoic acid, octadecane, acetic acid, skatole, and tetradecanal features. LogisticRegression models corroborated methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, skatole, and acetic acid as ASD predictors.ConclusionOur results will aid the development of advanced clinical decision support systems (CDSSs), assisted by ML models, for advanced ASD-personalized medicine, based on omics data integrated into electronic health/medical records. Furthermore, new ASD screening strategies based on GM-related predictors could be used to improve ASD risk assessment by uncovering novel ASD onset and risk predictors.

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Section: Methodsmentioning

confidence: 99%

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

Vernocchi,

Marangelo,

Guerrera

et al. 2023

Front. Microbiol.

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“…The microbial diversity in samples was estimated using the alpha diversity that included the Chao1 index and Shannon index. The Bray-Curtis distance matrix performed by R package [27] (Version 3.5.1) was used for Bray-Curtis principal coordinate analysis (PCoA) to estimate the beta diversity. Then, the R package was used to analyze the significant differences between different groups using ANOVA statistical test.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

The Combination of Biochar and Bacillus subtilis Biological Agent Reduced the Relative Abundance of Pathogenic Bacteria in the Rhizosphere Soil of Panax notoginseng

Zhou,

Liu,

et al. 2024

Microorganisms

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In the continuous cropping of Panax notoginseng, the pathogenic fungi in the rhizosphere soil increased and infected the roots of Panax notoginseng, resulting in a decrease in yield. This is an urgent problem that needs to be solved in order to effectively overcome the obstacles associated with the continuous cropping of Panax notoginseng. Previous studies have shown that Bacillus subtilis inhibits pathogenic fungi in the rhizosphere of Panax notoginseng, but the inhibitory effect was not stable. Therefore, we hope to introduce biochar to help Bacillus subtilis colonize in soil. In the experiment, fields planted with Panax notoginseng for 5 years were renovated, and biochar was mixed in at the same time. The applied amount of biochar was set to four levels (B0, 10 kg·hm−2; B1, 80 kg·hm−2; B2, 110 kg·hm−2; B3, 140 kg·hm−2), and Bacillus subtilis biological agent was set to three levels (C1, 10 kg·hm−2; C2, 15 kg·hm−2; C3, 25 kg·hm−2). The full combination experiment and a blank control group (CK) were used. The experimental results show that the overall Ascomycota decreased by 0.86%~65.68% at the phylum level. Basidiomycota increased by −73.81%~138.47%, and Mortierellomycota increased by −51.27%~403.20%. At the genus level, Mortierella increased by −10.29%~855.44%, Fusarium decreased by 35.02%~86.79%, and Ilyonectria increased by −93.60%~680.62%. Fusarium mainly causes acute bacterial wilt root rot, while Ilyonectria mainly causes yellow rot. Under different treatments, the Shannon index increased by −6.77%~62.18%, the Chao1 index increased by −12.07%~95.77%, the Simpson index increased by −7.31%~14.98%, and the ACE index increased by −11.75%~96.12%. The good_coverage indices were all above 0.99. The results of a random forest analysis indicated that Ilyonectria, Pyrenochaeta, and Xenopolyscytalum were the top three most important species in the soil, with MeanDecreaseGini values of 2.70, 2.50, and 2.45, respectively. Fusarium, the primary pathogen of Panax notoginseng, ranked fifth, and its MeanDecreaseGini value was 2.28. The experimental results showed that the B2C2 treatment had the best inhibitory effect on Fusarium, and the relative abundance of Fusarium in Panax notoginseng rhizosphere soil decreased by 86.79% under B2C2 treatment; the B1C2 treatment had the best inhibitory effect on Ilyonectria, and the relative abundance of Ilyonectria in the Panax notoginseng rhizosphere soil decreased by 93.60% under B1C2 treatment. Therefore, if we want to improve the soil with acute Ralstonia solanacearum root rot, we should use the B2C2 treatment to improve the soil environment; if we want to improve the soil with yellow rot disease, we should use the B1C2 treatment to improve the soil environment.

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“…Additionally, some studies have aimed to provide convenience in water quality research by effectively integrating the input, analysis, and output within web-based platforms [19], as well as offering online spaces for the real-time investigation and analysis of monitoring tools [20]. Certain studies have also opted for a web-based approach for visualizing and analyzing omics data [21][22][23][24]. In addition, due to COVID-19, there is active research and utilization of big data in the field of bioresearch [25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Data Lake Conceptualized Web Platform for Food Research Data Collection

An,

Oh,

Kim

et al. 2024

JWE

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Food research is uniquely intertwined with everyday life and necessitates the utilization of big data. Within this domain, the research data consist of various forms and formats, encompassing biological experiment results, chemical analysis data, nutritional information, microbiological data, sensor data, images, and videos. This diversity stems from the integration of data from various subdomains within the larger field. With recent advancements in deep learning technology, the importance of data has grown significantly, resulting in increased reliance on data-driven research. Although specialized platforms for sharing and utilizing data have been established at the national level, particularly in the bioscience field, food research lacks a dedicated infrastructure and specialized data-sharing platforms. In this study, we develop a platform that leverages Hadoop-based distributed file systems to create a data lake. This platform enables data storage and sharing through a web-based interface. The distributed file system supports scalability by adding data nodes, making it an effective solution for capacity expansion. In addition, the web-based platform ensures high accessibility, allowing users access from anywhere, at any time, using any device. Finally, we introduce the establishment of a 1.8 PB Hadoop-based physical storage system and present an approach for building a highly accessible web platform with substantial utility.

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Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities

Cited by 8 publications

References 13 publications

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

The Combination of Biochar and Bacillus subtilis Biological Agent Reduced the Relative Abundance of Pathogenic Bacteria in the Rhizosphere Soil of Panax notoginseng

Data Lake Conceptualized Web Platform for Food Research Data Collection

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