Robust volcano plot: identification of differential metabolites in the presence of outliers

Kumar, Nishith; Hoque, Md. Rakibul; Sugimoto, Masahiro

doi:10.1186/s12859-018-2117-2

Cited by 37 publications

(18 citation statements)

References 42 publications

(28 reference statements)

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“…Because the same or similar motifs could be found in multiple cells, we clustered these motifs into 3226 unique ones using motif similarity measurement based on Jensen-Shannon divergence (see Materials and Methods). To control false discovery rate (FDR), we further conducted a robust volcano test (49) with a stringent requirement ( P -value < 10 –10 and enrichment > 2), resulting in 313 methylation motifs for the follow-up analysis (Figure 1A, Supplementary Figure S1B), including 221 unmethylation motifs (UM) and 92 methylation motifs (MM). Among them, 36 (16.2%) and 14 (17.1%) are matched to 50 known motifs in the latest version of HOCOMOCO (17).…”

Section: Resultsmentioning

confidence: 99%

Identification of DNA motifs that regulate DNA methylation

Wang

Zhang

Ngo

et al. 2019

Nucleic Acids Research

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DNA methylation is an important epigenetic mark but how its locus-specificity is decided in relation to DNA sequence is not fully understood. Here, we have analyzed 34 diverse whole-genome bisulfite sequencing datasets in human and identified 313 motifs, including 92 and 221 associated with methylation (methylation motifs, MMs) and unmethylation (unmethylation motifs, UMs), respectively. The functionality of these motifs is supported by multiple lines of evidence. First, the methylation levels at the MM and UM motifs are respectively higher and lower than the genomic background. Second, these motifs are enriched at the binding sites of methylation modifying enzymes including DNMT3A and TET1, indicating their possible roles of recruiting these enzymes. Third, these motifs significantly overlap with “somatic QTLs” (quantitative trait loci) of methylation and expression. Fourth, disruption of these motifs by mutation is associated with significantly altered methylation level of the CpGs in the neighbor regions. Furthermore, these motifs together with somatic mutations are predictive of cancer subtypes and patient survival. We revealed some of these motifs were also associated with histone modifications, suggesting a possible interplay between the two types of epigenetic modifications. We also found some motifs form feed forward loops to contribute to DNA methylation dynamics.

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Section: Resultsmentioning

confidence: 99%

Identification of DNA motifs that regulate DNA methylation

Wang

Zhang

Ngo

et al. 2019

Nucleic Acids Research

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“…In this case due to the relatively small volume of samples and niche treatment types which have already had the peaks and correlated features screened, a p -value of <0.10 was applied to ensure that a good representation of each sample was created. Secondly only compounds with a minimum fold value ≥2 were considered ( Kumar et al, 2018 ). This means that, for a compound to be included in the metabolite pattern, its signal must have occurred with an intensity at least 2 times greater than that observed at the same mass-to-charge ratio in the reference sample.…”

Section: Resultsmentioning

confidence: 99%

A LCMS Metabolomic Workflow to Investigate Metabolic Patterns in Human Intestinal Cells Exposed to Hydrolyzed Crab Waste Materials

Fearghail

Behan

Engström

et al. 2021

Front. Bioeng. Biotechnol.

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We have developed a LCMS metabolomic workflow to investigate metabolic patterns from human intestinal cells treated with simulated gastrointestinal-digested hydrolyzed crab waste materials. This workflow facilitates smart and reproducible comparisons of cell cultures exposed to different treatments. In this case the variable was the hydrolysis methods, also accounting for the GI digestion giving an output of direct correlation between cellular metabolic patterns caused by the treatments. In addition, we used the output from this workflow to select treatments for further evaluation of the Caco-2 cell response in terms of tentative anti-inflammatory activity in the hopes to find value in the crab waste materials to be used for food products. As hypothesized, the treatment identified to change the cellular metabolomic pattern most readily, was also found to cause the greatest effect in the cells, although the response was pro-inflammatory rather than anti-inflammatory, it proves that changes in cellular metabolic patterns are useful predictors of bioactivity. We conclude that the developed workflow allows for cost effective, rapid sample preparation as well as accurate and repeatable LCMS analysis and introduces a data pipeline specifically for probe the novel metabolite patterns created as a means to assess the performing treatments.

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“…A two-tailed t-test was performed to examine DEGs by log2 (Fold Change) >1 or <−1 and adjusted P value < 0.05. Genes satisfying these conditions were grouped separately as DEGs by volcano plot in R [19].…”

Section: Identi Cation Of Degsmentioning

confidence: 99%

Integrated Transcriptome-wide Profiling and Protein Structure Analysis of Pathogenic Genes in Venous Thromboembolism

Ding

Zhang²,

Cao³

2021

Preprint

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BackgroundGenetic factors are considered to determine the balance of the coagulation and anticoagulation processes, yet the genetic variants related to venous thromboembolism (VTE) remain unclear. This study aimed to investigate the potential molecular mechanisms and pathogenic mutations associated with VTE by determining VTE-related differentially expressed genes (DEGs) by transcriptome-wide profiling and assaying protein structure in VTE.MethodsTwo gene expression datasets, GSE48000 and GSE19151, were accessed from the Gene Expression Omnibus (GEO) database to obtain gene expression data associated with VTE. We identified the DEGs between VTE patients and healthy people using R and performed functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, whole-exome sequencing (WES) was performed for 25 VTE patients and 17 normal cases, and the structural locations of pathogenic missense mutations were identified using pyMOL. Finally, DGIdb database was used to select candidate drugs for the treatment of VTE.ResultsA total of 232 DEGs were identified from the GEO database. The significant function of these DEGs was mostly involved in RNA catabolic process and ribosome pathway. Notably, the results of WES for DEGs and protein structure analysis showed that Histamine N-Methyltransferase (HNMT) (chr2: 138759649 C>T, rs11558538) may be a main predisposing factor for VTE. In addition, Amodiaquine, Harmaline, Aspirin, Metoprine, Dabigatran, and Diphenhydramine were screened for VTE therapy.ConclusionThe results showed that HNMT (chr2: 138759649 C>T, rs11558538) may be potential target for the diagnosis and treatment of VTE.

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Robust volcano plot: identification of differential metabolites in the presence of outliers

Cited by 37 publications

References 42 publications

Identification of DNA motifs that regulate DNA methylation

Identification of DNA motifs that regulate DNA methylation

A LCMS Metabolomic Workflow to Investigate Metabolic Patterns in Human Intestinal Cells Exposed to Hydrolyzed Crab Waste Materials

Integrated Transcriptome-wide Profiling and Protein Structure Analysis of Pathogenic Genes in Venous Thromboembolism

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