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Metabolomics data analysis
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Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches.
17p
vicwell
29-02-2024
5
2
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Part 2 book "Comprehensive analysis of parasite biology" includes content: Methods to investigate metabolic systems in trypanosoma; the role of metabolomics in antiparasitic drug discovery; the importance of targeting lipid metabolism in parasites for drug discovery; carbon metabolism of plasmodium falciparum; mathematical modeling and omic data integration to understand dynamic adaptation of apicomplexan parasites and identify pharmaceutical targets; ... and other contents.
255p
muasambanhan03
02-01-2024
7
2
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Prostate cancer is a common solid tumor that affects a significant number of men worldwide. Conventional androgen deprivation therapy (ADT) increases the risk of developing castration-resistant prostate cancer (CRPC).
16p
vileonardodavinci
23-12-2023
4
2
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Pathophysiology of transformation of inflammatory lesions in chronic pancreatitis (CP) to pancreatic ductal adenocarcinoma (PDAC) is not clear. Methods: We conducted a systematic review, meta-analysis of circulating metabolites, integrated this data with transcriptome analysis of human pancreatic tissues and validated using immunohistochemistry. Our aim was to establish biomarker signatures for early malignant transformation in patients with underlying CP and identify therapeutic targets.
13p
vikolindagrabar
27-07-2022
9
3
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We present results from a computational analysis developed to integrate transcriptome and metabolomic data in order to explore the heat stress response in the liver of the modern broiler chicken. Heat stress is a significant cause of productivity loss in the poultry industry, both in terms of increased livestock morbidity and its negative influence on average feed efficiency.
16p
visilicon2711
20-08-2021
13
1
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To fully elucidate the roles and mechanisms of plant hormones in leaf senescence, we adopted an integrated analysis of both non-senescing and senescing leaves from red maple with transcriptome and metabolome data.
22p
vijichea2711
28-05-2021
10
1
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MetSizeR: Selecting the optimal sample size for metabolomic studies using an analysis based approach
Determining sample sizes for metabolomic experiments is important but due to the complexity of these experiments, there are currently no standard methods for sample size estimation in metabolomics. Since pilot studies are rarely done in metabolomics, currently existing sample size estimation approaches which rely on pilot data can not be applied.
8p
viwyoming2711
16-12-2020
12
0
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Principal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g., the top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences are made for these metabolites.
9p
vikentucky2711
26-11-2020
12
0
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Mass spectrometry-based metabolomic analysis depends upon the identification of spectral peaks by their mass and retention time. Statistical analysis that follows the identification currently relies on one main peak of each compound.
11p
vikentucky2711
26-11-2020
15
1
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Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites.
11p
vikentucky2711
26-11-2020
22
0
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Metabolomics is a systems approach to the analysis of cellular processes through small-molecule metabolite profiling. Standardisation of sample handling and acquisition approaches has contributed to reproducibility.
7p
vikentucky2711
26-11-2020
9
1
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: Data generated from metabolomics experiments are different from other types of “-omics” data. For example, a common phenomenon in mass spectrometry (MS)-based metabolomics data is that the data matrix frequently contains missing values, which complicates some quantitative analyses.
13p
vikentucky2711
24-11-2020
18
1
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Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise.
14p
vioklahoma2711
19-11-2020
6
1
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In recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes. Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variables, which has a strong impact on crop yield.
10p
vioklahoma2711
19-11-2020
9
2
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High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies.
10p
vioklahoma2711
19-11-2020
14
1
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Advance in high-throughput technologies in genomics, transcriptomics, and metabolomics has created demand for bioinformatics tools to integrate high-dimensional data from different sources. Canonical correlation analysis (CCA) is a statistical tool for finding linear associations between different types of information.
11p
vioklahoma2711
19-11-2020
9
1
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During the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data.
16p
viflorida2711
30-10-2020
17
1
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Bioinformatic tools for the enrichment of ‘omics’ datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools.
11p
viconnecticut2711
29-10-2020
12
1
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Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed.
11p
viconnecticut2711
28-10-2020
15
0
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The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis.
17p
vijisoo2711
27-10-2020
11
0
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