SMART: Statistical Metabolomics Analysis—An R Tool

Liang, Yu-Jen; Lin, Yuting; Chen, Chia-Wei; Lin, Chien‐Wei; Chao, Kun-Mao; Pan, Wen‐Harn; Yang, Hsin-Chou

doi:10.1021/acs.analchem.6b00603

Cited by 24 publications

(14 citation statements)

References 63 publications

(95 reference statements)

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“…htm) are employed for statistical analysis. Also, some software packages written in R programs like Statistical Metabolomics Analysis-An R Tool, SMART (Liang et al 2016), and specmine (Costa et al 2016) are being used.…”

Section: Discussionmentioning

confidence: 99%

Current analytical technologies and bioinformatic resources for plant metabolomics data

Vinay

Udayamanoharan

Basrur

et al. 2021

Plant Biotechnol Rep

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Plant metabolome as the downstream product in the biological information of flow starting from genomics is highly complex, and dynamically produces a wide range of primary and secondary metabolites, including ionic inorganic compounds, hydrophilic carbohydrates, amino acids, organic compounds, and compounds associated with hydrophobic lipids. The complex metabolites present in biological samples bring challenges to analytical tools for separating and characterization of the metabolites. Analytical tools such as nuclear magnetic resonance (NMR) and mass spectrometry have recently facilitated the separation, characterization, and quantification of diverse chemical structures. The massive amount of data generated from these analytical tools need to be handled using fast and accurate bioinformatics tools and databases. In this review, we focused on plant metabolomics data acquisition using various analytical tools and freely available workflows from raw data to meaningful biological data to help biologists and chemists to move at the same pace as computational biologists.

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Section: Discussionmentioning

confidence: 99%

Current analytical technologies and bioinformatic resources for plant metabolomics data

Vinay

Udayamanoharan

Basrur

et al. 2021

Plant Biotechnol Rep

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“…Commercially available statistical tools are Matlab (https://www.mathworks.com) and SIMCA-P (http://umetrics.com/products/simca), which propose several types of procedures. Several other packages established for metabolic profiling [74][75][76][77] are shown in Table 2. As these statistical tools are applied in numerous fields, they can be used in several statistical software applications, which are not specially programmed for metabolic profiling.…”

Section: Statistical Tools and Biomarker Identificationmentioning

confidence: 99%

Metabolomics: A Way Forward for Crop Improvement

et al. 2019

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Metabolomics is an emerging branch of “omics” and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment–gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.

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“…HayStack [261] is a web platform that uses XCMS to process data and automatically generates total ion current chromatograms and base peak chromatograms as well as offering an easy way of plotting extracted ion chromatograms (EIC) and some basic statistical tools such as PCA scores plot, volcano plots, and dendrograms for group comparisons. Statistical Metabolomics Analysis–An R Tool (SMART) [262] is an R package that combines different tools such as XCMS and CAMERA with a series of common statistical approaches to provide an integrated pipeline for data processing, visualization, and analysis. MZmine 2 [263] is another very popular tool, with over 1000 citations.…”

Section: Future Perspectivesmentioning

confidence: 99%

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

et al. 2017

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The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

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SMART: Statistical Metabolomics Analysis—An R Tool

Cited by 24 publications

References 63 publications

Current analytical technologies and bioinformatic resources for plant metabolomics data

Current analytical technologies and bioinformatic resources for plant metabolomics data

Metabolomics: A Way Forward for Crop Improvement

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

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