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Matsuoka T, Yashiro M. Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer. Int J Mol Sci 2024; 25:5880. [PMID: 38892067 PMCID: PMC11172243 DOI: 10.3390/ijms25115880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 05/23/2024] [Accepted: 05/25/2024] [Indexed: 06/21/2024] Open
Abstract
Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.
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Affiliation(s)
- Tasuku Matsuoka
- Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan;
- Institute of Medical Genetics, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan
| | - Masakazu Yashiro
- Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan;
- Institute of Medical Genetics, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan
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2
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Frederiksen SD. Prioritizing Suggestive Candidate Genes in Migraine: An Opinion. Front Neurol 2022; 13:910366. [PMID: 35785356 PMCID: PMC9240222 DOI: 10.3389/fneur.2022.910366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/19/2022] [Indexed: 11/13/2022] Open
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3
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Vedi M, Nalabolu HS, Lin CW, Hoffman MJ, Smith JR, Brodie K, De Pons JL, Demos WM, Gibson AC, Hayman GT, Hill ML, Kaldunski ML, Lamers L, Laulederkind SJF, Thorat K, Thota J, Tutaj M, Tutaj MA, Wang SJ, Zacher S, Dwinell MR, Kwitek AE. MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses. Genetics 2022; 220:6516514. [PMID: 35380657 PMCID: PMC8982048 DOI: 10.1093/genetics/iyac005] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/03/2022] [Indexed: 12/12/2022] Open
Abstract
Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.
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Affiliation(s)
- Mahima Vedi
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Harika S Nalabolu
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Chien-Wei Lin
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Matthew J Hoffman
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jennifer R Smith
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Kent Brodie
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jeffrey L De Pons
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Wendy M Demos
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Adam C Gibson
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - G Thomas Hayman
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Morgan L Hill
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Mary L Kaldunski
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Logan Lamers
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | | | - Ketaki Thorat
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jyothi Thota
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Monika Tutaj
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Marek A Tutaj
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Shur-Jen Wang
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Stacy Zacher
- Information Services, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Melinda R Dwinell
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Anne E Kwitek
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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4
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Ghandikota S, Sharma M, Jegga AG. Computational workflow for functional characterization of COVID-19 through secondary data analysis. STAR Protoc 2021; 2:100873. [PMID: 34746856 PMCID: PMC8551262 DOI: 10.1016/j.xpro.2021.100873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021).
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Affiliation(s)
- Sudhir Ghandikota
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA,Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, USA,Corresponding author
| | - Mihika Sharma
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Anil G. Jegga
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA,Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, USA,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA,Corresponding author
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Graw S, Chappell K, Washam CL, Gies A, Bird J, Robeson MS, Byrum SD. Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics 2021; 17:170-185. [PMID: 33347526 PMCID: PMC8058243 DOI: 10.1039/d0mo00041h] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.
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Affiliation(s)
- Stefan Graw
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Kevin Chappell
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Charity L Washam
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA. and Arkansas Children's Research Institute, 13 Children's Way, Little Rock, AR 72202, USA
| | - Allen Gies
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Jordan Bird
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA.
| | - Michael S Robeson
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
| | - Stephanie D Byrum
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, 4301 West Markham Street (slot 516), Little Rock, AR 72205-7199, USA. and Arkansas Children's Research Institute, 13 Children's Way, Little Rock, AR 72202, USA
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Li Y, Ma L, Wu D, Chen G. Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Brief Bioinform 2021; 22:6189773. [PMID: 33778867 DOI: 10.1093/bib/bbab024] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 12/31/2020] [Accepted: 01/20/2021] [Indexed: 12/13/2022] Open
Abstract
Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.
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Affiliation(s)
| | - Lu Ma
- China Normal University, China
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7
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Chen S, Ghandikota S, Gautam Y, Mersha TB. MI-MAAP: marker informativeness for multi-ancestry admixed populations. BMC Bioinformatics 2020; 21:131. [PMID: 32245404 PMCID: PMC7119171 DOI: 10.1186/s12859-020-3462-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 03/20/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Admixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection. RESULTS To address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data. CONCLUSIONS MI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.
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Affiliation(s)
- Siqi Chen
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, 3333 Burnet Avenue, MLC 7037, Cincinnati, OH 45229-3026 USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221 USA
| | - Sudhir Ghandikota
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, 3333 Burnet Avenue, MLC 7037, Cincinnati, OH 45229-3026 USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221 USA
| | - Yadu Gautam
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, 3333 Burnet Avenue, MLC 7037, Cincinnati, OH 45229-3026 USA
| | - Tesfaye B. Mersha
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, 3333 Burnet Avenue, MLC 7037, Cincinnati, OH 45229-3026 USA
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Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights 2020; 14:1177932219899051. [PMID: 32076369 PMCID: PMC7003173 DOI: 10.1177/1177932219899051] [Citation(s) in RCA: 534] [Impact Index Per Article: 133.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 11/09/2019] [Indexed: 12/22/2022] Open
Abstract
To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.
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Affiliation(s)
| | | | | | - Abhay Jere
- Innovation Cell, Ministry of Human Resource Development, New Delhi, India
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Gautam Y, Afanador Y, Abebe T, López JE, Mersha TB. Genome-wide analysis revealed sex-specific gene expression in asthmatics. Hum Mol Genet 2019; 28:2600-2614. [PMID: 31095684 DOI: 10.1093/hmg/ddz074] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 03/04/2019] [Accepted: 04/02/2019] [Indexed: 01/08/2023] Open
Abstract
Global gene-expression analysis has shown remarkable difference between males and females in response to exposure to many diseases. Nevertheless, gene expression studies in asthmatics have so far focused on sex-combined analysis, ignoring inherent variabilities between the sexes, which potentially drive disparities in asthma prevalence. The objectives of this study were to identify (1) sex-specific differentially expressed genes (DEGs), (2) genes that show sex-interaction effects and (3) sex-specific pathways and networks enriched in asthma risk. We analyzed 711 males and 689 females and more than 2.8 million transcripts covering 20 000 genes leveraged from five different tissues and cell types (i.e. epithelial, blood, induced sputum, T cells and lymphoblastoids). Using tissue-specific meta-analysis, we identified 439 male- and 297 female-specific DEGs in all cell types, with 32 genes in common. By linking DEGs to the genome-wide association study (GWAS) catalog and the lung and blood eQTL annotation data from GTEx, we identified four male-specific genes (FBXL7, ITPR3 and RAD51B from epithelial tissue and ALOX15 from blood) and one female-specific gene (HLA-DQA1 from epithelial tissue) that are disregulated during asthma. The hypoxia-inducible factor 1 signaling pathway was enriched only in males, and IL-17 and chemokine signaling pathways were enriched in females. The cytokine-cytokine signaling pathway was enriched in both sexes. The presence of sex-specific genes and pathways demonstrates that sex-combined analysis does not identify genes preferentially expressed in each sex in response to diseases. Linking DEG and molecular eQTLs to GWAS catalog represents an important avenue for identifying biologically and clinically relevant genes.
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Affiliation(s)
- Yadu Gautam
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Yashira Afanador
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Tilahun Abebe
- Department of Biology, University of Northern Iowa, Cedar Falls, IA, USA
| | - Javier E López
- Department of Internal Medicine, University of California Davis, Davis, CA, USA
| | - Tesfaye B Mersha
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
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Misra BB, Mohapatra S. Tools and resources for metabolomics research community: A 2017-2018 update. Electrophoresis 2018; 40:227-246. [PMID: 30443919 DOI: 10.1002/elps.201800428] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 11/09/2018] [Accepted: 11/09/2018] [Indexed: 01/09/2023]
Abstract
The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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