Literature DB >> 22997859

Platforms for biomarker analysis using high-throughput approaches in genomics, transcriptomics, proteomics, metabolomics, and bioinformatics.

B Alex Merrick1, Robert E London, Pierre R Bushel, Sherry F Grissom, Richard S Paules.   

Abstract

Global biological responses that reflect disease or exposure biology are kinetic and highly dynamic phenomena. While high-throughput DNA sequencing continues to drive genomics, the possibility of more broadly measuring changes in gene expression has been a recent development manifested by a diversity of technical platforms. Such technologies measure transcripts, proteins and small biological molecules, or metabolites, and respectively define the fields of transcriptomics, proteomics and metabolomics that can be performed at a cell-, tissue-, or organism-wide basis. Bioinformatics is the discipline that derives knowledge from the large quantity and diversity of biological, genetic, genomic and gene expression data by integrating computer science, mathematics, statistics and graphic arts. Gene, protein and metabolite expression profiles can be thought of as snapshots of the current, poorly-mapped molecular landscape. The ultimate aim of genomic platforms is to fully map this landscape to more completely describe all of the biological interactions within a living system, during disease and toxicity, and define the behaviour and relationships of all the components of a biological system. The development of databases and knowledge bases will support the integration of data from multiple domains, as well as computational modelling. This chapter will describe the technical platform methods involving DNA sequencing, mass spectrometry, nuclear magnetic resonance combined with separation systems, and bioinformatics to derive genomic and gene expression data and include the relevant bioinformatic tools for analysis. These genomic, or omics platforms should have wide application to epidemiological studies.

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Year:  2011        PMID: 22997859

Source DB:  PubMed          Journal:  IARC Sci Publ        ISSN: 0300-5038


  11 in total

1.  Serum Metabolomic Profiles in Neonatal Mice following Oral Brominated Flame Retardant Exposures to Hexabromocyclododecane (HBCD) Alpha, Gamma, and Commercial Mixture.

Authors:  David T Szabo; Wimal Pathmasiri; Susan Sumner; Linda S Birnbaum
Journal:  Environ Health Perspect       Date:  2016-11-04       Impact factor: 9.031

2.  OmicsON - Integration of omics data with molecular networks and statistical procedures.

Authors:  Cezary Turek; Sonia Wróbel; Monika Piwowar
Journal:  PLoS One       Date:  2020-07-29       Impact factor: 3.240

3.  Overexpression of BUB1B, CCNA2, CDC20, and CDK1 in tumor tissues predicts poor survival in pancreatic ductal adenocarcinoma.

Authors:  Shu Dong; Fei Huang; Hao Zhang; Qiwen Chen
Journal:  Biosci Rep       Date:  2019-02-26       Impact factor: 3.840

4.  CAncer bioMarker Prediction Pipeline (CAMPP)-A standardized framework for the analysis of quantitative biological data.

Authors:  Thilde Terkelsen; Anders Krogh; Elena Papaleo
Journal:  PLoS Comput Biol       Date:  2020-03-16       Impact factor: 4.475

5.  Identification of Hub Genes as Potential Prognostic Biomarkers in Cervical Cancer Using Comprehensive Bioinformatics Analysis and Validation Studies.

Authors:  Han Xue; Zhaojun Sun; Weiqing Wu; Dong Du; Shuping Liao
Journal:  Cancer Manag Res       Date:  2021-01-08       Impact factor: 3.989

6.  The Systematic Analysis of Exercise Mechanism in Human Diseases.

Authors:  Lei Pu; Peng Sun
Journal:  Genet Res (Camb)       Date:  2022-03-24       Impact factor: 1.588

Review 7.  A Precision Medicine Agenda in Traumatic Brain Injury.

Authors:  Jovany Cruz Navarro; Lucido L Ponce Mejia; Claudia Robertson
Journal:  Front Pharmacol       Date:  2022-03-16       Impact factor: 5.810

8.  Hair metabolomics: identification of fetal compromise provides proof of concept for biomarker discovery.

Authors:  Karolina Sulek; Ting-Li Han; Silas Granato Villas-Boas; David Scott Wishart; Shu-E Soh; Kenneth Kwek; Peter David Gluckman; Yap-Seng Chong; Louise Claire Kenny; Philip Newton Baker
Journal:  Theranostics       Date:  2014-07-20       Impact factor: 11.556

Review 9.  The Discovery of Novel Genomic, Transcriptomic, and Proteomic Biomarkers in Cardiovascular and Peripheral Vascular Disease: The State of the Art.

Authors:  Stefano de Franciscis; Laurent Metzinger; Raffaele Serra
Journal:  Biomed Res Int       Date:  2016-05-19       Impact factor: 3.411

10.  Upregulation of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in Tumor Tissues Predicted Worse Overall Survival and Disease-Free Survival in Hepatocellular Carcinoma Patients.

Authors:  Liping Zhuang; Zongguo Yang; Zhiqiang Meng
Journal:  Biomed Res Int       Date:  2018-09-30       Impact factor: 3.411

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