Literature DB >> 23466472

Universal tight correlation of codon bias and pool of RNA codons (codonome): The genome is optimized to allow any distribution of gene expression values in the transcriptome from bacteria to humans.

Allison Piovesan1, Lorenza Vitale, Maria Chiara Pelleri, Pierluigi Strippoli.   

Abstract

Codon bias is the phenomenon in which distinct synonymous codons are used with different frequencies. We define here the "codonome value" as the total number of codons present across all the expressed mRNAs in a given biological condition. We have developed the "CODONOME" software, which calculates the codon bias and, following integration with a gene expression profile, estimates the actual frequency of each codon at the transcriptome level (codonome bias) of a given tissue. Systematic analysis across different human tissues and multiple species shows a surprisingly tight correlation between the codon bias and the codonome bias. An aneuploidy and cancer condition such as that of Down Syndrome-related acute megakaryoblastic leukemia (DS-AMKL), does not appear to alter this relationship. The law of correlation between codon bias and codonome emerges as a property of the distribution and range of the number, sequence and expression level of the genes in a genome.
Copyright © 2013 Elsevier Inc. All rights reserved.

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Year:  2013        PMID: 23466472     DOI: 10.1016/j.ygeno.2013.02.009

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  9 in total

1.  A quantitative transcriptome reference map of the normal human brain.

Authors:  Maria Caracausi; Lorenza Vitale; Maria Chiara Pelleri; Allison Piovesan; Samantha Bruno; Pierluigi Strippoli
Journal:  Neurogenetics       Date:  2014-09-04       Impact factor: 2.660

2.  Strong Selectional Forces Fine-Tune CpG Content in Genes Involved in Neurological Disorders as Revealed by Codon Usage Patterns.

Authors:  Rekha Khandia; Anushri Sharma; Taha Alqahtani; Ali M Alqahtani; Yahya I Asiri; Saud Alqahtani; Ahmed M Alharbi; Mohammad Amjad Kamal
Journal:  Front Neurosci       Date:  2022-06-10       Impact factor: 5.152

3.  Integrated differential transcriptome maps of Acute Megakaryoblastic Leukemia (AMKL) in children with or without Down Syndrome (DS).

Authors:  Maria Chiara Pelleri; Allison Piovesan; Maria Caracausi; Anna Concetta Berardi; Lorenza Vitale; Pierluigi Strippoli
Journal:  BMC Med Genomics       Date:  2014-12-05       Impact factor: 3.063

4.  GeneBase 1.1: a tool to summarize data from NCBI gene datasets and its application to an update of human gene statistics.

Authors:  Allison Piovesan; Maria Caracausi; Francesca Antonaros; Maria Chiara Pelleri; Lorenza Vitale
Journal:  Database (Oxford)       Date:  2016-12-26       Impact factor: 3.451

5.  Systematic identification of human housekeeping genes possibly useful as references in gene expression studies.

Authors:  Maria Caracausi; Allison Piovesan; Francesca Antonaros; Pierluigi Strippoli; Lorenza Vitale; Maria Chiara Pelleri
Journal:  Mol Med Rep       Date:  2017-07-06       Impact factor: 2.952

6.  A molecular view of the normal human thyroid structure and function reconstructed from its reference transcriptome map.

Authors:  Lorenza Vitale; Allison Piovesan; Francesca Antonaros; Pierluigi Strippoli; Maria Chiara Pelleri; Maria Caracausi
Journal:  BMC Genomics       Date:  2017-09-18       Impact factor: 3.969

7.  Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank.

Authors:  Allison Piovesan; Maria Caracausi; Marco Ricci; Pierluigi Strippoli; Lorenza Vitale; Maria Chiara Pelleri
Journal:  DNA Res       Date:  2015-11-17       Impact factor: 4.458

8.  Systematic large-scale meta-analysis identifies a panel of two mRNAs as blood biomarkers for colorectal cancer detection.

Authors:  Maria Teresa Rodia; Giampaolo Ugolini; Gabriella Mattei; Isacco Montroni; Davide Zattoni; Federico Ghignone; Giacomo Veronese; Giorgia Marisi; Mattia Lauriola; Pierluigi Strippoli; Rossella Solmi
Journal:  Oncotarget       Date:  2016-05-24

9.  Integrated Quantitative Transcriptome Maps of Human Trisomy 21 Tissues and Cells.

Authors:  Maria Chiara Pelleri; Chiara Cattani; Lorenza Vitale; Francesca Antonaros; Pierluigi Strippoli; Chiara Locatelli; Guido Cocchi; Allison Piovesan; Maria Caracausi
Journal:  Front Genet       Date:  2018-04-24       Impact factor: 4.599

  9 in total

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