Open Access

Downloads

Download data is not yet available.

Abstract

Highly expressed genes [HEG] are genes available in the organism, which carry the preferred codons for the expression system. Identifying HEG helps to find preferred codons and use them in the gene optimization to express target proteins. Currently, HEG-DB is the only database storing HEG data of many strains of microorganisms, but the data is not updated and maintained. Therefore, our research is carried out to predict HEG in the E. coli K-12 MG1655 strain based on reference sets that are the mostly used ribosomal protein coding genes and genes with high transcription levels from microarray data proposed by the research. Next, the results of HEG from the two above reference sets, HEG-RP and HEG-mRNA, were compared. Finally, we analyzed and compared the HEG that the project predicted with HEG from HEG-DB database. The results from RP and 100-mRNA reference sets were completely identical and were better than data from HEG-DB in the number of HEGs, CAI values and the number of genes contributing to important metabolic pathways. The results showed that it was possible to use reference sets from mRNA microarray data instead of ribosomal protein reference sets in HEG prediction.



Author's Affiliation
Article Details

Issue: Vol 5 No 2 (2021)
Page No.: 1068-1077
Published: Apr 30, 2021
Section: Original Research
DOI: https://doi.org/10.32508/stdjns.v5i2.945

 Copyright Info

Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Vo, N., Pham, N., Truong, N., Tran, T., & Nguyen, H. (2021). Study on predicting highy expressed genes for Escherichia coli based on mRNA microarray data. Science & Technology Development Journal: Natural Sciences, 5(2), 1068-1077. https://doi.org/https://doi.org/10.32508/stdjns.v5i2.945

 Cited by



Article level Metrics by Paperbuzz/Impactstory
Article level Metrics by Altmetrics

 Article Statistics
HTML = 262 times
Download PDF   = 233 times
Total   = 233 times