Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity

<p>Abstract</p> <p>Background</p> <p>There are a number of methods (also called: measures) currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can introduce strong methodological bia...

Full description

Bibliographic Details
Main Authors: Vlahoviček Kristian, Supek Fran
Format: Article
Language:English
Published: BMC 2005-07-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/6/182
id doaj-6d0a861aa4d14200b4df655ff1fc5174
record_format Article
spelling doaj-6d0a861aa4d14200b4df655ff1fc51742020-11-25T02:19:06ZengBMCBMC Bioinformatics1471-21052005-07-016118210.1186/1471-2105-6-182Comparison of codon usage measures and their applicability in prediction of microbial gene expressivityVlahoviček KristianSupek Fran<p>Abstract</p> <p>Background</p> <p>There are a number of methods (also called: measures) currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can introduce strong methodological bias into measurements; therefore we attempted to develop a method free from such dependencies. One of the common applications of codon usage analyses is to quantitatively predict gene expressivity.</p> <p>Results</p> <p>We compared the performance of several commonly used measures and a novel method we introduce in this paper – Measure Independent of Length and Composition (MILC). Large, randomly generated sequence sets were used to test for dependence on (i) sequence length, (ii) overall amount of codon bias and (iii) codon bias discrepancy in the sequences. A derivative of the method, named MELP (MILC-based Expression Level Predictor) can be used to quantitatively predict gene expression levels from genomic data. It was compared to other similar predictors by examining their correlation with actual, experimentally obtained mRNA or protein abundances.</p> <p>Conclusion</p> <p>We have established that MILC is a generally applicable measure, being resistant to changes in gene length and overall nucleotide composition, and introducing little noise into measurements. Other methods, however, may also be appropriate in certain applications. Our efforts to quantitatively predict gene expression levels in several prokaryotes and unicellular eukaryotes met with varying levels of success, depending on the experimental dataset and predictor used. Out of all methods, MELP and Rainer Merkl's GCB method had the most consistent behaviour. A 'reference set' containing known ribosomal protein genes appears to be a valid starting point for a codon usage-based expressivity prediction.</p> http://www.biomedcentral.com/1471-2105/6/182
collection DOAJ
language English
format Article
sources DOAJ
author Vlahoviček Kristian
Supek Fran
spellingShingle Vlahoviček Kristian
Supek Fran
Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity
BMC Bioinformatics
author_facet Vlahoviček Kristian
Supek Fran
author_sort Vlahoviček Kristian
title Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity
title_short Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity
title_full Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity
title_fullStr Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity
title_full_unstemmed Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity
title_sort comparison of codon usage measures and their applicability in prediction of microbial gene expressivity
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2005-07-01
description <p>Abstract</p> <p>Background</p> <p>There are a number of methods (also called: measures) currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can introduce strong methodological bias into measurements; therefore we attempted to develop a method free from such dependencies. One of the common applications of codon usage analyses is to quantitatively predict gene expressivity.</p> <p>Results</p> <p>We compared the performance of several commonly used measures and a novel method we introduce in this paper – Measure Independent of Length and Composition (MILC). Large, randomly generated sequence sets were used to test for dependence on (i) sequence length, (ii) overall amount of codon bias and (iii) codon bias discrepancy in the sequences. A derivative of the method, named MELP (MILC-based Expression Level Predictor) can be used to quantitatively predict gene expression levels from genomic data. It was compared to other similar predictors by examining their correlation with actual, experimentally obtained mRNA or protein abundances.</p> <p>Conclusion</p> <p>We have established that MILC is a generally applicable measure, being resistant to changes in gene length and overall nucleotide composition, and introducing little noise into measurements. Other methods, however, may also be appropriate in certain applications. Our efforts to quantitatively predict gene expression levels in several prokaryotes and unicellular eukaryotes met with varying levels of success, depending on the experimental dataset and predictor used. Out of all methods, MELP and Rainer Merkl's GCB method had the most consistent behaviour. A 'reference set' containing known ribosomal protein genes appears to be a valid starting point for a codon usage-based expressivity prediction.</p>
url http://www.biomedcentral.com/1471-2105/6/182
work_keys_str_mv AT vlahovicekkristian comparisonofcodonusagemeasuresandtheirapplicabilityinpredictionofmicrobialgeneexpressivity
AT supekfran comparisonofcodonusagemeasuresandtheirapplicabilityinpredictionofmicrobialgeneexpressivity
_version_ 1724878468346281984