Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations

Mohammad Hadi Sekhavati; Mahdi Elahi Torshizi; Mahyar Heydarpour; Adham Fani Maleki

doi:doi:10.11648/j.abb.20140205.11

| Peer-Reviewed

Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations

Mohammad Hadi Sekhavati, Mahdi Elahi Torshizi, Mahyar Heydarpour, Adham Fani Maleki

Published in Advances in Bioscience and Bioengineering (Volume 2, Issue 5)

Received: 8 December 2014 Accepted: 23 December 2014 Published: 4 January 2015

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Quantitative competitive polymerase chain reaction (QC-PCR) technique is playing an important role in nucleic acid quantification. This paper describes a new statistical approach for data analyzing in relative quantitative competitive PCR assays. In order to test the accuracy of this statistical model for quantifying anaerobic rumen fungi, samples of rumen fluid were collected from six fistulated Holstein steers which were fed in two different diets groups (soybean meal diet and canola meal diet). Competitor intensity signal (CIS) and efficiency of PCR (EFF) were assumed as two covariates in ANCOVA method. The assumptions for using of these two covariates were tested. A high positive correlation between the mean of the template intensity signal (TIS) through serial dilutions showed an appropriate efficiency of the competitive PCR assays. Results showed that the accuracy of data analyzing for relative quantification anaerobic fungi was considerable improved in ANCOVA model in comparison with ANOVA method and also the power of test is much greater. So, it seems that considering of the CIS and EFF as two co-variables was suitable.

Published in	Advances in Bioscience and Bioengineering (Volume 2, Issue 5)
DOI	10.11648/j.abb.20140205.11
Page(s)	44-50
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Analysis of Covariance (ANCOVA), Competitor Intensity Signal (CIS), Efficiency of PCR (EFF), Template Intensity Signal (TIS)

References

[1]	W.M. Freeman, S.J. Walker, and K.E. Vrana, Quantitative RT-PCR: pitfalls and potential. Biotechniques 1999. 26 p. 112-125.
[2]	F. Watzinger, E. Hörth, and T. Lion, Quantification of mRNA expression by competitive PCR using non-homologous competitors containing a shifted restriction site. Nucleic Acids Research, 2001. 29 p. 52.
[3]	A. Gaiger, et al., Increase of bcr- abl chimeric mRNA expression in tumor cells of patients with chronic myeloid leukemia precedes disease progression. Blood 1995. 86 p. 2371.
[4]	Joblin, K., Isolation, enumeration, and maintenance of rumen anaerobic fungi in roll tubes. Applied and environmental microbiology, 1981. 42(6): p. 1119-1122.
[5]	Vu, H.L., et al., A method for quantification of absolute amounts of nucleic acids by (RT)–PCR and a new mathematical model for data analysis. Nucleic acids research, 2000. 28(7): p. e18-e18.
[6]	Zar, J.H., Biostatistical Analysis. Second Edition ed1984: Prentice Hall International, Inc.
[7]	Reilly, K. and G. Attwood, Detection of Clostridium proteoclasticumand Closely Related Strains in the Rumen by Competitive PCR. Applied and environmental microbiology, 1998. 64(3): p. 907-913.
[8]	G.W, S. and W.G. Cochran, Statistical Methods. Eighth edition ed1991: Iowa state university press/AMES
[9]	Zentilin, L. and M. Giacca, Competitive PCR for precise nucleic acid quantification. Nature protocols, 2007. 2(9): p. 2092-2104.
[10]	Robinson, P., et al., Some experimental design and statistical criteria for analysis of studies in manuscripts submitted for consideration for publication. Animal feed science and technology, 2006. 129(1): p. 1-11.
[11]	Boom, R., et al., Rapid and simple method for purification of nucleic acids. Journal of clinical microbiology, 1990. 28(3): p. 495-503.
[12]	Sekhavati, M.H., et al., Development and use of quantitative competitive PCR assays for relative quantifying rumen anaerobic fungal populations in both in vitro and in vivo systems. Mycol Res, 2009. 113(Pt 10): p. 1146-53.
[13]	SAS, S. and S.U.s. Guide, Version 9.1. SAS Institute Inc., Cary, NC, 2003.
[14]	J. Dubcovsky, http://www.plantsciences.ucdavis.edu/agr205/schedule.htm . Introduction to Analysis of covariance.

Cite This Article

Plain Text BibTeX RIS

APA Style

Mohammad Hadi Sekhavati, Mahdi Elahi Torshizi, Mahyar Heydarpour, Adham Fani Maleki. (2015). Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations. Advances in Bioscience and Bioengineering, 2(5), 44-50. https://doi.org/10.11648/j.abb.20140205.11

Copy | Download

ACS Style

Mohammad Hadi Sekhavati; Mahdi Elahi Torshizi; Mahyar Heydarpour; Adham Fani Maleki. Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations. Adv. BioSci. Bioeng. 2015, 2(5), 44-50. doi: 10.11648/j.abb.20140205.11

Copy | Download

AMA Style

Mohammad Hadi Sekhavati, Mahdi Elahi Torshizi, Mahyar Heydarpour, Adham Fani Maleki. Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations. Adv BioSci Bioeng. 2015;2(5):44-50. doi: 10.11648/j.abb.20140205.11

Copy | Download

@article{10.11648/j.abb.20140205.11,
  author = {Mohammad Hadi Sekhavati and Mahdi Elahi Torshizi and Mahyar Heydarpour and Adham Fani Maleki},
  title = {Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations},
  journal = {Advances in Bioscience and Bioengineering},
  volume = {2},
  number = {5},
  pages = {44-50},
  doi = {10.11648/j.abb.20140205.11},
  url = {https://doi.org/10.11648/j.abb.20140205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.abb.20140205.11},
  abstract = {Quantitative competitive polymerase chain reaction (QC-PCR) technique is playing an important role in nucleic acid quantification. This paper describes a new statistical approach for data analyzing in relative quantitative competitive PCR assays. In order to test the accuracy of this statistical model for quantifying anaerobic rumen fungi, samples of rumen fluid were collected from six fistulated Holstein steers which were fed in two different diets groups (soybean meal diet and canola meal diet). Competitor intensity signal (CIS) and efficiency of PCR (EFF) were assumed as two covariates in ANCOVA method. The assumptions for using of these two covariates were tested. A high positive correlation between the mean of the template intensity signal (TIS) through serial dilutions showed an appropriate efficiency of the competitive PCR assays. Results showed that the accuracy of data analyzing for relative quantification anaerobic fungi was considerable improved in ANCOVA model in comparison with ANOVA method and also the power of test is much greater. So, it seems that considering of the CIS and EFF as two co-variables was suitable.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations
AU  - Mohammad Hadi Sekhavati
AU  - Mahdi Elahi Torshizi
AU  - Mahyar Heydarpour
AU  - Adham Fani Maleki
Y1  - 2015/01/04
PY  - 2015
N1  - https://doi.org/10.11648/j.abb.20140205.11
DO  - 10.11648/j.abb.20140205.11
T2  - Advances in Bioscience and Bioengineering
JF  - Advances in Bioscience and Bioengineering
JO  - Advances in Bioscience and Bioengineering
SP  - 44
EP  - 50
PB  - Science Publishing Group
SN  - 2330-4162
UR  - https://doi.org/10.11648/j.abb.20140205.11
AB  - Quantitative competitive polymerase chain reaction (QC-PCR) technique is playing an important role in nucleic acid quantification. This paper describes a new statistical approach for data analyzing in relative quantitative competitive PCR assays. In order to test the accuracy of this statistical model for quantifying anaerobic rumen fungi, samples of rumen fluid were collected from six fistulated Holstein steers which were fed in two different diets groups (soybean meal diet and canola meal diet). Competitor intensity signal (CIS) and efficiency of PCR (EFF) were assumed as two covariates in ANCOVA method. The assumptions for using of these two covariates were tested. A high positive correlation between the mean of the template intensity signal (TIS) through serial dilutions showed an appropriate efficiency of the competitive PCR assays. Results showed that the accuracy of data analyzing for relative quantification anaerobic fungi was considerable improved in ANCOVA model in comparison with ANOVA method and also the power of test is much greater. So, it seems that considering of the CIS and EFF as two co-variables was suitable.
VL  - 2
IS  - 5
ER  -

Copy | Download

Author Information

Mohammad Hadi Sekhavati

Department of Animal Science, Ferdowsi University of Mashhad, Iran
Mahdi Elahi Torshizi

Department of Animal Science, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Mahyar Heydarpour

CABG genomics group, Brigham & Women's Hospital, Harvard Medical School, USA
Adham Fani Maleki

Embryonic and Stem Cell Biology and Biotechnology Research Group, Institute of Biotechnology, Ferdowsi University of Mashhad, Iran

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Mohammad Hadi Sekhavati, Mahdi Elahi Torshizi, Mahyar Heydarpour, Adham Fani Maleki. (2015). Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations. Advances in Bioscience and Bioengineering, 2(5), 44-50. https://doi.org/10.11648/j.abb.20140205.11

Copy | Download

ACS Style

Mohammad Hadi Sekhavati; Mahdi Elahi Torshizi; Mahyar Heydarpour; Adham Fani Maleki. Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations. Adv. BioSci. Bioeng. 2015, 2(5), 44-50. doi: 10.11648/j.abb.20140205.11

Copy | Download

AMA Style

Mohammad Hadi Sekhavati, Mahdi Elahi Torshizi, Mahyar Heydarpour, Adham Fani Maleki. Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations. Adv BioSci Bioeng. 2015;2(5):44-50. doi: 10.11648/j.abb.20140205.11

Copy | Download

@article{10.11648/j.abb.20140205.11,
  author = {Mohammad Hadi Sekhavati and Mahdi Elahi Torshizi and Mahyar Heydarpour and Adham Fani Maleki},
  title = {Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations},
  journal = {Advances in Bioscience and Bioengineering},
  volume = {2},
  number = {5},
  pages = {44-50},
  doi = {10.11648/j.abb.20140205.11},
  url = {https://doi.org/10.11648/j.abb.20140205.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.abb.20140205.11},
  abstract = {Quantitative competitive polymerase chain reaction (QC-PCR) technique is playing an important role in nucleic acid quantification. This paper describes a new statistical approach for data analyzing in relative quantitative competitive PCR assays. In order to test the accuracy of this statistical model for quantifying anaerobic rumen fungi, samples of rumen fluid were collected from six fistulated Holstein steers which were fed in two different diets groups (soybean meal diet and canola meal diet). Competitor intensity signal (CIS) and efficiency of PCR (EFF) were assumed as two covariates in ANCOVA method. The assumptions for using of these two covariates were tested. A high positive correlation between the mean of the template intensity signal (TIS) through serial dilutions showed an appropriate efficiency of the competitive PCR assays. Results showed that the accuracy of data analyzing for relative quantification anaerobic fungi was considerable improved in ANCOVA model in comparison with ANOVA method and also the power of test is much greater. So, it seems that considering of the CIS and EFF as two co-variables was suitable.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Covariance Analysis, a New Approach for Relative Quantification Competitive PCR in Evaluation of Rumen Anaerobic Fungal Populations
AU  - Mohammad Hadi Sekhavati
AU  - Mahdi Elahi Torshizi
AU  - Mahyar Heydarpour
AU  - Adham Fani Maleki
Y1  - 2015/01/04
PY  - 2015
N1  - https://doi.org/10.11648/j.abb.20140205.11
DO  - 10.11648/j.abb.20140205.11
T2  - Advances in Bioscience and Bioengineering
JF  - Advances in Bioscience and Bioengineering
JO  - Advances in Bioscience and Bioengineering
SP  - 44
EP  - 50
PB  - Science Publishing Group
SN  - 2330-4162
UR  - https://doi.org/10.11648/j.abb.20140205.11
AB  - Quantitative competitive polymerase chain reaction (QC-PCR) technique is playing an important role in nucleic acid quantification. This paper describes a new statistical approach for data analyzing in relative quantitative competitive PCR assays. In order to test the accuracy of this statistical model for quantifying anaerobic rumen fungi, samples of rumen fluid were collected from six fistulated Holstein steers which were fed in two different diets groups (soybean meal diet and canola meal diet). Competitor intensity signal (CIS) and efficiency of PCR (EFF) were assumed as two covariates in ANCOVA method. The assumptions for using of these two covariates were tested. A high positive correlation between the mean of the template intensity signal (TIS) through serial dilutions showed an appropriate efficiency of the competitive PCR assays. Results showed that the accuracy of data analyzing for relative quantification anaerobic fungi was considerable improved in ANCOVA model in comparison with ANOVA method and also the power of test is much greater. So, it seems that considering of the CIS and EFF as two co-variables was suitable.
VL  - 2
IS  - 5
ER  -

Copy | Download